Background: This study aimed to explore the effect and mechanism of iguratimod (IGT) on M1 macrophage polarization and antibody-mediated rejection (ABMR) after renal transplant.Methods: Bioinformatics analysis was performed using three public databases derived from the GEO database. Sprague–Dawley (SD) rats were pre-sensitized with donors of Wistar rats in skin transplantation and a rat renal transplant ABMR model was established from the donors to skin pre-sensitized recipients. Subsequently, IGT was treated on the ABMR model. Routine staining and immunofluorescence (IF) staining were performed to observe the pathological changes in each group and flow cytometry was performed to detect the changes of DSA titers in peripheral blood. In addition, bone-marrow-derived macrophage (BMDM) was extracted and interfered with IGT to explore the effect of IGT in vivo. PCR, IF staining, and Western blot were used to detect the expression of related genes and proteins.Results: Bioinformatics analysis revealed that several immune cells were significantly infiltrated in the ABMR allograft, while M1 macrophage was noticed with the most significance. Results of IF staining and PCR proved the findings of the bioinformatics analysis. Based on this, IGT was observed to significantly attenuate the degree of peritubular capillary vasculitis and arteriolitis in the rat renal transplant ABMR model, whereas it decreases the expression of C4d and reduces the titer of DSA. Results in vitro suggested that M1 macrophage-related transcripts and proteins were significantly reduced by the treatment of IGT in a dose- and time-dependent manner. Furthermore, IGT intervention could remarkably decrease the expression of KLF4.Conclusion: Polarization of M1 macrophages may aggravate ABMR after renal transplant by promoting DSA-mediated endothelial cell injury, and IGT may attenuate the pathogenesis of ABMR by targeting KLF4. Allograft rejection after kidney transplantation is the main factor affecting the long-term survival of renal allograft . Accordide novo antibodies (DSA)  . As anties (DSA) . In the es (DSA) . In compes (DSA) . Moreovees (DSA) .Macrophages can be divided into M1 macrophages (promoting inflammatory response) and M2 macrophages (promoting cell proliferation and mediating tissue repair) . Under dThe Kruppel-like factor (KLF) family is a class of DNA-bound transcription regulatory factors of zinc-finger proteins, which play an important regulatory role in various physiological functions . It has Iguratimod (IGT) is a small molecule anti-inflammatory drug, which has been widely used in the treatment of rheumatoid arthritis because of its efficient and safe anti-cytokine-mediated inflammatory response . PreviouFrom what has been discussed previously, we hypothesized that IGT may inhibit the polarization of M1-type macrophages by upregulating the expression of KLF4, reduce the secretion of TNF-α cytokines, and consequently alleviate DSA-mediated allograft endothelial injury and the progression of ABMR after renal transplantation.The research protocol meets the ethical standards of the Declaration of Helsinki and Istanbul. The research protocol involving human kidney tissues was approved by the local ethics committee of the First Affiliated Hospital of Nanjing Medical University (2016-SR-029). Written informed consent was obtained from all transplant recipients and nephrectomy patients.https://www.ncbi.nlm.nih.gov/geo) and merged into a metadata cohort after removing the batch effect by the “SVA” package of R software. The “limma” package was used to identify differentially expressed genes. To quantify the relative proportions of infiltrating immune cells, a bioinformatics algorithm was used to calculate immune cell infiltrations by the CIBERSORT R script. The putative abundance of immune cells was estimated using a reference set with 22 types of immune cell subtypes (LM22) with 1,000 permutations  and Wistar rats were procured from Charles River Laboratories . Adult male Balb/c mice were procured from the Animal Core Facility of Nanjing Medical University. The animal center provided the animals with clean tap water and standard feedstuff. All animal handling procedures followed guidelines issued by the US National Institutes of Health and animal ethics established by Nanjing Medical University.We pre-sensitized recipient SD rats with donor Wistar rats by skin transplantation and constructed an ABMR model by abdominal ectopic kidney transplantation from donors to skin pre-sensitized recipients. Based on ABMR models, we gave a gavage of IGT (15 mg/kg) every morning and evening after surgery to explore the effect of IGT on ABMR. Detailed procedures are described in previous studies .in vitro, cells were treated for 24 h with IGT .Bone marrow suspension was obtained by rinsing the bone marrow cavity of the femur and tibia of Balb/c mice. A monocyte separator  was used to isolate monocytes from the bone marrow suspension. Isolated monocytes were cultured with DMEM  containing 10% FBS , 1% penicillin-streptomycin, and 30 ng/ml M-CSF . The new medium was replaced 3 days later. The cells matured 7 days after the extraction and could be used for downstream experiments. BMDMs were stimulated with 50 ng/ml LPS  and 10 ng/ml IFN-γ  to promote M1 polarization. To explore the effect of IGT 4) were inoculated into a 96-well plate. After incubation at 37 °C for 4 h, 10uL of CCK8  was added and cells were cultured for 0.5–4 h. It is recommended to use dual wavelengths to determine the absorbance, with a detection wavelength of 450–490 nm and a reference wavelength of 600–650 nm . cDNA was synthesized with a reverse transcription kit . Quantitative real-time PCR was performed with a SYBR Green PCR kit . The specific primers used were as follows: CD68: 5′‐TGTCTGATCTTGCTAGGACCG‐3′ (F),5′-GAGAGTAACGGCCTTTTTGTGA‐3′ (R); CD86: 5′‐TGTTTCCGTGGAGACGCAAG‐3′(F),5′-TTGAGCCTTTGTAAATGGGCA‐3′ (R); NOS2: 5′‐GTTCTCAGCCCAACAATACAAGA‐3′ (F),5′- GTGGACGGGTCGATGTCAC‐3′ (R); CD206: 5′‐CTCTGTTCAGCTATTGGACGC‐3′ (F),5′- CGGAATTTCTGGGATTCAGCTTC‐3′ (R); IL-1β: 5′‐TTCAGGCAGGCAGTATCACTC‐3′ (F),5′-GAAGGTCCACGGGAAAGACAC.‐3′ (R); IL-6: 5′‐TAGTCCTTCCTACCCCAATTTCC‐3′ (F),5′- TTGGTCCTTAGCCACTCCTTC‐3′ (R); KLF4: 5′‐GTGCCCCGACTAACCGTTG‐3′ (F),5′- GTCGTTGAACTCCTCGGTCT‐3′ (R); and GAPDH: 5′‐GAAGGTCGGTGTGAACGGAT‐3′ (F),5′-CCCATTTGATGTTAGCGGGAT‐3′ (R). The primers were bought from Tsingke Biotechnology .Kidney tissue sections were stained with HE and PAS for the histopathological examination. Immunofluorescence microscopy was performed as previously described, using antibodies against C4d , CD68 , iNOS , iNOS , F4/80 , and KLF4  . Images Flow cytometry was performed to identify macrophage polarization. We use fluorescein isothiocyanate (FITC)-conjugated anti-mouse iNOS  to identify M1 macrophages and phycoerythrin (PE)-conjugated anti-mouse F4/80  to identify M0 macrophages according to the instructions.We co-cultured the spleen lymphocytes of normal Wistar rats with the serum of the rats to be tested. The cells were incubated with PE-conjugated anti-rat IgG  after cleaning. The mean fluorescence intensity (MFI) of the cell suspension was measured by flow cytometry and the level of serum DSA was evaluated .p < 0.05 was considered statistically significant.All analyses were independently repeated at least three times. All data were expressed as the mean ± SD value determined by three independent experiments. One-way analysis of variance (ANOVA) was used for intergroup comparison. Multiple methods were compared using the Tukey test. The difference between the two groups was determined by a non-parametric test with regard to the limited sample size. The CIBERPORT algorithm was used to explore the immune infiltration in renal allograft specimens from three GEO databases . Significant renal injury and more C4d deposition in peritubular capillaries and glomeruli in the ABMR group when compared with the STA group were observed by histological studies and immunofluorescence microscopy . MoreoveWe established a rat renal transplant ABMR model by skin pre-sensitization. Immunofluorescence microscopy revealed that iNOS, the specific transcription factor of M1 macrophages, was highly expressed in the ABMR group compared with other groups . The expHE and PAS staining showed significant peritubular capillary inflammation and glomerulitis in the ABMR group. Compared with the ABMR group, the inflammation in ABMR + IGT group was significantly relieved . IGT canImmunofluorescence microscopy revealed remarkably high expression of iNOS in the ABMR group . After tWe used CCK8 to detect the toxicity of IGT and little effect has been noticed in the dose-dependent manner of IGT on cell proliferation . Under aPrevious studies have noticed that KLF4 may be involved in the polarization of M1 macrophages, which could be served as a critical target for the modulation of macrophage polarization . AllograIn the present study, we reported that IGT could attenuate ABMR after kidney transplantation via regulating M1 macrophage polarization induced by the deficiency of the transcription factor KLF4. The roles of IGT are not only to block M1 macrophage polarization but to also largely reduce the manufacture of proinflammatory cytokines such as TNF-α and IL6 which are closely related to vascular endothelial injury. In the scope of our knowledge, this is the first time that IGT has been applied to the field of transplantation.Macrophage polarization plays an important role in maintaining the homeostasis of the immune microenvironment. In the present study, we found that M1 macrophages infiltrated significantly in ABMR transplanted kidney tissues, both in rat models and humans. These experiments elucidate that the occurrence and development of ABMR are related to M1 macrophage infiltration and that the mechanism may be related to the secretion of pro-inflammatory cytokines and exosomes. In further experiments, we found that M1 secreted a large number of inflammatory factors during polarization. Previous studies have reported that TNF-α and other pro-inflammatory factors are closely related to endothelial injury. We can reasonably suspect that pro-inflammatory factors secreted by M1 cause endothelial cell damage through potential pathways, and then result in ABMR progression. Currently, studies on macrophage polarization in the field of transplantation mainly focus on ischemia-reperfusion injury, and research on rejection, especially ABMR, is still lacking . More imin vitro experiments indicate that KLF4 had low expression in M1 macrophages while IGT can reverse this trend. We speculated that KLF4 in macrophages could play a protective role by suppressing their M1 polarization. Sharma et al demonstrated that KLF4 reduced the secretion of pro-inflammatory cytokines in myeloid cells and thereby constrains atherogenesis (believed that KLF4 could inhibit M1 polarization via the intercept of co-activators required for NF-κB activation (Our ogenesis . This fitivation . These sIn recent years, our team has conducted a clinical study in the world for the first time in combination with traditional immunosuppression regimens in kidney transplant recipients. The results showed that the incidence of biopsy-proven acute rejection in the IGT group was significantly lower than that in the control group and no obvious toxic side effects were observed. However, its mechanism of immunosuppression is still unclear, which restricts its further clinical application. In this study, we found that IGT significantly slowed the occurrence of ABMR after surgery, and this process was associated with the targeted inhibition of M1-type macrophages. This result explained part of the mechanism of the effect of IGT, which is conducive to further promoting the clinical application of IGT after kidney transplantation.Obviously, there are still certain limitations in this study. The mechanism of KLF4 may be further explored. We still need to explore the specific mechanism of the interaction between macrophages and endothelial cells. Furthermore, the LM22 matrix used for the bioinformatics analysis in this study was to explore the immune cell infiltrations, which were originally designed for tumor biology. With regard to the difference in the microenvironment in various pathogenesis, the immune infiltration in renal organs, especially in the renal allograft, should be interpreted with caution.in vitro suggest that IGT may attenuate the progression of ABMR after kidney transplantation by inhibiting KLF4-regulated M1 macrophage polarization. Our study of IGT provided a novel insight into the further exploration of treatment for ABMR.In conclusion, studies on the rat ABMR model and


We combine novel laboratory experiments and exospheric modeling to reveal that “dynamic” Ly-α photolysis of Plutonian methane generates a photolytic refractory distribution on Charon that increases with latitude, consistent with poleward darkening observed in the New Horizons images. The flux ratio of the condensing methane to the interplanetary medium Ly-α photons, φ, controls the distribution and composition of Charon’s photoproducts. Mid-latitude regions are likely to host complex refractories emerging from low-φ photolysis, while high-φ photolysis at the polar zones primarily generate ethane. However, ethane being colorless does not contribute to the reddish polar hue. Solar wind radiolysis of Ly-α–cooked polar frost past spring sunrise may synthesize increasingly complex, redder refractories responsible for the unique albedo on this enigmatic moon. Exosphere model combined with dynamic Lyman-α photolysis experiments reveal the photoproduct distribution at Charon. The Multispectral Visible Imaging Camera (MVIC) images of Charon taken during the 2015 New Horizons flyby revealed its northern polar region to be marked with a distinct reddish hue , which iet al. ] by additional “fresh” methane.The exosphere model constrains the total methane feedstock available for photoconversion at all Charon surface locations. However, what processes control the refractory synthesis in the methane frost that is photolyzed during accrual? Charon’s dynamic photolysis conditions differ fundamentally from the previous experiments  inertia, methane coming off the spring polar zone is “promptly” (over ~4 years) refrozen into a small polar cap at the rapidly cooling autumnal polar zone. At high (40 J m−2 K−1 s−1/2) inertia for which the autumnal polar zone cools more slowly, larger polar caps extending to lower latitudes form more gradually over several decades.Cold locations on Charon therefore receive a continual supply of methane, but this cryo-trapping is concurrent with IPM Ly-α irradiation, as illustrated by the orange arrows in 4 cold-trapping rates at all Charon locations while also accounting for the ballistic redistribution of Plutonian methane  of the condensate  lamp. Although MWD lamps are generally a broadband source in the vacuum ultraviolet, their spectral output can be tuned to maximize the flux at Ly-α relative to longer-wavelength H2 molecular emissions. Our MWD lamp operational conditions are adopted from the study of Chen et al.  cooled to 10 K, the temperature at Charon’s winter pole  to 4 (solstice) are methane rich, containing photoproducts at extremely dilute concentrations ranging from 0.001  and 15% (post-equinox layer), depending on the depth in the frost layer and the instantaneous φ at the time of deposition.The mass fraction retained on the QCM at 50 K belongs to the heavier photoproducts complex than CHMethane conversion to refractories occurs most efficiently in the PAL, which is the ~35-nm photoabsorption depth below the top surface .FM, (ii) the CH4-to-refractory conversion rate in the PAL, and (iii) the burial rate of the photolyzed methane-refractory mixture beneath the PAL  is determined by (i) the methane arrival rate  the PAL . These p4 abundance is achieved in the PAL when methane depletion due to photodestruction and seepage to subsurface below the PAL is balanced by the methane condensation rate. Solutions to Eq. 1 gives methane and refractory fractions in the PAL in the limit of constant φM → R ρMdPAL)−1 and ρM is the density of solid methane. The inverse dependence of refractory concentration on φ is clear in A constant CHM → R is the only free parameter in M → R to 9 (± 3) × 10−22 m2 through the fit of RF versus φ data  compared to the high-φ cases. The pliability of the lattice cage is unchanged over the range of φ measured in our dynamic photolysis experiments. Our conversion cross section is larger than the 0.7 × 10−22 to 2 × 10−22 m2 photodestruction cross section  of complex hydrocarbons over the course of a single Charon winter. Methane spends less time on the surface at lower latitudes where the polar night is shorter, and therefore, photolysis has less time to produce complex hydrocarbons at these latitudes. Thus, the photoproduct abundance thins gradually with decreasing latitude dropping to nearly half the polar value at 45°. This latitudinally varying refractory distribution, consistent with Charon’s albedo gradient (−22 m2 versus 14 × 10−22 m2) combined with inefficient photoconversion of polar methane during rapid accretion (high φ) over a much smaller zone (north of 70° versus the expanded 45°) explains our reduced estimate.The distribution of photoproducts over Charon’s surface, produced over a Pluto orbit , falls of high φ , bottom.ion rate . At the gradient , is a noo latitude. These steps are the methane polar caps, “flash-frozen” over ultrashort ~1 Earth year surges of exospheric gas density near the autumnal equinoxes as frozen methane rapidly sublimes from the opposite (spring) polar zone  beneath the PAL in the high-φ films curb the production of the ethyl radicals needed to form increasingly complex refractories. Hence, C2H6 emerges as the dominant photoproduct at Charon. This is especially true close to the poles, where the methane polar caps, tens of micrometers thick, are formed in only ~3 Earth years, so short a time that photoproduct concentrations within these thick frost layers are as low as ~0.001%. Photoproduct concentration, composition, and complexity are a function of time, latitude, and depth within the surface frost, controlled by the seasonally varying φ.By comparison, the high-φ film only shows infrared absorptions corresponding to C−19 m2 , 1-keV solar wind impinging at a 1010 m−2 s−1 flux . The equation takes into account the surface solar insolation source and its variation according to Charon’s heliocentric distance, as well as the solar zenith angle versus local time, latitude, and solar declination angle. We consider as an approximation a uniform surface bolometric bond albedo of 0.3, such that 70% of the incident solar energy is absorbed by the surface. We assume a surface emissivity of unity, such that the surface radiates heat back to space at a rate given by σT4, with σ the Stephan-Boltzmann constant. The heat diffusion constant D = (I/C × ρ)2 = 3.9 × 10−11 m2/s for a thermal inertia = 10 J m−2 K−1 s−1/2, where C = 800 J kg−1 K−1 and ρ = 500 kg/m3 are the surface heat capacity and mass density, respectively  chamber cryo-pumped to a base pressure of ~10−2) of the thin methane films. Collimated deposition of methane in our experiments is different from “omnidirectional” freezing occurring on Charon, but differences in film microstructure  between deposition techniques , where R and R0 are reflectances of the photolyzed methane films and the Au substrate coating the QCM, respectively.The front face of the QCM has a specular gold coating that enables reflectance spectroscopy on thin ice films deposited onto the QCM. Infrared spectra of the dynamically photolyzed methane films shown in


Quantifying biochemical reaction rates within complex cellular processes remains a key challenge of systems biology even as high-throughput single-cell data have become available to characterize snapshots of population variability. That is because complex systems with stochastic and non-linear interactions are difficult to analyze when not all components can be observed simultaneously and systems cannot be followed over time. Instead of using descriptive statistical models, we show that incompletely specified mechanistic models can be used to translate qualitative knowledge of interactions into reaction rate functions from covariability data between pairs of components. This promises to turn a globally intractable problem into a sequence of solvable inference problems to quantify complex interaction networks from incomplete snapshots of their stochastic fluctuations. Statistical models are the dominant tool to interpret co-variability of molecular components in cellular processes because they can be formulated for a subset of components while leaving the full complexity of the processes unspecified. Their drawback lies in the difficulty of translating statistical associations into causal interactions. In contrast, complete mechanistic models of biochemical reaction networks are a powerful tool to describe physical interactions, but often necessitate making a large number of assumptions such that each individual assumption is only marginally tested in global model comparisons with data. We introduce a novel inference method that combines the power of both approaches by exploiting testable predictions for only partially specified mechanistic models. We present numerical proof-of-principle examples in which we reconstruct biochemical reaction rates from partial observations of variability within simulated biochemical reaction networks in the absence of cell division. In contrast to existing approaches, our algorithm does not require perturbations, temporal information, observing all components within a complex network, or complete model knowledge. Its key ingredients are partial knowledge of qualitative network interactions paired with high precision probability distributions to quantify stochastic fluctuations within biochemical reaction networks. Quantifying interactions between components within a complex network from snapshots of their activity is a challenge common to many areas of science. For example, understanding cellular processes requires quantifying biochemical reaction rates between molecules while typical high-throughput methods such as single-cell sequencing , 2, flowrate dependencies will lead to non-linear statistical relations between observed values of cellular components.Covariability of components within cellular processes is typically analyzed using statistical associations –13 becauone interaction at a time using incompletely specified mechanistic models, see Mechanistic models that quantify causal interactions between components would thus be preferable to describe biochemical reaction networks. However, constructing complete mechanistic models requires describing every interaction in a system. For complex cellular processes, often only some of the mechanistic details of molecular interactions will be known in detail, some aspects will have to be estimated from comparable systems reported in the literature, and some will have to be postulated because of a lack of direct experimental evidence. Here, we introduce a novel data analysis approach to deduce mechanistic rate dependencies We present numerical evidence for four distinct network topologies in which we successfully infer how one component affects the production rate of another from their observed joint probability distribution without without making any assumptions about the dynamics of the non-observed components. This proof-of-principle across four different network dynamics illustrates how qualitative knowledge of local network interactions can be translated into quantitative rate functions using only static snapshots of naturally occurring population variability.Describing the dynamics of some components within a complex interaction network in which the interactions between many components are unknown may seem impossible. However, we can trivially do so as long as we are content with describing one component’s dynamics in terms of components directly affecting it. The actual dynamics are fundamentally indeterminable for incomplete models but if components of interest are experimentally measurable their empirically observed covariability can be used to close the problem and constrain interaction rates as described below.x =  of abundances can be arbitrarily high-dimensional, the kth reaction changes levels of component Xi by dki, and the reaction rates rk(x) are arbitrarily non-linear functions of the state vector. This notation is motivated by biochemical reaction networks but many areas of science encounter stochastic systems whose dynamics are determined by the corresponding general chemical master equationP denotes the probability of the system to be in state x at some time t. Directly solving We follow the previously established approach  to charai that reaches a time-independent stationary state with probability distribution Pss(xi) must satisfyHowever, even when many details of a system are unknown, any given molecular component Xj for j ≠ i such that conditional rates cannot be predicted from incompletely specified models. Note, In this paper, we demonstrate that Next, we present results to show that stationary state probability distributions contain enough information to reconstruct an entirely unknown rate function in an otherwise unknown network of interactions. This is in contrast to an analysis of deterministic steady-states which only give one balance equation for each variable of interest and do not contain enough information to reconstruct a general rate function that is not parameterized by a single parameter.i-levels are known, i and all variables directly affecting those rates. Next, we show how this relation can be “inverted” to determine rate functions from observed probability distributions.When the rates of all reactions directly changing Xmmax for which Xi is observed and thus m = mmax due to the lack of sampling of the rarest states. Inverting the equation system rk(x) thus requires minimizing deviations from the predicted relations. In general, minimizing the sum of squared differences remains an underdetermined problem if we treat each value of the rate function as an independent unknown. However, under the assumption that biochemical rate functions are sufficiently smooth the problem can be solved by limiting the variability of the rate functions across neighbouring states  and X3 molecules are degraded independently leading to the following class of reaction systemsτ3 denotes the average life-time of component X3 and τ3 = 1 and λ = 80, n = 2, K = 40.The conserved part of our test systems that we want to reconstruct from simulation data corresponds to a stereotypical biochemical reaction rate in cells. In particular, we specified that the production of component X1, X2 affect each other and how they are affected by X3 were chosen to achieve diverse system dynamics and are specified in the Materials & Methods. Regardless of the X1,X2-dynamics, the probability balance equation The reaction dynamics of the other variables, i.e., how X3 as discussed in a later section.Although f(x2) and thus infer how the X3 production rate depends on X2 from the observed P. To do so we generated exact realizations of the above stochastic processes using the standard Doob-Gillespie algorithm [2, X3 from the numerically observed stationary distribution to generate an observed joint probability from N = 100, 000 independent samples. Any information about the dynamics of X1 was discarded because our method does not utilize any information beyond the pairs of components under consideration. The numerical algorithm is straightforward and detailed in the in the Materials & Methods. In short, a convex optimization algorithm can identify the f(x2) that minimizes P. Doing so requires finding a large-dimensional but finite solution vector with elements fn ≔ f(x2 = n) over the observed states that solves a regular least-squares problem as defined in 3 in all systems as illustrated by the orange crosses in f(x2) from pairwise observation of X2, X3 in the absence of any temporal information and independent of any information about the vastly different global system dynamics.As detailed in the Materials & Methods, we employed a numerical algorithm to approximately solve lgorithm , 27. We f(x2) beyond its smoothness and non-negativity. Alternatively, one could, e.g., assume that f(x2) is a Hill-function and identify its characterizing parameters K, n, λ through minimizing violations of f(x2) is a Hill-function, one can always fit a Hill-function through the f(x2) obtained from our algorithm and identify K, n, λ that way.Note, in these successful proof-of-principle examples we deliberately made no assumption about f(x2) = const. above and below the largest and smallest state, requires additional a priori information. Because such information cannot be deduced from the experimental observations we refrain from doing so throughout the manuscript.To fully specify a rate function rather than a finite table of values, e.g., through fitting a Hill function or simply continuing 2 varies very slowly f(x2) can be directly read off from the conditional averages 〈f(x2)|x3〉 as discussed in the next section. The above algorithm solves the problem of determining f(x2) when the timescales of X2 and X3 are not separable.Also note, that when Xf(x2), could prove useful , but here we want to illustrate the core of the inference quality based solely on the convex optimization of E to quantify the quality of our inference by weighting errors in the rate function by the probability of the system to have been observed in that state, relative to the overall average of the rate function 〈ftrue〉:Information cannot be created from nothing and the above inference cannot determine rates for states that were never observed. In practice, making additional assumptions to fill in gaps, such as monotonicity or the functional form of 2 and X3 affect this inference error E we consider the above “noise enhancing” system (f(x2) is straightforward when the variability of X2 is slow such that X3 has enough time to adjust to X2-levels and the conditional average 〈x3|x2〉 directly identifies the production rate of X3 (SI). For faster upstream fluctuations the effect of X2-variability on X3 decreases and the inference of the production rate becomes more challenging. However, compared to the naive statistical approach of interpreting conditional averages as rates, our inference algorithm based on 2-fluctuations is fast relative to X3, see 3 our inferred rate function deviates significantly from the true one when inferred from a joint probability distribution constructed from N = 100, 000 samples. However, even in this unfavourable regime with a 40-fold separation of time-scale between the upstream and downstream variable such that the conditional average 〈x3|x2〉 levels-off, N = 5 × 106 were enough sample observations to correctly infer f(x2), see To illustrate how the relative time-scale of X” system  for whicτ3 is structurally unidentifiable by our approach. However, if it is unknown, our approach will still determine τ3 ⋅ f(x2), i.e., we can identify the shape of the rate function but not its scale.Note, that the parameter 3 was conserved across all systems. Next, we demonstrate that our inference method works for arbitrary Hill-type functions for the production rate. We simulated the noise enhancing three-component system .Here, 3-molecules were degraded in a first-order reaction as is commonly the case for cellular components [3 undergoes non-linear degradation reactions.In all of the above systems, Xmponents , 30 and mponents . Next, wf(x2) in a class of noise enhancing  behaves for individual states. As intuitively expected, we find that the inference error initially decreases N increases. However, for large N a plateau becomes apparent that is most severe for the most rarely observed states, Additionally, we analyzed how the inference quality of 3 degrades as a dimer, such that its specified degradation rate in Note, in these systems the component of interest XDue to finite sampling and unavoidable measurement noise, empirically observed probability distributions will not perfectly reproduce the stationary distributions of the underlying chemical reaction network. Next, we analyze how measurement noise affects our inference method by explicitly accounting for small absolute and relative error terms as well as systemic undercounting of molecules.P while adding a two-dimensional normally distributed error with zero mean and a standard deviation of σabs = 1, 3, 8 molecules respectively (SI). For small absolute errors, we find that the inference method still succeeds to satisfactorily determine the original rate function, see To simulate “empirically observed” probability distributions of the above noise enhancing system  we resamp to detect a specific molecule, and applying our inference method to the re-scaled probability distribution, see Furthermore, we analyzed the effect of relative measurement noise by multiplying each sampled data point with a two-dimensional normally distributed error term to simulate a relative error of 1%, 5%, 20% in the observed variables respectively (SI). In its current form, our inference is significantly affected by large multiplicative noise because it causes the “measured” probability distribution to differ significantly from the underlying stationary distribution of the stochastic process, see n → 0 or K ≪ 〈x2〉, see Our presented method relies on knowing that one component directly affects the production rate of another. We thus obtained unsatisfactory inferences when the upstream variable barely affects the downstream variable, as illustrated in the regime when f(x2) is approximately constant across the observed stochastic fluctuations of X2. Following a standard cross-validation approach, we can use one half of the observed data as a “training set” [f(x2) using our usual unconstrained method but additionally perform a constrained optimization for constant production rates, i.e., we find the best f(x2) = λ for some λ > 0. This by itself will not pick a constant production rate over the freely optimized f(x2) because the latter has many more degrees of freedom. However, because the free optimization overfits sampling errors when minimizing deviations of f(x2) is approximately constant, it will do relatively worse than a constant production rate when applied to the “validation set” of the data. Incorporating this cross-validation approach into our inference methods as detailed in the Materials & Methods, removes the most unsatisfactory regime while leaving the successful inferences unaffected as illustrated in f(x2) illustrated in Breakdown of satisfactory inference in that regime can be prevented by explicitly considering the possibility that ing set” . Using tIdentifying constant production rates is a special case of the general problem of identifying which component affects which in complex biochemical reaction networks . Future f = f(x2). Whether our algorithm can also successfully identify more complex rate functions such as f = f will require future work.In principle, the basic idea of matching probability fluxes can be applied to components with more than one birth and death reaction as illustrated by the complete generality of Importantly, the value of our approach lies not necessarily in identifying arbitrarily complex rate functions but in identifying simple rate functions in arbitrarily complex systems. The benefit of our approach lies in the fact that it effectively “eliminates” network complexity when reconstructing simple rate laws for local interactions between variables. Note, many complex biological control systems indeed involve many simple interactions which in turn lead to complex systems through feedback loops and an overall large number of interacting components .As presented, our method requires highly accurate and discrete probability distributions which is motivated by measuring small numbers of molecules in cells where such discreteness is fundamentally built into the problem and single-molecule accuracy is possible –40.This type of data is at the forefront of what is technologically possible , 42 and As is, our method cannot be directly applied to experimental data from growing and dividing cells. Future work is required to demonstrate that our approach could potentially be exploited to infer production rates of components from single-cell data.In the simplest case, biochemical reactions in growing and dividing cells can be modelled as cyclo-stationary processes of periodically changing probability distributions . In prinAdditionally, in growing populations time-averaged ensemble averages do not agree with population averages because the former corresponds to a uniform cell-age distribution whereas in a growing population we must have twice as many newborn cells as dividing cells which leads to a non-uniform age-structure . As presFormulating stochastic models in terms of concentrations rather than abundances might partially resolve these issues because on average concentrations are the same at the beginning and the end of the cell-cycle unlike abundances which must double on average.quantitatively understanding each subproblem through painstaking experimental measurements [Early aeronautical engineering faced a challenge analogous to that of current synthetic biology. While the qualitative requirements for motored flight were well known, designing a reliable flyer required a breakthrough in urements .Similarly, designing reliable synthetic circuits in biology requires a quantitative description of biochemical reaction dynamics rather than qualitative network interaction models. Here, we present a method that promises to iteratively turn a qualitative model of biochemical interactions into a network of quantitative reaction rates. Given one arrow within a network of interacting components, our method can identify the functional dependence of the actual reaction rate, one interaction at a time, from fluctuations of a subset of components without having to perturb the system.Existing mechanistic modelling approaches often rely on temporal data –59 and gWhile approaches that combine mechanistic models with Bayesian inference –73 have Our results show that we can reliably identify reaction rates independent of the larger network structures, in contrast to previous approaches in which small models of gene expression were inverted under the assumption they were correct as a whole and only a handful of parameter values needed to be determined , 74, 75.The presented numerical proof-of-principle results establish that our presented algorithm works across a broad range of tested systems and that the number of required observations for algorithmic success is comparable to those accessible by modern experimental single-cell techniques in biology such as flow-cytometry that routinely measure millions of isogenic cells at a time.As presented, our algorithm requires highly accurate input distributions, and we cannot exclude the possibility that our method works only in theory but not in practice. However, here we presented only the most basic inference algorithm to extract information from the probability flux balance relations. We thereby established that the information contained in Gf = h, where Gij = P, fi = f(x2 = i) and hi = (i + 1)P(x3 = i + 1)/τ3. To avoid overfitting the solution f to sampling noise we add a regularization term that effectively penalizes the discrete “second derivatives” fi+2 − 2fi+1 + fi. The effect of this regularization is to smoothen the resulting reaction rate function. Motivated by complex cellular processes where f(x2) represent biochemical reaction rates, we furthermore constrain the solution vector f to be non-negative. We thus solve the following optimization problemij = δi,j − 2δi,j+1 + δi,j+2 is the regularization matrix and ϵ corresponds to the strength of the smoothening. The strength of this regularization parameter affects the quality of the inference. We found N increases. This regularization was used throughout the paper. In other applications, alternative heuristics to choose ϵ may prove useful to achieve satisfactory results (SI). Note, that if the lifetime τ3 is unknown, the inference method will still correctly identify τ3 ⋅ f(x2) meaning that we get the correct shape of the reaction rate function up to an unknown scale-factor.To utilize probability distributions that contain sampling errors we write the balance https://github.com/t-wittenstein/quantify-biochemical-rates. While it is straightforward to add further constraints, such as monotonicity, about the solution function f(x2) we here deliberately only present results without making any additional assumptions beyond f(x2) and, say, fit a Hill-function directly by minimizing K, n, λ which is in general less robust than the linear optimization problem we solve above.We solved In order to avoid overfitting systems when production rates are approximately constant we compared our inferred production rate against a constant one as follows: We divided the sampling data into two equally sized sets, and applied our inference method first to the “training” set and then checked against a constant production rate using the second “validation” set . To comp1,X2,X2 took the following formWe considered simple three-component systems in which the production and degradation rates of X3 was kept the same as specified in the main text while the other components were subject to the following dynamics.In particular, we simulated the following example systems depicted in n = 6, K = 37, c = 15 and life-times τ1 = τ2 = 1.Bistable system: 1 = 50000, n1 = 10, K1 = 0.1, λ2 = 80, n2 = 1, K2 = 100 and life-times τ1 = τ2 = 1.Oscillating system: f1(x3) = λ1x3, 1 = 50, λ2 = 3000, n2 = 10, K2 = 10 and life-times τ1 = 50, τ2 = 1.Noise controlling system: f1 = 5, n = 4, K = 50, c = 8 and life-times τ1 = τ2 = 1.Noise enhancing system: γx3(x3 − 1) were simulated with γ = 2.Data in Figs S1 TextDetailed description of simulation process, models, and supplementary data.(PDF)Click here for additional data file.


The authors regret multiple errors in the original article. The corrections are listed below.Journal of Agricultural and Food Chemistry paper, ‘Quantitation of Aristolochic Acids in Corn, Wheat Grain, and Soil Samples Collected in Serbia: Identifying a Novel Exposure Pathway in the Etiology of Balkan Endemic Nephropathy’ (DOI: 10.1021/acs.jafc.6b02203).Firstly, the authors have amended their Graphical Abstract in order to further differentiate it from the cover image associated with Yinan Wang’s y-axes labels.The authors regret that y-axes labels.The corrected version of The authors wish to remove Table 1 from the original article as this table contains instrumental parameters which do not correspond to the experimental conditions used in this work and was included in error.The statement on line 8 of Section 2.3 on page 25320 ‘and 13–18 min, 10% .’ should be replaced with ‘and 13–15 min, 10% .’m/z values for MRM transitions for the target analytes and internal standard are listed in Table 1, where the residence time for each transition is set to 100 ms.’ should be replaced with ‘The MS/MS spectra of AA I and AA II were obtained using Agilent 1200RRLC-6520 Accurate-Mass Q-TOF LC/MS.’The sentence starting on line 12 of Section 2.3 on page 25320 ‘The −1) on the same day (n = 5) and over separate days in a month (n = 5).’ should be replaced with ‘Method precision was evaluated by analyzing herbal extracts spiked with AAs at two different concentrations (50 and 500 ng g−1) on the same day (n = 5) and over separate days in two weeks (n = 5).’The sentence starting on line 5 of Section 2.5 on page 25321 ‘Method precision was evaluated by analyzing herbal extracts spiked with AAs at two different concentrations  on the same day (n = 5) and over seven separate days for two weeks.’ should be replaced by ‘Meanwhile, method precision was investigated by analysing blank samples spiked with AAs at two different concentrations (50 and 500 ng g−1) on the same day (n = 5) and over five separate days for two weeks.’The sentence starting on line 19 of column 2 on page 25321 ‘Meanwhile, method precision was investigated by analysing blank samples spiked with AAs at two different concentrations (50 and 500 ng gThe Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.


Women in Ethiopia prefer sedentary behavior and are physically inactive during pregnancy; this increases the risks of pregnancy-related complications. Therefore, this study aimed to assess physical exercise and its associated factors among pregnant women attending Antenatal Care at Debermarkose Referral Hospital, Northwest Ethiopia: using the theory of planned behavior.p < 0.05 was used as a cutoff to determine statistical significance at multivariable logistics regressions.An institution-based cross-sectional study was conducted from 23rd February to 10th June, 2020. A systematic random sampling technique was used to select 333 study participants. Data were collected with face-to-face interviews using a pretested structured questionnaire. Epi-info and STATA version 14.0 were used for data entry and analysis respectively. Multivariable linear regression analysis was done to identify the association between dependent and independent factors, and R2) in intention accounted for the factors was 0.79. The magnitude of intention to do physical exercise during the current pregnancy was 3.8 ± 2. primary level of education , 2nd trimester gestational age , 3rd trimester gestational age , Attitude , subjective norm  and perceived behavioral control  were significantly associated with pregnant womens’ intention to do physical exercise during pregnancy period.A total of 333 pregnant women were participated in this study with response rate of 98%. The proportion of variance (This study provided that the theory of planned behavior could be successfully applied to identify the factors related to the intention to do physical exercise during pregnancy The program designers who work on pregnancy better consider providing Information education and communication to change the attitude, work on influencing significant individuals and barriers and enabling factors. Physical exercise during pregnancy is associated with short and long-term benefits for the mother and fetus , 2. HealEthiopia is one of the Sub-Saharan Africa countries that has high maternal mortality ratio (MMR) reported at 412 . Even thAccording to the theory of planned behavior three kinds of considerations: beliefs about the likely consequences of the behavior , beliefs about the normative expectations of others (normative beliefs), and beliefs about the presence of factors that may facilitate or impede performance of the behavior (control beliefs) guide human behavior . In combAn institution-based cross-sectional study was conducted from 23rd February to 10th June 2020 among Debermarkose referral hospital of antenatal care attendant pregnant women. It is situated in the Amhara National Regional state at 376 km away from Addis Ababa city in North West direction of the country.2 = 95%confidence level, δ = standard deviation (5), d = degree of accuracy desired to set (0.04) and, mean intention (13.64). By adding a 5% non-response rate the final sample size was 339 participants. Those participants with placenta Previa, premature rupture of membrane, and pregnancy-induced hypertension were excluded.As far as the knowledge of the investigators there was no study conducted in Ethiopia on intention to do physical activity among pregnant women. Therefore, a pilot study was conducted to get the standard deviation and mean intention. The sample size was determined using the single population mean and double population mean formula as below;To select a sample of 339 antenatal care attendants from the total 819 attendants , we assigned a consecutive number from 1 to N in the ANC registration book. Then sampling started by selecting an element from the list at random and then every kth element in the frame was selected, where k was the sampling interval.Intention Intention to do physical exercise is the outcome variable, it is the mother’s readiness to do physical exercise during the current pregnancy. It was measured with four items having a five-point Likert scale (Cronbach’s α = 0.96). The questions were asked like “I intend to do physical exercise during the pregnancy period.” The responses were 1 “strongly disagree”, 2 “disagree”, 3 “neutral”, 4 “agree”, and 5 “strongly agree”. The composite score ranged from four to twenty and the high score indicate high intention [ntention .Regular physical exercise is an aerobic physical exercise that lasts 30 min continuously at least 3 days a week like brisk walking, stationary cycling, and swimming [swimming .Direct attitude Individual feelings and beliefs about physical exercise. It was measured with seven items having a five- Likert scale (Cronbach’s α = 0.89). The questions were asked, “For me exercising regularly for 30 min per day at least 3 days a week during my pregnancy will be…” The response was 1 “bad” …0.5 “good”. The composite score ranges from seven to thirty five the high score indicates a high attitude towards physical exercise [exercise .Direct subjective norm Perception of individuals of the social pressure to do physical exercise. It was measured with five items having five-point Likert scale (Cronbach’s α = 0.95). Subjective norm questions were asked, “Most people who are important to me think that I should exercise regularly during my pregnancy period”. The response was 1 “strongly disagree”, 2 “disagree”, 3 “neutral”, 4 “agree” and 5 “strongly agree”. The composite score ranges from five to twenty five the higher score indicates high social influence towards physical exercise [exercise .Perceived behavioral control The perceived ability of an individual to control factors that influenced physical exercise. It was measured with five items having a five-point likert scale (Cronbach’s α = 0.91). The questions were asked, “For me doing regular physical exercise during my pregnancy is…” The responses were 1 “very difficult” …0.5 “easy”. The composite score ranges from five to twenty five and the higher score indicates higher perceived ability of individuals to control factors [ factors , 25.Previous experience Individuals who have done exercise during the antenatal period at least once in life time. And the questions were asked like “have you ever done physical exercise during pregnancy period?”.Indirect Subjective norm It is composed of normative belief i.e. one’s belief about perceived social pressure to do physical exercise and motivation to comply. The normative questions were asked as “my husband thinks that I should do physical exercise during pregnancy.” The responses were (1) “strongly disagree”, (2) “disagree”, (3) “neutral”, (4) “agree”, (5) “strongly agree”. And the motivations to comply questions were asked like “how much do you care about what your husband think you should do physical exercise during pregnancy?” The responses were (1) “not at all”, (2) “not”, (3) “neutral”, (4) “somewhat” and (5) “very much”. It was assessed by a total twelve items for normative belief measuring and six motivations to comply with measuring items. The final value of the variable was produced by computing the sum product of normative belief and motivation to comply [o comply . The intIndirect Perceived behavioral control It is composed of control belief i.e. one’s perception to do physical exercise and perceived power. The questions of control belief were asked like “how the access of the facilities may affect your physical exercise during pregnancy. The responses were (1) “not at all”, (2) “not”, (3) “neutral”, (4) “somewhat” and (5) “very much”. And questions of perceived power were asked like “if I feel that I don’t have access of the facilities, it would be more difficult to do physical exercise. The questions were asked like “I intend to do physical exercise during the pregnancy period.” The responses were (1) “strongly disagree”, (2) “disagree”, (3) “neutral”, (4) “agree”, (5) “strongly agree”. It was assessed by a total of ten items for perceived control and five for perceived power measuring items. The final value of the variable was produced by computing the sum product of perceived control and perceived power [ed power . The intKnowledge knowledge about physical exercise during pregnancy was asked with eleven items. The items were developed from works of various literatures [eratures –31 and hThe data were collected using a structured, pretested and interview administered questionnaire prepared by the investigators after reviewing different literatures , 32, 33.2 was used for the ability of explanatory variables to explain dependent variables. First all independent variables were entered in simple linear regression and those variables whose p value less than 0.2 were included in multiple linear regressions. Unstandardized β coefficient was used to interpret the effect of factors associated to intention to do physical exercise. Variables with p value less than 0.05 at 95%confidence interval were considered as statistically significant.Prior to the use of the instrument the reliability analysis or internal consistency test for the TPB constructs carried out and the result was above 0.7 which is acceptable ; it shows how closely related a set of items are as a group. All collected data was coded, cleaned and entered in to epi-data version 4.6 and was transferred to SPSS version 20 statistical software for its analysis. Descriptive analysis was used to see frequency distribution, mean and standard deviation. Correlation analysis was done between indirect and direct TPB variables in order to see the relation between them. Linear regression analysis was fitted to test the association between dependent variable and independent variables. The assumptions of normality of dependent variable which is intention statistically using skewness and kurtosis test i.e., Z value of skewness and it was found to be between − 1.96 and + 1.96 so that it was normally distributed. Linearity assumption was checked using scatter plot of the standardized residuals versus the predicted values from the regression analysis. Multicollinearity assumptions were tested by variance inflation factor (VIF) and the value of all variables were below ten. The assumption of outlier was tested using box plot and there was no outlier. RA total of 333 participants were engaged with a response rate of 98.2%. Regarding to the religion, most (73.3%) were orthodox Christian followers. About one-fourth (24%) of the participants had no formal education, and the majority (90.7%) were resided in urban while 60.7% were house wife in occupation. Almost all 99.1%) of the women were married  of participants did brisk walking and the remaining engaged in jogging. Nearly half (47.6%) reported they engage for 15 min and while most (81%) them engaged in physical activity irregularly per week while about 14.3% did twice a week.The mean knowledge score of the respondents was 5.28 (SD ± 2.38). The majority (81%) of the participants knew that aerobic exercise like brisk walking during pregnancy prevent excessive weight gain and 90% reduce risk of high blood pressure, and more than half (54%) knew that it is not recommended for a woman with vaginal bleeding (Table The majority of the participants 144 (43.2%) were multigravida, 101 (30.3%) were multiparous and 88 (26.4%) were primigravida. Most of the respondents were belongs with in third trimester pregnancy 114 (34.2%).The median score of direct attitude, subjective norm and perceived behavioral control was 3.57, 4 and 3.6 respectively. Indirect measures of theory of planned behavior were assessed and the median score of indirect attitude was 104 ± 83. The median intention was 3.8 ± 2.p value of < 0.05. Indirect attitude had highest correlation followed by direct perceived behavioural control. Indirect perceived behavioural control had negative correlation with intention  was the strongest factor of intention to do regular physical exercise followed by direct attitude (β = 0.27) and direct subjective norm (β = 0.11).The participant level of education was significantly and positively associated with intention to do regular physical exercise. Participants who attained primary level education (1–8) were 0.43 (B = 0.43:95% CI 0.25–0.61) times more likely to increase intention than those with no formal education provided that other variables are constant. Moreover, the intention of mothers in second and third trimester increased by 0.19  and 0.17  than those mothers in first trimester.For a score increase of direct attitude, intention to do regular physical exercise increased by 0.34 . A positive score increase in direct perceived behavioral control could result in increase of intention to do physical activity by 0.45 . For a positive unit increase in a subjective norm could result increase of intention to do regular physical exercise by 0.12  (Table This study assessed intention to do physical exercise and associated factors among pregnant women attending ANC at Debermarkose referral hospital using the theory of planned behavior. The participants’ level of education, gestational age and theory of planned behavior variables such as perceived behavioral control, attitude and subjective norm were associated variables of intention to do physical exercise among pregnant women. The current study revealed that the mean value of intention to do regular physical exercise was 3.8. It suggested that the average intention to do physical exercise was almost agreeing. This finding is supported by other study .This study showed that participants’ level of education is an important associated factor of intention to do exercise regularly. This finding is consistent with studies conducted in the Tigray region of Ethiopia and India . It mighOur study found that gestational age is a significant associated factor of intention to do physical exercise. Participants in second and third trimesters had increased intention than those in first trimester. It is supported by a study conducted in Brazil . Most prThis study revealed that perceived behavioral control is an associated factor of intention to do regular physical exercise. This is supported with a study done in Taiwan . This isAttitude was a positively associated with intention to do regular physical exercise. The finding is consistent with previous studies conducted on intention to do physical activity , 42. ThiThe current study found that subjective norm was a positively associated with intention to do regular physical exercise. This is consistent with a research conducted in Taiwan . This imSince the data was from self-report there could be the probability of the occurrence of recall and social desirability bias. Furthermore, due to the cross-sectional nature of the study, it doesn’t show cause and effect relationship. Recent studies showed that personality trait has an effect on intention to do physical exercise so future researchers better to incorporate personality trait as an explanatory variable.Theory of planned behavior could be successfully applied to determine the associated factors of intention to do physical exercise during pregnancy. Factors of intention to do regular physical exercise were direct perceived behavioral control, direct attitude, direct subjective norm, participants level of education, and gestational age. Therefore, information education and communication is required to bring favorable attitude, to reduce perceived berries and interventions should also consider the mother, friend, spouse and health professionals who had influence to her decision.


T) when pressure changes instantaneously. Their thermal insulation performance was evaluated by analyzing the influence of pressure gradients on the unsteady-state heat transfer. When the cold surface’s temperature of the specimen keeps constant at 15 °C and ΔT = 171~912 K, the results demonstrate that the correlative thermal conductivities of silica aerogel with 1% and 5.84% SiC are 0.02223~0.04077 W·m−1·K−1 at P ≈ 10 Pa and 0.03165~0.04665 W·m−1·K−1 at P = 1 atm, respectively. The aerogel composite with 0% SiC showed the best thermal insulation performance at ΔT < 200 K and P ≈ 10 Pa, while the aerogel with 5.84% SiC became the best at ΔT > 700 K and P = 1 atm. In addition, the transient pressure decreases will significantly impair the heat transfer of the gas inside the aerogel, thereby weakening the gaseous thermal conductivity and improving the thermal insulation performance.Silica aerogel composite is an excellent thermal insulator for spacecraft under high-temperature and complex air environments. This study intends to evaluate SiC-doped silica aerogel’s thermal insulation performance under large temperature and air pressure differences. In this paper, the hot surface’s temperature response of SiC-doped silica aerogel with different content was studied at significant temperature differences (Δ Silica aerogel is a typical nano-porous material  with theSilica aerogel’s excellent thermal insulation performance has attracted extraordinary attention in aerospace. It was first applied to the Mars Sojourner rover as an insulator to protect the primary battery from extremely low temperatures . NASA haT = 296 K in air, which decreased sharply to 0 when P < 0.01 bar. Spagnol [T = 300~315 K under variable atmospheric pressure, which accounted for over 60% of its total thermal conductivity. Zhang’s experimental results [Most current methods for measuring the thermal conductivity of aerogels are carried out in a static air pressure environment. Zeng  tested s Spagnol  measured results  proved t results ,21,22. T results ,24 evalu results  proposedT = 0–912 K and P = 0–1 atm; their thermal conductivities are further calculated. Then, their thermal insulation performance prediction model is proposed to describe unsteady-state heat transfer under the experiment’s same pressure and temperature conditions. Finally, the influence of pressure difference (ΔP) and porosity on silica aerogel’s thermal insulation is excavated.Hence, it is necessary to study silica aerogel’s thermal insulation performance by the temperature response at pressure changes instantaneously and under large temperature difference conditions. In this paper, the hot surface’s temperature response of silica aerogel composites with different SiC components is measured under ΔAn experimental system for testing the dynamic hot surface’s temperature response of monolithic silica aerogel is established, as shown in The hot surface’s heat flux of the specimen keeps constant, and the temperature of the cold surface, initial moment, and environment stay constant at 288.15 K. The air pressure can be adjusted from 10 Pa to 1 bar, and the temperature difference is from 0 to 912 K. The air pressure will be changed instantaneously when the heat transfer reaches the first steady state. The heat transfer covers the initial steady state, first unsteady state when heated, first steady state, second unsteady state when pressure changes instantaneously, and second steady state in the experimental test process.The components and parameters of silica aerogel doping with 3.5 μm SiC are listed in (1)Dehydrate two identical samples in a drying oven to remove water vapor.(2)Put the specimens sandwiched with the heater, thermocouples, and cooling units and place the main test section into the sealed chamber, as shown in (3)Set the initial and ambient temperature identical and run the system.(4)Turn on the D.C. power supply when the specimen’s heat transfer reaches the initial steady state; change the atmospheric pressure through the pressure transient valve at the first steady state.(5)Record the experimental data of the whole process by the thermocouples. Shut down the test system after the second steady state.The experimental procedures are introduced as follows:The heat transfer process inside the silica aerogel usually consists of three parts ,27, as sP is pressure; T represents temperature; Ss = (324.3/ρ + 5.03) × 105 denotes specific surface area [ρ stands for the density of the specimen in φ means porosity in The gaseous thermal conductivity model of silica aerogel can be described as follows ,27:(1)λgace area ; ρ standThe solid thermal conductivity of bulk silicon dioxide is written as :(2)λbulkr* = r/l means the nondimensional radius of particles; l = 0.588 nm [r = 3 nm [Silica aerogel’s solid thermal conductivity can be expressed as :(3)λs = λg + λs is the pure silica aerogel’s effective thermal conductivity contributed by heat conduction; λf is the thermal conductivity of the SiC [fv is the volume fraction of the additive.SiC is randomly distributed in the silica aerogel. Thus, SiC-doped silica aerogel’s effective thermal conductivity, ll model :(4)λeλr is the thermal conductivity of the aerogel in Equation (6).The initial and boundary conditions can be expressed as:The energy and pressure equations above are coupled and solved discretely using the finite volume method (FVM) . The steUI = 10~68 W, respectively, and atmospheric pressure changes abruptly from P ≈ 10 Pa to P = 1 atm. Qualitatively, the higher the hot surface’s temperature is, and the faster Thot rises, the better the thermal insulation performance has.Thot responds the fastest, meaning aerogel with no SiC additive has the best insulation performance at UI = 10 W. However, the Thot of specimen P-1 becomes lower than specimens O−1 and O−4 in Thot of specimens O−1 and O−4 both at P ≈ 10 Pa and P = 1 atm, the deviation of Thot between specimens O−1 and O−4 becomes more significant as heating power increases, which means specimen O−4′s insulation performance becomes better than that of specimen O−1. SiC particles, which are added to silica aerogel, increase the contribution of the solid heat conduction and decrease the thermal radiation’s contribution. The heat conduction is prominent when the temperature difference is relatively small, while the thermal radiation becomes dominant at high temperatures.In T between the Thot at the first and second steady state is more significant than that of specimen O−1 before and after the transient pressure changes. The porosity of specimen O−4 is more significant than that of specimen O−1, and thus gaseous heat conduction’s contribution of specimen O−4 is more extensive than that of specimen O−1 as heating power increases. It is because air entry increases the contribution of the gaseous heat conduction after the transient pressure change.Further, specimen O−4′s ΔU refers to the voltage; I represents the electric current; A is the cross-section area.Quantitatively, the thermal insulation performance of SiC-doped silica aerogel is evaluated by the correlative thermal conductivity in the first and second steady state. Note that such correlative thermal conductivity at large temperature differences is not defined in thermal physics but can evaluate the specimen’s thermal insulation performance .(9)λ¯ and the best thermal insulation.(1)Transient pressure changes the heat transfer of the gas inside the aerogel, affecting the gaseous thermal conductivity and the thermal insulation performance. When the pressure decreases instantaneously, the hot surface’s temperature increases, and its thermal insulation performance is improved, and vice versa. The more extensive the transient pressure change is, the more significant the thermal insulation performance’s variation will be.(2)Tcold = 15 °C and ΔT = 171~912 K, the effective thermal conductivities of SiC-doped silica aerogel with 0, 1, and 5.84% SiC are 0.02220~0.06901, 0.02304~0.04077, and 0.02228~0.03550 W·m−1·K−1 at P ≈ 10 Pa, and 0.02856~0.07207, 0.03165~0.04665, and 0.03653~0.04541 W·m−1·K−1 at P = 1 atm, respectively.When (3)The higher SiC content increases the solid thermal conductivity of the aerogel, but the ability to restrain thermal radiation is more significant at large temperature differences.(4)Aerogels with greater porosity are strongly influenced by the movement of the internal gas molecules at transient pressure changes. The larger the porosity of the aerogel, the greater the influence of the internal molecular motion, the more noticeable change of the gaseous thermal conductivity, and the more significant change in thermal insulation performance.The thermal insulation performance of SiC-doped silica aerogel is investigated by testing their hot surface’s temperature response at transient pressure changes and large temperature differences. The insulation performance prediction model is proposed to describe unsteady-state heat transfer. Furthermore, the influence of the critical parameters on the thermal insulation is excavated based on the prediction model. The conclusions are drawn as follows:


Kaanders P, Sepulveda P, Folke T, Ortoleva P, De Martino B. 2022. Humans actively sample evidence to support prior beliefs. Published 11 April 2022For some of the analyses, z-scores for sampling times of each of the two choice options were computed per participants across both conditions (free and fixed sampling), rather than separately. Of course z-scoring should have been done separately for each of the conditions for all analyses, as the fixed condition imposed sampling times on the participants, while the free condition allowed participants to sample the two choice options freely. These analyses have now been rerun and the affected figures re-plotted using the correct z-scores. This error did not affect any of the main results.We regret this error and the article has been corrected accordingly.The corrected Figure 4 is shown here:The original Figure 4 is shown here for reference:The corrected Figure 2—figure supplement 1 is shown here:The original Figure 2—figure supplement 1 is shown here for reference:We have also updated a sentence in the Results section about Figure 2—figure supplement 1, from:16.59 = –6.39, P<0.001), but in contrast to experiment 1 there was no effect of sampling time difference on the second confidence rating .““Change of mind also negatively predicted confidence in the second choice phase , and there was again a positive effect of sampling time difference on the second confidence rating  as well as a significant negative interaction effect between change of mind and sampling time difference .”“Change of mind also negatively predicted confidence in the second choice phase (Figure 2—figure supplement 1; tThe corrected Figure 4—figure supplement 3 is shown here:The original Figure 4—figure supplement 3 is shown here for reference:The corrected Figure 4—figure supplement 4 is shown here:The original Figure 4—figure supplement 4 is shown here for reference:


Clostridrum ramosum IgA protease enzyme digestion, as predicted by the lack of the cleavage site in the rabbit hinges. Some IgA1s featuring long rabbit hinges were cleaved by Neisseria meningitidis IgA1 protease cleavage type 1 or 2 enzymes, despite the lack of the predicted cleavage sites. More interestingly, the hybrid featuring the rabbit IgA15 hinge was not affected by any of the IgA proteases. The IgA15 hinge is longer than that found in human IgA1 and is composed by a unique motif with a stretch of nine consecutive Ser residues. These characteristics allow the preservation of a long hinge, with associated ability to bridge distantly spaced antigens and provide higher avidity binding, while remaining resistant to IgA protease degradation. The data suggest that the rabbit Cα15 hinge represents an interesting alternative hinge sequence for therapeutic human IgA antibodies that remains resistant to proteolytic cleavage.Immunoglobulin A provides a major line of defence against pathogens and plays a key role in the maintenance of the commensal microbiota in the intestinal tract. Having been shown to be more effective at tumour cell killing than IgG and strongly active against pathogens present in the mucosae, IgA antibodies have been attracting significant attention in recent years for use as therapeutic antibodies. To improve their therapeutic potential, bioengineered IgA forms with increased serum half-life and neutralizing abilities have been developed but the IgA hinge, which impacts susceptibility to bacterial proteases and ability to bridge between target and effector cells, has not yet been explored. The European rabbit has 15 IgA subclasses with exclusive hinge region motifs and varying lengths, constituting a unique model to evaluate the functional capabilities offered by incorporation of longer IgA hinges into immunoglobulins. Hinge regions from rabbit IgAs, featuring different lengths and sequences, were inserted into human IgA1 heavy chain to substitute the IgA1 hinge. These hinges did not appear to affect antigen binding nor the ability of the engineered chimeric IgA1 to bind and trigger FcαRI, as detected by IgA-mediated cell agglutination and release of superoxide by neutrophils. All rabbit hinge-human IgA1 hybrids were resistant to  Much of it is secreted in mucosal tissues, such as the intestinal and respiratory tract, where it provides a major line of defence against pathogens and plays a key role in the maintenance of the commensal microbiota in the intestinal tract  immunoglobulin (Ig) heavy chain genes (IGH) have particularities that set this species apart from all studied mammals, including the presence of multiple IgA subclasses, being the most complex IgA system of all studied mammals  for therapeutic applications.A plasmid containing the gene for the heavy chain constant region of human IgA1 downstream of the mouse VNIP gene was used , 21. An The plasmid was cut using the restriction enzymes AgeI and XhoI (cutting site downstream of the hinge region) (New England BioLabs) for the removal of the human IgA1 hinge region. The hinges from rabbit Cα4, Cα5, Cα8 (or Cα13), and Cα15, containing the AgeI and XhoI sites at their 5’ and 3’ ends respectively, were synthesised by Integrated DNA Technologies as gBlocks Gene Fragments (supplementary data). Following restriction digestion with AgeI and XhoI, the hinge fragments were inserted into the plasmid. The hinge from rabbit Cα12 was introduced through site-directed mutagenesis using the primers Rb_Ca12_mut_fw 5’-CCC CAT TAC CTG CCA CCC CCG ACT GTC ACT GC-3’ and Rb_Ca12_mut_rv 5’-GGG ATG GAG CAG TCT GGA GTG GAG AGA TGG CCT GAA C-3’. The introduction of the rabbit hinges in place of the human hinge was confirmed in each case by sequencing.The FreeStyle 293 Expression System from Invitrogen was used to produce the hIgA1 and the hIgA1/rabbit hinge hybrids. FreeStyle™ 293-F Cells were kept in GIBCO FreeStyle 293 Expression Medium according to the manufacturer’s instructions. The cells were transfected with an expression vector containing the gene for a mouse anti-NIP λ light chain , and an The presence of IgA in the supernatant was confirmed by ELISA. Recombinant antibodies were purified using the CaptureSelect IgA-CH1 (Hu) Affinity Matrix (Thermo Fisher Scientific) according to the manufacturer’s instructions. Purified antibodies were concentrated using Amicon Ultra-15 Centrifugal Filter Units (Merck Millipore Ltd.) and kept in PBS supplemented with 0.1% sodium azide at -20°C.96 well MaxiSorp ELISA Nunc plates were coated with 10μg/mL NIP-BSA in coating buffer overnight at 4°C . After cClostridium ramosum AK183 supernatant (Neisseria meningitidis IgA protease cleavage type 1 and type 2 were prepared through precipitation from MC58 (type 1) and 18-172 (type 2) culture supernatants by addition of ammonium sulphate up to 50% saturation and followed by resuspension in Phosphate Buffered saline (PBS) and sterile filtration. 0.1μg of each IgA was incubated with 2μL of protease preparation in a total volume of 20μL (PBS), and incubated overnight at 37°C. The reaction was stopped by adding sample buffer  and boiling for 5 min. The reduced samples were loaded in a 12% polyacrylamide gel, and upon separation, the proteins were transferred to nitrocellulose membranes. The membranes were incubated with goat anti-human IgA  peroxidase-labelled antibody from SeraCare at a dilution of 1:3000, and developed using Pierce ECL Western Blotting Substrate.Protease preparation from ernatant  was a kiSheep blood in Alsever’s solution was washed in isotonic borate buffer (pH 8.5). The red blood cells (RBCs) were derivatized in the same buffer containing 10μg/mL of NIP-caproate-0-succinimide for 1 hour at room temperature. After washing with PBS, the RBCs were sensitized with hIgA1 or one of the hinge hybrids at different concentrations (from 200μg/mL to 12.5μg/mL). Coating levels for each antibody were found to be equivalent by reactivity with goat anti-human IgA FITC conjugate from BioRad and assessed by flow cytometry (data not shown). Neutrophils were isolated from healthy volunteers’ blood using the EasySep Direct Human Neutrophil Isolation Kit from StemCell according to the manufacturer’s instructions. 25μL of a 1% suspension of sensitized RBCs and 25μL neutrophils at 2x106 cells/ml in HBSS were incubated with 50μL PBS for 15 min at room temperature, centrifuged for 1 min at 50 x g and further incubated for 90 mins at 37°C. Upon incubation, cells were transferred to a haemocytometer to be counted. A rosette was defined as an effector cell having three or more RBCs attached.6 cells/ml in HBSS containing 0.1% BSA were added to each well. The plate was transferred to a plate reader and chemiluminescence was measured at regular intervals for 85min.Nunc LumiNuc 96-Well plates were coated with 10μg/mL of NIP-BSA in coating buffer and incubated overnight at 4°C. hIgA1 or one of the hinge hybrids were added in triplicate at 50 μg/ml, and the plate incubated overnight at 4°C. After washing with PBS, 100 μl of luminol solution (67μg/ml in HBSS containing 20mM HEPES buffer and 0.1% globulin-free BSA) was added to each well. 50μL of neutrophils (isolated as previously described) at 10For the purpose of this study, different hinges were chosen due to their characteristics. Our aim was to identify long hinges with improved characteristics. The Cα12 hinge was selected to test the impact of a very short hinge. From the longest hinges, two have clear protease cleavage sites, most do not have apparent IgA protease cleavage sites but possess Pro-Thr or Pro-Ser bonds possibly susceptible to cleavage and one, the hinge from IgA15, has no such bonds. As such, the Cα4 and Cα5 hinges were selected for their extended length (24 amino acids), with the Cα5 hinge containing sequences resembling known IgA protease cleavage sites, while the hinge from Cα4 does not have apparent IgA protease cleavage sites, but possesses Pro-Thr and Pro-Ser bonds possibly susceptible to cleavage. The hinge from Cα8  was also chosen for sharing the Cα4 characteristics: a long hinge lacking obvious IgA protease cleavage sites but with Pro-Ser bond. The hinge from Cα15, with 22 residues, was chosen for its unique composition Figure 1Analysis by electrophoresis and immunoblotting under reducing conditions of the hIgA1 and the hIgA1 incorporating the hinges from rabbit IgA subclasses , showed that all antibodies have a similar band at ≈ 60 kDa representing the IgA heavy chain Figure 2The integrity of the immunoglobulins was also analysed regarding their ability to bind the antigen. All the IgAs were produced containing the heavy and light chain variable regions with specificity for the hapten NIP (3-nitro-4-hydroxy-5-iodophenylacetate). Therefore, binding to the antigen could be evaluated by ELISA using plates coated with NIP. Using equal concentrations of the different immunoglobulins, no significant difference was found regarding antigen binding Figure 3The ability to interact with the Fcα receptor was evaluated through rosette formation between NIP-coated erythrocytes and neutrophils that naturally express this receptor. All immunoglobulins showed a comparable ability to mediate rosette formation, with an increased percentage of rosette formation observed for the erythrocytes coated with higher concentrations of antibody Figure 4Clostridium ramosum and Neisseria meningitidis strains producing a cleavage type 1 and cleavage type 2 proteases were used to digest the different immunoglobulins. As expected, the three enzyme preparations cleaved hIgA1 heavy chain in the hinge region to generate Fab and Fc fragments, proving their protease activity  to 24 amino acid residues . The Cα15 hinge has a very different amino acid composition than other hinges, being composed of 22 amino acid residues, 9 of which form a serine stretch , 19. DueAs expected, the replacement of the human IgA1 hinge with those of different rabbit IgA subclasses did not affect the ability of the antibody to bind to FcαRI. Binding to this receptor occurs through the Fc region , which wHaemophilus and Neisseria species  and not the type 1 protease (Pro-Ser specific)  under the project PTDC/BIA-OUT/29667/2017 – POCI-01-0145-FEDER-029667 FCT also supported the post-doctoral fellowships of AP (ref. SFRH/BPD/117451/2016) and the FCT Investigator grant of PJE (CEECIND/CP1601/CT0005).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Synthesizing mRNA in vitro is a standard and simple procedure. Adding the 5′ cap and 3′ polyadenylated (poly(A)) tail to make this mRNA functional for use as a vaccine or therapy increases the time and cost of production and usually decreases the yield, however. We designed mRNA that lacked a cap and poly(A) tail but included an internal ribosomal entry site (IRES) to initiate protein translation. To protect the 5′ and 3′ ends of mRNA from exonucleases, we added stable terminal hairpins. When compared against typical mRNA  tail but lacked hairpins), expression of the delivered reporter protein in HEK293 cells was similar. Using a triple instead of a single hairpin at each end increased protein expression even more. This method has the potential to simplify the production and reduce the cost of synthesizing exogenous mRNA for use as biologics or vaccines. This makes exogenous mRNA attractive for use as a therapeutic agent where only short-term expression of a protein is required, such as for a transient disease/injury or as a vaccine. mRNA-based vaccines were the first vaccines against SARS-CoV-2 approved in the United States and induce both humoral and cell mediated immunity against its spike protein  and HiScribe™ T7 Quick High Yield RNA Synthesis Kit were from New England BioLabs . Hi-Lo DNA Markers were obtained from Minnesota Molecular, Inc. . QIAprep spin miniprep kit was from Qiagen . LB Broth was purchased from Alfa Aesar . N1-Methylpseudouridine was from TriLink Biotechnologies . TransIT-mRNA lipid transfection kit was from Mirus Bio . CellTiter 96® Aqueous One Solution Cell Proliferation Assay was from Promega . Ethidium homodimer was purchased from Molecular Probes .Dulbecco’s Modified Eagle Medium (DMEM), Opti-MEM, 0.05% trypsin/0.53 mM EDTA and L-glutamine were all purchased from Gibco . Fetal bovine serum (FBS) was purchased from Atlanta Biologicals . Gentamicin Sulfate was from Corning . All restriction enzymes, DNA polymerase I (Klenow), T4 DNA ligase, Deoxynucleotides, Poly(A) polymerase, 3′-O-Me mE. coli cells  in LB Broth  at 37 oC overnight. The plasmids were then isolated from E. coli and purified using QIAprep Spin Miniprep Kit  following the manufacturer’s protocols. DNA plasmids were linearized by the BsaI-HF®v2 restriction enzyme  to terminate T7 polymerase and used as templates for a T7 promoter driven in vitro RNA synthesis.All template plasmids were derived from the pBR322 plasmid backbone  and contained a prokaryotic origin of replication and an ampicillin resistance gene for selection and were amplified in 1-Methylpseudouridine (m1Ψ)  as a substitute for all uridines (U). The synthetized RNAs were used as experimental mRNA vectors. Some mRNAs were designed to form a single stable 5′ (24 paired bp) and/or 3′ (30 paired bp) terminal hairpin (see schematics in figures). All hairpins had about 70% G/C content. Some mRNAs were designed to form a triple stable hairpin with 48, 34, and 36 paired bp individual hairpins on the 5′ end and/or 20, 21, and 28 paired bp individual hairpins on the 3′ end connected immediately to each other with no unpaired nucleotides in between (see schematics in corresponding figures). Each individual hairpin of the triple hairpin is structurally different from the other individual hairpins to avoid potential interference due to the formation of secondary structures.HiScribe™ T7 Quick High Yield RNA Synthesis Kit  was used to generate up to 180 μg of RNA per reaction from 1 μg of each linearized DNA template following DNase treatment to remove DNA template and LiCl Precipitation. For some experiments, mRNAs were synthetized using NSome mRNAs included 5’ (40 bp) and/or 3′ (40 bp) short poly(A) sequences that were placed either terminally (in the absence of hairpins) or directly connected to the terminal hairpins. All mRNA vectors encoded eGFP as the reporter protein. Most of the mRNA vectors had an EMCV IRES sequence upstream of the eGFP ORF. In the figures, the names of control vectors that did not have an IRES are underlined. All mRNA vectors with a single hairpin or with unstructured ends (unpaired nucleotides) had the same 5′ (68 bp) and 3′ (104 bp) length UTR, which flanked the internal cassette ; vectors with internal poly(A) stretches had 40 bp longer 5′ and/or 3′ UTR sequences; vectors with a triple hairpin structures had 137 bp 5′ UTR and/or 186 bp 3′ UTR. For an efficient in vitro transcription from the T7 promoter, all vectors with a short terminal 5′ poly(A) segment had two G nucleotides in front of the poly(A) stretch (GGAAAAAA…). The basic internal design of the tested mRNAs is shown in the Graphical Abstract and includes the eGFP coding region and an upstream IRES to initiate translation. To highlight the important differences between the mRNAs while keeping figures as simple as possible, only the differences in the terminal ends are shown. Although the length of the actual mRNA vectors varies, all figures are drawn to similar scale.E. coli Poly(A) Polymerase . The purity of mRNA and the length of poly(A) tails (if applicable) were confirmed by gel electrophoresis. Some mRNA vectors were capped with 3´-O-Me-m7G(5′)ppp(5′)G RNA Cap Structure Analog , New England BioLabs, NEB# S1411) using a 6:1 ratio of cap analog to GTP.Some mRNA vectors were tailed with poly(A) (range from 75–200 adenosines) using 2 and routinely passaged after reaching 80% confluency. Cells were harvested by 0.05% trypsin/0.53 mM EDTA digestion and counted with Coulter Z1 (Coulter Electronics).HEK293 cells (Human Embryonic Kidney cell line [Cat. No. CRC-1573]) and MDCK (NBL-2) cells (Madin-Darby Canine Kidney cell line [Cat. No. CCL-34]) were obtained from ATCC. Cells were grown in humidified incubators in DMEM/10%FBS supplemented with Gentamicin Sulfate and L-glutamine at 37 °C in 5% CO5 cells were aliquoted into pre-chilled 4 mm electroporation cuvettes (BTX). Ten μg of vector RNA was added into the cuvette. Negative control cells were electroporated in the same way, but no RNA was added. The cuvette was then inserted into the BTX 830  electroporation system and electroporation was carried out using a single pulse at 150 V for 15 milliseconds. The cells were then transferred into a 35 mm cell culture dish containing complete medium and incubated at 37 °C for 24–96 h. Cells were trypsinized and analyzed by BD Biosciences Canto II cell analyzer in the University of South Alabama Flow Cytometry Core.mRNAs designed for this project are compatible with any method of cellular transfection. However, to exclude any effect of lipid or cationic polymer formulations on mRNA stability and functionality, we chose to deliver mRNA by electroporation. In some experiments, we confirmed our electroporation findings with TransIT-mRNA lipid transfection kit . For mRNA vector electroporation, HEK293 or MDCK cells were suspended in 200 μl of ice-cold Opti-MEM. Cell suspensions containing 5 × 10Cell viability was assessed by CellTiter 96® Aqueous One Solution Cell Proliferation Assay , and ethidium homodimer uptake ; cell growth was determined by cell counts over 3 days.P < 0.05 considered significant. In all figures ‘n’ represents the number of independent experiments. Each independent experiment was done on a separate day and was done in triplicate.Data are expressed as mean ± SD. Changes in eGFP expression were compared using one-way ANOVA combined with Fisher post hoc analysis, with a We first studied the effect of incorporating stable secondary RNA hairpin structure(s) on either or both ends of mRNA on the efficiency of eGFP expression 24 h after vector transfection into HEK293 cells by electroporation Fig. . The resTransfection with the control vector without any hairpins (-R-) resulted in detectable, but very low eGFP expression Fig. . eGFP exIn some pox viruses, the presence of 5′-proximal short poly(A) sequences can act as strong translational enhancers that mediate the formation of the ribosomal initiation complexes independently of some multifunctional eIFs, (eIF3 and eIF4F)  potentiaTo determine whether internal poly(A) sequences would improve translational efficiency in vectors with terminal hairpins, we inserted relatively short (40 bp) poly(A) stretches immediately downstream of the 5′ terminal hairpin or upstream of the 3′ terminal hairpin and assessed eGFP expression. All vectors had an identical IRES-eGFP internal transcription cassette and differed only by the presence or absence of the terminal hairpins and short internal poly(A) segments. HEK293 cells were electroporated with these mRNA vectors and eGFP signal was measured 24 h later.eGFP expression was higher in vectors that had both 5′ and 3′ hairpins Fig. , when coThe addition of a longer poly(A) tail at the 3′ end of mRNA increases the protein expression of capped messengers by stabilizing RNA and activating translation regulatory mechanisms . WhetherWe replaced the 5′ and 3′ terminal hairpins with short poly(A) stretches and determined translation efficiency of these vectors in HEK293 cells. Cells were electroporated with the appropriate vector and eGFP expression was measured 24 h after vector delivery. The addition of a short terminal 3′ poly(A) sequence modestly increased eGFP expression in vectors that lacked a 3′ terminal hairpin  that contained the coding region for eGFP, a 5′ cap, and a 3′ poly(A) tail, but lacked an IRES and was designed to not form terminal hairpins. We compared this vector to vector haRh which showed the best performance in the previous experiment. Cells transfected with an unmodified vector (-R-)  tail) and cells electroporated without RNA were used as negative controls. We measured eGFP levels in transfected cells between 24 to 96 h after vector delivery  and haRh (which initiated translation through an IRES and lacked a cap and poly(A) tail) had comparable eGFP expression when used in equimolar concentrations  exogenous mRNA, we generated a vector  and a capped and adenylated version of mRNA with triple hairpins on both ends (ChhhRhhhA).To determine whether including a triple terminal hairpin structure in the delivered RNA improved protein expression above that of a single hairpin, we constructed three new mRNA vectors (without any poly(A) sequences because they had no effect on eGFP expression as shown in Fig. CRA). Unlike the effect of adding a cap and poly(A) tail to single hairpin constructs  Fig. . The veccts Fig. , howevercts Fig. . The flu1-Methylpseudouridine  wasHere, we show that mRNA designed to form hairpin secondary structures at both the 5′ and 3′ ends maintains a high level of reporter expression in eukaryotic cells, even in the absence of a 5′ cap and 3′ polyadenylated tail, as long as an internal ribosome entry site (IRES) is included in its 5′ UTR. Equimolar levels of IRES-containing mRNA vectors with single hairpins at both terminal ends showed the same level of protein expression as conventionally constructed (non-IRES-containing) mRNA that contained a 5′ cap and a 3′ poly(A) tail. Using a triple hairpin structure instead of a single hairpin at both ends further increased protein expression, outperforming capped and poly-adenylated vectors without an IRES.In general, exogenous mRNA is produced in three steps: (1) in vitro mRNA synthesis from a DNA template, 2) the addition of a modified guanosine cap on the 5′ end of the mRNA, and 3) the addition of a poly(A) tail on the 3′ end of mRNA [ the addi the addiThe question is whether mRNA can be synthesized without a cap and poly(A) tail and still function when introduced into cells. For in vitro mRNA transcripts to function without a 5′ cap would require an alternative way to initiate protein synthesis. One way to do this is to include an internal ribosome entry site (IRES) in the 5′ region to initiate translation. Cells use IRESs to increase translation of certain proteins during mitosis and programmed cell death , 50. IREOne way to protect exogenously generated uncapped and non-adenylated mRNA from exonuclease degradation is to construct circular RNA. IRES-driven RNA vectors can be engineered to form circular RNAs lacking both a cap and poly(A) tail. Such circular RNAs do not have free ends that are vulnerable to exonucleases and thus showed an increased stability that resulted in extended duration of protein expression . Howeverlocation of this stem-loop at, or very near, the 5′ and 3′ terminus is crucial to its stabilizing effect, whereas the sequence of this hairpin and its position relative to the ribosome binding site appears to have little effect. Up to two unpaired nucleotides upstream of the 5′ hairpin are tolerated without any reduction in mRNA stability, but the addition of 10–15 unpaired nucleotides of random sequence is as destabilizing as deletion of the 5′ hairpin [E.coli can recruit ribosomes and stabilize the message by blocking access of nucleases to degradative signals present in the naked mRNA [An alternative method for protecting the terminal ends of mRNA lacking a cap and poly(A) tail is to include nucleotide hairpins at the 5′ and 3′ ends. A nucleotide hairpin is a pairing of complementary base pairs that is an essential secondary structure of RNA. It can guide RNA folding, determine interactions with ribozymes, protect mRNA from degradation, serve as a recognition motif for RNA binding proteins or act as a substrate for enzymatic reactions . It has  hairpin . A stronked mRNA , 60.A role for terminal hairpin structures in eukaryotes has not been widely studied because such structures appear to be uncommon in metazoans. At the 5′ end, a terminal hairpin can interfere with cap-induced processes, and at the 3′ end, the majority of mRNAs are flanked by a polyadenylation signal followed by 10–30 downstream nucleotides and a poly(A) tail which makes the formation of a terminal hairpin unlikely , 11. NonWe hypothesized that we could protect both ends of the exogenous mRNA vector by adding stable hairpins during in vitro synthesis; this could stabilize the molecule and protect the mRNA from exonuclease degradation when delivered to target cells. However, this could be possible only with mRNA that does not depend on their ends to function. Including an IRES in the 5′ end to initiate translation would allow us to generate in vitro mRNA transcripts in a single step, bypassing the costly and time-consuming 5′ capping and 3′ poly (A) addition.Using this strategy, we generated an effective mRNA transcript that had single hairpins at each terminal end that was the equal of conventionally constructed mRNA that contained a 5′ cap and a 3′ poly(A) tail. The use of triple instead of single hairpins at each end resulted in the highest reporter expression of any vector tested. Such mRNA structures are uncommon in eukaryotic cell, but they can be easily synthetized in vitro in a single step and then delivered as drugs or vaccines. In these experiments, we used the EMCV IRES, but other IRESs may potentially be used as well. However, each IRES may function differently depending on the vector, so results would need to be validated experimentally .Exogenous mRNA used for therapy or vaccines often uses N1-methylpseudouridine (m1Ψ) instead of uridine to avoid the immune response and cytotoxicity induced by introducing mRNA into cells. This was based on the breakthrough studies from Kariko and co-workers, who showed that base modifications naturally found in human RNA such as pseudouridine, thiouridine, and 5-methylcytidine reduced the immunostimulatory potential of exogenously introduced RNA . m1Ψ subThese results demonstrate that a 5′ cap and a 3′ poly(A) tail are not always required for the successful expression of exogenously generated mRNA in eukaryotic cells. The inclusion of a 5′ IRES is sufficient to initiate translation of the encoded protein. The inclusion of hairpins (single or triple) at each end protects the mRNA against degradation by exonucleases. This provides a potential method for rapidly generating exogenous mRNA using a single step, thus saving time and reducing cost.