Psychonomic Bulletin & Review21(2), 301–308, Perspectives on Psychological Science7, 627–633, Psychonomic Bulletin & Review21(2), 301–308, Recently, optional stopping has been a subject of debate in the Bayesian psychology community. Rouder ( P value-based null-hypothesis significance testing (NHST) is widely used in the life and behavioral sciences, even though the use of p values has been severely criticized for at least the last 50 years. During the last decade, within the field of psychology, several authors have advocated the Bayes factor as the most principled alternative to resolve the problems with p values. Subsequently, these authors have made an admirable effort to provide practitioners with default Bayes factors for common hypothesis tests. Key references include, among many others, Rouder, Speckman, Sun, Morey, and Iverson  for scientific research, and we agree that the Bayes factor has many advantages. However, as also noted by Gigerenzer and Marewski  or undefined (type II priors).Rouder  was writstrong calibration, and which remains meaningful for all default priors. Then, however, we shall see that strong calibration fails to hold under optional stopping for all default priors except, interestingly, for a special type of priors  on a special (but common) type of nuisance parameters. Since these are rarely the only parameters incurring in one’s models, one has to conclude that optional stopping is usually a problem for pragmatic Bayesians—at least under Rouder’s calibration criterion of handling optional stopping. There exist (at least) two other reasonable definitions of ‘handling optional stopping’, which we provide in Section tools for inference just as much or even more than beliefs about the world, and should thus be equipped with a precise prescription as to what type of inferences they can and cannot be used for. A first step towards implementing this radical idea is given by one of us in the recent paper Safe Probability  of the model. In our example, 𝜃 expresses the bias of the coin, and is a real number between 0 and 1. After the specification of the prior, we conduct the experiment and obtain the data D and the likelihood posterior distributionBayes’ theorem:simple hypothesis. A hypothesis can also consist of two or more, or even infinitely many hypotheses, which we call a composite hypothesis. An example is: ‘the coin is biased towards heads’, so the probability of heads can be any number between 0.5 and 1, and there are infinitely many of those numbers. Suppose again that we want to test prior odds: D, and update our odds with the new knowledge, using Bayes’ theorem .Rouder  and Wage).Rouder  provide ).Rouder , which wmentclass2pt{minimposterior odds, it is our updated belief about which hypothesis is more likely. Right of the prior odds, we see the Bayes factor, the term that describes how the beliefs (prior odds) are updated via the data. If we have no preference for one hypothesis and set the prior odds to 1-to-1, we see that the posterior odds are just the Bayes factor. If we test a composite 𝜃. Bayesian inference tells us how to calculate The left term is called entclass1pt{minima𝜃0 denotes the parameter of the null hypothesis 𝜃1 is the parameter of the alternative hypothesis change in odds from prior to posterior in favor of the alternative hypothesis where optional stopping, but if we want to make more precise what it means if we say that a test can handle optional stopping, it turns out that different approaches  lead to different interpretations or definitions. In this section we adopt the definition of handling optional stopping that was used by Rouder, and show, by repeating and extending Rouder’s original simulation, that Bayesian methods do handle optional stopping in this sense. In the next section, we shall then see that for ‘default’ and ‘pragmatic’ priors used in practice, Rouder’s original definition may not always be appropriate—indicating there are problems with optional stopping after all.Validity under optional stopping is a desirable property of hypothesis testing: we gather some data, look at the results, and decide whether we stop or gather some additional data. Informally we call ‘peeking at the results to decide whether to collect more data’ μ = 0 or not. In Bayesian statistics, the composite alternative μ; like in Rouder’s example, we take the prior on the mean to be a standard normal, which is a fairly standard (though by no means the only common) choice , while a mean as large as 1.0 is neither typical nor exceedingly rare. We take the variance to be 1, such that the mean equals the effect size. We set our prior odds to 1-to-1: This expresses a priori indifference between the hypotheses, or a belief that both hypotheses are really equally probable. To give a first example, suppose we observe n = 10 observations Now we can observe the data and update our prior beliefs. We calculate the posterior odds, in our case equal to the Bayes factor, via Eq. D = :n is the sample size (ten in our case), and We start by repeating Rouder’s  second e Berger, . This exentclass1pt{minimaAs Rouder writes: ‘If a replicate experiment yielded a posterior odds of 3.5-to-1 in favor of the null, then we expect that the null was 3.5 times as probable as the alternative to have produced the data.’ In mathematical language, this can be expressed asn = 10 and a = 1/3.5; of course we would expect this to hold for general n and a. The quotation marks indicate that we condition on an event, i.e. a set of different data realizations; in our case this is the set of all data x1,…,xn for which the posterior odds are a. We say that Eq. calibration of the posterior odds. To explain further, we draw the analogy to weather forecasting: consider a weather forecaster who, on each day, announces the probability that it will rain the next day at a certain location. It is standard terminology to call such a weather forecaster calibrated if, on average on those days for which he predicts ‘probability of rain is 30%’, it rains about 30% of the time, on those days for which he predicts 40%, it rains 40% of the time, and so on. Thus, although his predictions presumably depend on a lot of data such as temperature, air pressure at various locations etc., given only the fact that this data was such that he predicts a, the actual probability is a. Similarly, given only the fact the posterior odds based on the full data are a (but not given the full data itself), the posterior odds should still be a  at which we stop; note that τ ≤ 25. Remarkably, it turns out that we still haveIndeed, it turns out that Eq. τ. In words, the posterior odds remain calibrated under optional stopping. Again, this can be shown formally, as a mathematical theorem .Following  really want to establish Eqs. When presenting Rouder’s simulations to other researchers, a common concern is: ‘how can sampling a parameter from the prior in mentclass2pt{minimstrong calibration, we sometimes (Example 1) still get calibration, but usually (Example 2) we do not; so such readers will likely agree with our conclusion that ‘optional stopping can be a problem for Bayesians’, even though they would disagree with us on some details, because we do think that Eq. Some readers of an earlier draft of this paper concluded that, given its equivalence to an experiment involving sampling from the prior, which feels meaningless to them, Eq. μ = 0, but the variance σ2 is unknown. Posterior calibration will still be obtained under optional stopping; the example mainly serves to gently introduce the notions of improper prior and strong vs. prior calibration, that will play a central role later on. So, σ2, and σ2, and some mean μ, where the uncertainty about μ is once again captured by a normal prior: the mean is distributed according to a normal with mean zero and variance (again) σ2 . If σ2 = 1, this reduces to Rouder’s example; but we now allow for arbitrary σ2. We call σ2 a nuisance parameter: a parameter that occurs in both models, is not directly of interest, but that needs to be accounted for in the analysis. The setup is analogous to the standard 1-sample frequentist t test, where we also want to test whether a mean is 0 or not, without knowing the variance; in the Bayesian approach, such a test only becomes defined once we have a prior for the parameters. For μ we choose a normal,σ we will make the standard choice of Jeffreys’ prior for the variance: σ cf. Eq. We now adjust Rouder’s example to a case where we still want to test whether  et al., . To obtaσ is a ‘measure’ rather than a distribution, since it does not integrate to 1: clearlyimproper. Use of such priors for nuisance parameters is not really a problem for Bayesian inference, since one can typically plug such priors into Bayes’ theorem anyway, and this leads to proper posteriors, i.e., posteriors that do integrate to one, and then the Bayesian machinery can go ahead. Since Jeffreys’ prior is meant to express that we have no clear prior knowledge about the variance, we would hope that Bayes would remain interpretable under optional stopping, no matter what the (unobservable) variance in our sampling distribution actually is. Remarkably, this is indeed the case: for all Formally, Jeffreys’ prior on aand that the actual variance is a, irrespective of what In words, this means that, given that the posterior odds  are equal to σ2 of the normal distribution(s) from which we sample. Whereas, as in the previous experiment, we can sample the mean in 𝜃. But we cannot directly sample σ from an improper prior. As an alternative, we can pick any particular fixed σ2 to sample from, as we now illustrate. Let us first try σ2 = 1. Like Rouder’s example, we sample the mean of the alternative hypothesis To repeat Rouder’s experiment, we have to simulate data under both entclass1pt{minimaentclass1pt{minimaσ2 = 1, or σ2 = 2, or any other value—in simulation terms Eq. σ2 we actually sample from: even though calculation of the posterior odds given a sample makes use of the prior σ, calibration is retained under sampling under arbitrary ‘true’ σ. We say that the posterior odds are prior-calibrated for parameter μ and strongly calibrated for σ2. More generally and formally, consider general hypotheses γ0,γ1 and suppose that Eq. γ1 in the role of σ2. Then we say that γ0 is prior-calibrated  and γ1 is strongly calibrated .Importantly, the same conclusion remains valid whether we sample data using σ2 will cease to hold; we will see examples below. The reason that σ is a special type of nuisance parameter for which there exists a suitable group structure, relative to which both models are invariant , then the Bayes factor remains unchanged; similarly, one may have for example location invariances.Notably, strong calibration is a special property of the chosen prior. if we had chosen another proper or improper prior to calculate the posterior odds , then we obtain strong calibration, both for fixed sample sizes and under optional stopping, relative to these parameters.If such group structure parameters are equipped with a special prior  but often have one or two parameters that can be specified in a subjective way. These may then be replaced by more informative priors when more knowledge becomes available after all. We will see several examples of such default priors below.On the other end of the spectrum, the 𝜃 from the prior and then data from 𝜃 within a region that does not have overly small prior probability.4So what category of priors is used in practice? Recent papers that advocate the use of Bayesian methods within psychology such as Rouder et al., , Rouder Type 0 priors: these are priors on parameters freely occurring in both hypotheses for which strong calibration  or the data  and are not of type 0 above. Thus, strong calibration under optional stopping is violated with such priors—an example is the Cauchy prior in Example 2 of Section Example 2: Bayesian t test with default priors below.Type II priors: these are default and pragmatic priors that are not of type 0 or I: the priors may themselves depend on the experimental setup, such as the sample size, the covariates (design), or the stopping time itself, or other aspects of the data. Such priors are quite common in the Bayesian literature. Here, the problem is more serious: as we shall see, prior calibration is ill defined, and correspondingly Rouder’s experiments cannot be performed for such priors, and ‘handling optional stopping’ is in a sense impossible in principle. An example is the g-prior for regression as in Example 3 below or Jeffreys’ prior for the Bernoulli model as in Section The point of this article is that Rouder’s view on what constitutes ‘handling optional stopping’ is tailored towards a fully subjective interpretation of Bayes; as soon as one allows default and pragmatic priors, problems with optional stopping do occur . We can distinguish between three types of problems, depending on the type of prior that is used. We now give an overview of type of prior and problem, giving concrete examples later. t test, going back to /σ; details are provided by Rouder et al.,  that the fertilizer will enable the corn to grow to 8 m on average? Of course we could use a Cauchy with a different spread, but which one? Default Bayesians have emphasized that such choices should be made subjectively , but whatever value one choices, the chosen functional form of the prior  severely restricts the options, making any actual choice to some extent arbitrary. While growing crops  may be particularly ill-suited to be modeled by heavy-tailed distributions, the same issue will arise with many other possible applications for the default Bayesian t test: one will be practically sure that the effect size will not exceed certain values , but it may be very hard to specify exactly which values. As a purely objective Bayesian, this need not be such a big problem—one resorts to the default prior and uses it anyway; but one has to be aware that in that case, sampling from the prior—as done by Rouder—is not meaningful anymore, since the data one may get may be quite atypical for the underlying process one is modeling.Prior calibration in terms of Eq. μ on the effect size instead, truncated at 0 to avoid negative means. But then there is the question what variance this Gaussian should have—as a pragmatic Bayesian, one has to acknowledge that there will always be arbitrary or ‘convenience’ aspects about one’s priors. This is the second reason why it is interesting to study not just prior calibration, but also strong calibration for the parameter of interest.In practice, most Bayesians are pragmatic, striking a balance between ‘flat’, ‘uninformative’ priors, prior knowledge and ease of computation. In the present example, they might put a Gaussian prior with mean Thus, both from a purely objective and from a pragmatic Bayesian point of view, strong calibration is important. Except for nuisance parameters with type 0 priors, we cannot expect it to hold precisely  of our simulation experiment. If we would like to repeat the examples of the previous sections and construct the calibration plots, we can generate the y-values—the increase or decrease in length of the wheat from the intercept μ—according to the proposed priors in Eq. g from an inverse gamma distribution, then we draw a β from the normal prior that we construct with the knowledge of the x-values, and we compute each yi as the product of β and xi plus Gaussian noise.We further extend the previous example to a setting of linear regression with fixed design. We employ the default Bayes factor for regression from the R package Bayesfactor , it does depend on the design/covariates . If there is no optional stopping, then for a pragmatic Bayesian, the dependency on the x-values of the data is convenient to achieve appropriate scaling; it poses no real problems, since the whole model is conditional on X: the levels of fertilizer we administered to the plants. But under optional stopping, the dependency on X does become problematic, for it is unclear which prior she should use! If initially a design with 40 pots was planned , but after adding three pots to the original twenty , the researcher decides to check whether the results already are interesting enough to stop, should she base her decision on the posterior reached with prior based the initially planned design with 40 pots, or the design at the moment of optional stopping with 23 pots? This is not clear, and it does make a difference, since the g-prior changes as more x-values become available. In Fig. g-priors on the regression coefficient β for the same fixed value of g, the same x-values as described in the fertilizer experiment above, but increasing sample size. First, each dose is administered to one plant, yielding the black prior distribution for β. Next, three plants are added to the experiment, with doses 0.1,0.2 and 0.3, yielding the red distribution: wider and less peeked, and lastly, another 11 plants are added to the experiment, yielding the blue distribution which puts even less prior mass close to zero.As we can see in Eq. n always using the prior for the given design at sample size n; or one could, as a rule, always use the design for the maximum sample size available. It is very unclear though whether there is any sense in which any of these two (or other) solutions ‘handle optional stopping’ convincingly. In the first case, the notion of prior calibration is ill defined, since β from the prior based on the design at the maximum sample size), but it seems rather meaningless, for if, for some reason or other, even more data were to become available later on, this would imply that the earlier sampled data were somehow ‘wrong’ and would have to be replaced.This problem may perhaps be pragmatically ‘solved’ in practice in two ways: either one could, as a rule, base the decision to stop at sample size β is meaningful in this regression example. However (figures omitted), when we pick reasonable values for β instead of sampling β from the prior, we obtain again the conclusion that strong calibration is, on one hand, violated significantly under optional stopping (where the prior used in the decision to stop can be defined in either of the two ways defined above); but on the other hand, only violated mildly for fixed sample size settings. Using the taxonomy above, we conclude that optional stopping is a significant problem for Bayesians with type II priors.What, then, about strong calibration? Fixing particular, ‘reasonable’ values of D consist of a sequence of n1 ones and n0 zeros. Under 𝜃) for any 0 ≤ 𝜃 ≤ 1 except 1/2, 𝜃 representing the bias of the coin. One standard objective and default Bayes method (in this case coinciding with an MDL (minimum description length) method, Grünwald, π is for normalization; see Fig. 𝜃, within 0.01 of either 1 or 0, would be about ten times as large as the probability that we would pick a 𝜃 within the equally wide interval . But, lacking real prior knowledge, do we really believe that such extreme values are much more probable than values around the middle? Most people would say we do not: under the subjective interpretation, i.e., if one really believes one’s prior in the common interpretation of ‘belief’ given in Section Now let us turn to discrete data: we test whether a coin is fair or not. The data 𝜃 is intuitively a nuisance parameter, and thus strong calibration relative to this parameter would be especially desirable. However, the Bernoulli model does not admit a group structure, and hence neither Jeffreys’ nor any other prior we know of can serve as a type 0 prior, and strong calibration can presumably not be attained—the experiments show that it is certainly not attained if the default Gunel and Dickey Bayes factors . This is the main point concerning optional stopping of  is only vaguely acceptable... So it is important not only to know the exact answer for an exactly specified initial problem, μ which were not too unlikely under the prior, which one may perhaps view as ‘a reasonable neighborhood of the initial opinion’.— note that when we checked for strong calibration, we took parameter values “ ...in practice the theory of personal probability is supposed to be an idealization of one’s own standard of behavior; the idealization is often imperfect in such a way that an aura of vagueness is attached to many judgments of personal probability...” Savage, .We cannot resist to add here that, while for a subjective Bayesian, prior-based calibration is sensible, even the founding fathers of subjective Bayes gave a warning against taking such a prior too seriously:10n.Hence, one would expect that even a subjectivist would be interested in seeing what happens under a sensitivity analysis, for example checking for strong rather than prior-based calibration of the posterior. And even a subjectivist cannot escape the conclusion from our experiments that optional stopping leads to more brittle (more sensitive to the prior choice) inference than stopping at a fixed do handle optional stopping in this frequentist sense.Interestingly, some other well-known Bayesian arguments claiming that ‘optional stopping is no problem for Bayesians’ really show that some Bayesian procedures can deal, in some cases, with optional stopping in a different, frequentist sense. These include  frequentist interpretation of handling optional stopping is about controlling the type I error of an experiment. A type I error occurs when we reject the null hypothesis when it is true, also called a p value. However, in the case that simple , there is a well-known intriguing connection between Bayes factors and type I error probabilities:At first sight none of this seems applicable to Bayesian tests, which output posterior odds rather than a H0 iff the posterior odds in favor of α, then we are guaranteed a type I error of at most α. And interestingly, this holds not just for fixed sample sizes but even under optional stopping. Thus, if one adopts the rejection rule above , for simple, frequentist optional stopping is no problem for Bayesians. This is what was noted by Edwards et al., . As to optional stopping under t test, all free parameters in δ and desired level of type II error β, a threshold B can be determined such that the standard Bayesian t test with  the following optional stopping and decision rule, has type II error β: B or smaller than 1/B; accept B, and reject B.Take at least 20 data points. After that, stop as soon as posterior odds are larger than For example, if δ ≥ 0.3 and one takes B = 7 then the type II error will be smaller than 4% , optional stopping with the t test is indeed unproblematic. Note that this does not contradict our findings in any way: our simulations show that if, when sampling, we fix an effect size in Bayesian way. depend on aspects of the problem at hand such as the stopping rule or the inference task of interest. To make sense of such priors generally, it thus seems necessary to Bayesian statistics’. Given the betting interpretation above, the essence here is that we need to make a distinction between the purely subjective and the pragmatic approach: we can certainly not evaluate and interpret all Bayesian statistics as purely subjective Bayesian statistics, what Rouder (default Bayes factors developed for practical use in social science: both strong calibration and the frequentist type I error guarantees will typically be violated, and for Bayes factors involving type II priors, both prior and strong calibration are even undefined. In Table Rouder  argues it Rouder  seems tosafe tests of Grünwald, de Heide, & Koolen, E-variables: E-variables are related to, and sometimes coincide with, default Bayes factors, but tests based on E-variables invariably handle a variation of frequentist optional stopping. For example, the three default Bayes factors that handle frequentist optional stopping in Table E-variables, but there exist other E-variables for these three settings that also handle optional stopping but achieve higher frequentist power; and there also exists an E-variable for contingency tables that, unlike the default Bayes factor, handles frequentist optional stopping.While some find the purely subjective Bayesian framework unsuitable for scientific research (see e.g., Berger,


Metschnikowia yeasts. Self-splicing mitochondrial introns are mobile elements, which can copy and paste themselves at specific insertion sites in mitochondrial DNA using a homing endonuclease in conjunction with homologous recombination. Here, we explore the mutational effects of self-splicing introns by comparing sequence variation within the intron-rich cox1 and cob genes from 71 strains (belonging to 40 species) from the yeast genus Metschnikowia. We observed a higher density of single nucleotide polymorphisms around self-splicing-intron insertion sites. Given what is currently known about the movement of organelle introns, it is likely that their mutational effects result from the high binding affinity of endonucleases and their interference with repair machinery during homologous recombination . These findings suggest that there are fitness costs to harbouring self-splicing, mobile introns and will help us better understand the risks associated with modern biotechnologies that use endonuclease-mediated homologous recombination, such as CRISPR-Cas9 gene editing.It has been argued that DNA repair by homologous recombination in the context of endonuclease-mediated cleavage can cause mutations. To better understand this phenomenon, we examined homologous recombination following endonuclease cleavage in a native genomic context: the movement of self-splicing introns in the mitochondrial genomes of  The ability to safely modify the DNA of living organisms may soon be within our grasp thanks to CRISPR-Cas9. Despite its immense promise, CRISPR-Cas9 gene editing has not yet been approved for widespread therapeutic use because of its potential for introducing unwanted mutations . Recent An excellent model for studying the relationship between endonuclease activity and on-target mutagenicity in a native genomic context is the movement of self-splicing introns in mitochondrial genomes. Self-splicing introns are a class of mobile element commonly found in the mitochondrial DNAs (mtDNAs) of plants, fungi, and protists . These eMetschnikowia , and the presence/absence of intronic homing endonucleases, please refer to Supplemental Information of Although self-splicing mitochondrial introns are found in a diversity of taxa , they arhnikowia , many meectively , making ectively . For detMetschnikowia mtDNAs, there is a 30-fold size range for cox1 and a 20-fold size range for cob, which encompasses the entire known size variation for both genes across the eukaryotic domain , both between and within species.Large variations in intron number can lead to remarkable differences in gene and genome size . Among Mc domain . More as to 5 kb  — and ken 1.6 kb . MetschnMetschnikowia may not be coincidental , 5 nt, and 10 nt to the left and right of each intron insertion site. The number of SNPs that fell outside of these windows were also counted. Note: for the 10 nt windows, there were instances where a single SNP was present in two different windows , they also indicate that there is significant variation outside of our selected windows as well, and that forces other than intron movement are likely contributing to the observed SNPs. Keeping in mind the huge reservoir of mitochondrial introns within this genus, there is a strong possibility that introns recently existed in other regions of the genes but have recently been lost. If true, this could have potentially led to some of the SNPs observed outside of the defined windows. We reiterate our call for more research on both the potential mutagenicity of self-splicing intron movement and the high amount of mtDNA variability exhibited by species of the genus Metschnikowia.In simpler terms, we found that there was an average of 1.2x more SNPs in the cox1 and cob, respectively. Position 7 to the left of the insertion site showed the highest average number of SNPs for both gene alignments  across the 25 and 13 intron insertion sites of ignments , suggestMetschnikowia species accumulated so many mitochondrial introns? In other words, why would this genus retain something that arguably imposes a high mutational burden? The simplest answer is that Metschnikowia may not be able rid itself of these selfish elements. This could be a consequence of where the insertion sites are located as well as recurring horizontal gene transfer events involving the lateral exchange of mitochondrial introns between species and strains . One explanation for these observations is that mitochondrial introns can lead to inefficient splicing; thus, in genes where introns abound transcription needs to be upregulated, but removal of the introns can result in an overabundance of transcripts. This evolutionary lock-in event may partly explain the longstanding persistence of self-splicing introns in mitochondrial genomes, despite their potential deleterious effects.There is also the possibility that Metschnikowia species explored here are all insect symbionts (mostly of beetles). Although speculative, this symbiotic lifestyle could potentially reduce their reliance on proper mitochondrial function, as their insect vectors constantly make sure that they occupy the proper habitat. It may also greatly impact the population genetics of these yeasts, potentially increasing random genetic drift.Finally, it is noteworthy that the We observed a higher density of SNPs near to self-splicing intron insertion sites, which we argue is the result of mitochondrial intron movement. The mutagenicity of self-splicing introns may result from incorrect mtDNA repair following homologous recombination due to the persistent presence and high binding affinity of endonucleases. These findings provide further support for the notion that, while once considered to have no ill effects , self-sp


Generation and control of humidity in a testing environment is crucial when evaluating a chemical vapor sensor as water vapor in the air can not only interfere with the sensor itself, but also react with a chemical analyte changing its composition. Upon constructing a split-flow humidity generator for chemical vapor sensor development, numerous issues were observed due to instability of the generated relative humidity level and drift of the humidity over time. By first fixing the initial relative humidity output of the system at 50%, we studied the effects of flowrate on stabilization time along with long term stability for extended testing events. It was found that the stabilization time can be upwards of 7 h, but can be maintained for greater than 90 h allowing for extended experiments. Once the stabilization time was known for 50% relative humidity output, additional studies at differing humidity levels and flowrates were performed to better characterize the system. At a relative humidity of 20% there was no time required to stabilize, but when increased to 80% this time increased to over 4 h. With this information we were better able to understand the generation process and characterize the humidity generation system, output stabilization and possible modifications to limit future testing issues. Replicating realistic environmental conditions on a small scale is essential when testing and developing chemical vapor sensors. In particular, the presence of water vapor, or relative humidity (RH), in the air is a real world challenge in almost all scenarios and must be taken into consideration as it can have a dramatic impact on a sensor’s performance.4. Therefore, it is possible that under completely dry conditions, very low RH, a sensor would detect a chemical, but under humid conditions the sensor may not due to the presence of hydrolysis breakdown products or the influence of the ambient RH.Ambient humidity can cause an instrument to produce a false alarm or fail to alarm  depending on the sensor type or algorithm. Moreover, it is also possible for the water vapor to interact with a target chemical vapor, analyte, through hydrolysis causing the chemical structure to change thus further complicating the problem6. The split-flow system presented was designed and built in-house from commercially available components and its performance monitored using in-house programmed software and commercially available transducers. While the system was simple to operate and control, several issues were observed during initial use and testing. Constant adjustments to the humidity control valves were required to counteract the fluctuations and instability in the humidity output and levels within a test chamber. Due to these observed fluctuations, the ability to perform long term experiments would be questionable, as constant adjustments to the system would be required to compensate for the instability in RH levels. The time required for the system to reach a stable RH level, equilibration time, is also unknown. Because of the instability of the humidity generated using this type of system, further investigation of the problem was required to gain a better understanding of the problem in order to mitigate the issues outlined above.During testing and evaluation, humidity can be generated and controlled using numerous commercial or non-commercial systems, including the split-flow design utilized for the work detailed in this paperWhile a split-flow design is an established method for generating humidity, the issues observed regarding stabilization time of a system and the resultant decrease in humidity are not well documented. Experiments and results documented herein shed light on an often overlooked or undocumented area of research that would likely be encountered by a user of this system type. This study only investigates one method of humidity generation, but the resulting information could be applied to other methods as they may be impacted by similar environmental and physical limitations.7. Expressing humidity is possible using a number of different measurements with RH being common. RH is the ratio of partial vapor pressure to equilibrium vapor pressure of water in the air at a specific temperature. The pressure dependency of humidity is related through Dalton’s law where the total exerted pressure  is equal to the sum of the partial pressures of the gases, as shown through Eq.  must decrease, as related through Eq. , i.e., ad in Eq. , where TIt should be noted that Eq.  provides9. By fixing these variables, the relationship between air flowrate, RH generated and the equilibration time can be investigated.During test and evaluation of the humidity generator, a controlled humid air stream was generated through sub-surface aeration of a water reservoir using a sparger, as shown in Fig. 10. The system described in this paper is of a split flow design constructed from commercially available components with the schematic shown in Fig. Many types of humidity generators and controller methods exist, they range from split flow, multi flow, to salt solution baths, and pressure systems4. Construction of the system is simple, can be built in-house using commercially available components, with the exception of the sparger, which was custom sized, but there are commercial alternatives available (Mott micro sparger tips). The use of a single MFC maintains the total flow volume of the system rather than multiple MFCs used for a multi flow design, removing a source of error in the total flow volume and the need to adjust and control multiple MFCs. When mixing the humid air stream with a chemical analyte supplied by a separate MFC, calculations are needed to determine the mixing ratios due to the additional supply of analyte stream. This is again simplified by using a single MFC as opposed to multiple MFCs to control the dry and wet streams separately. Safety is also a consideration for the generation system, where use of a split flow design maintains the system at near ambient pressure unlike a two pressure system that generates a pressurized saturated stream that is mixed with the dry stream in an expansion chamber. For this effort we did not address temperature management and instead operated at ambient laboratory temperatures of 21 ± 1 °C. Note that a temperature change of 1 °C would result in a RH change of approximately 3% which were observed, but did not greatly impact the experiments. In summary, a split-flow design provides a simple solution to humidity generation and control using commercially available components.A split flow design offers several advantages over other common types of humidity generators and controllersInitial experiments were conducted to determine if the system could produce a steady stream of humid air, as well as the time required to reach a steady state, or equilibrium. An initial target RH of 50% was chosen, a common setting based on past experimental needs, with a flowrates in the range of 1 to 10 L/min. Prior to the start of the air flow, the wet valve was closed and dry valve was fully opened. The sparger was filled with deionized water to a predetermined level, and the desired air flowrate was set in the MFC. The air flow was started and the system was allowed to run until the reported RH level was below 2% to remove any condensed water remaining in in the lines from previous experiments. After the system was cleared of any residual moisture, the level was visually monitored and needle valves adjusted until the 50% RH target was reached and remained unchanged for approximately 1 min. The system was then allowed to run for a minimum of 12 h with the temperature and RH values recorded every second by the LabVIEW software.11. This level of fluctuation is within tolerances of many commercially available environmental chambers.Experiments were conducted for flowrates of 1, 2, 3, 4, 4.5, 5, 7, 8.5 and 10 L/min all initiated at the starting RH of 50%. The flowrates and range were chosen as they are common measurement rates for commercially available handheld gas sensors. Equilibration was defined as a change in RH of ± 2% to determine the equilibration time. This value was selected as this is a commonly observed fluctuation outdoorsAfter establishing an equilibration time for the system over a range of air flowrates, the observed decrease in RH over time was investigated. Repeating the initial steps from the previous experiments, the starting RH was set to about 57%, above the target RH of 50%, at a flowrate of 5 L/min and allowed to run for 12 h. The temperature and RH were monitored and recorded continuously, with the goal for the system to equilibrate at the target RH of 50%.To determine the effect of different starting RH values on time to equilibration and stability, a series of experiments were conducted where the starting RH was set to 20, 50 or 80%, with flowrates of 2, 5, 7, 8.5 or 10 L/min. These experiments were conducted in triplicate, with the system to running for 12+ h per replicate. The goal was to establish the relationship between, RH, flowrate and equilibration time.Initial equilibration time experiment results with a target RH of 50% and flowrates ranging from 1 to 10 L/min are shown below in Table At a flowrate of 1 and 2 L/min there was no significant change in RH over the duration of the experiment and thus no time for the humidity generator to equilibrate is required. As the flowrate of air increased above 3 L/min, there was continuous decrease from initial RH and the equilibrium RH with the greatest decrease around 7% which was observed at flowrates of 5 and 10 /min. The time to equilibrate ranged from about 210 to 500 min (3.5 to 8 h) over the flowrate range of 3–10 L/min. No obvious trends in the equilibration time or equilibrium humidity were observed, such as a linear decrease in humidity with flowrate. The equilibration time varied by as much as 150 min within the same flowrate experiments. There were increasing and decrease equilibration times observed with increase flow rate, e.g. at 5 L/min the equilibration time was 210–300 min, but at both and 7 L/min the equilibration time was greater. The volume of the sparger may have played a role in this as it has volume of only 2 L, with approximately 1.5 L of water. At 10 L per minute, five times the volume of air is being pushed through the sparger than its volume and more than six times the volume of water it contains. Later experiments will show that at lower humidity levels and thus lower air flow rates through the sparger, no equilibration time is required.For higher flowrates the volume of water in the sparger decreased slightly over longer test periods of up to 96 h, however no noticeable change in RH was observed due to the water volume changing. Therefore, longer term, multi-day experiments could be performed with minimal concern of effects due to the sparger volume decrease.12. The overall temperature change was less than 1 °C, presumably from variations in laboratory air temperature. This resulted in a RH fluctuation of less than 1% after equilibrium, which is within the tolerance defined above. While there is no direct evidence that evaporative cooling within the sparger occurred, it is possible that the drop in RH until equilibrium is reached is due to this effect. Additional temperature probes could be placed within the sparger air space and along the air flow path to determine this was in fact the source of the observed variation.Fluctuations in RH were observed to be inversely proportional to the temperature, as expected due to the dew point temperature relation described Eq. , and is 3To counteract the observed decrease in RH during equilibration, compensation could be made by overshooting the starting RH level and allowing the system to equilibrate to the target RH. With a target RH of 50% and an observed decrease of 7% in 5 experiments, denoted by the bold text of Table 9. While equilibration time was observed to be up to 8 h, the ability to start the system the prior day and allow it to come to equilibrium over night with little effect on performance negates some concerns associated with required long equilibration periods.Several experiments were allowed to run for up to 96 h to assess the long term stability of the generation system. Such long experimental runs support multi day testing and demonstrate that the humidity generation can be started the day before testing in order to allow the system equilibrate. At a flowrate of 2, 5, and 10 L/min, generation of humidity was stable for 86, 91, and 96 h, respectively, though longer generation period is possible as the sparger water volume only slightly decreased. As previously discussed, decrease in the sparger water volume did not have a noticeable effect on humidity generation or stability, at < 1% RH change, though this may not hold true on a larger scale, such as a pondTo study equilibration time and stability of RH environmental conditions other than 50%, experiments were conducted at 20 and 80% RH. Flowrates of 2, 5, 7, 8.5 and 10 L/min were used and the results are shown in Table For all of the experiments ran with a starting RH of 20%, there was no change observed in the RH through the course of the experiments, and therefore there was no equilibration time required. Unlike the experiments ran with a starting RH of 50% where at the lowest flow rates of 2 L/min there was no change in the RH, when the starting RH was increased to 80%, an equilibration time of at least 210 min was required. As the flow rate increased, so did the equilibration time, but for both the 50% and 80% start RH experiments at the highest flow rate of 10 L/min the equilibration time did decrease below the maximum observed value. The stabilization humidity level decreased with a range of 5 to 20%, with a greater decrease at the 80% RH starting point and higher flow rates of 8.5 and 10 L/min.9. This is supported by the results, comparing the lower 20% RH setting with the higher 80% RH, where at the lower setting there is no equilibration time, but at a higher humidity the sparger has to equilibrate due to the high air flow to create the more highly saturated water vapor. Another option to increase the saturated vapor pressure would be to increase the water temperature, however, this would require the rest of the system to be conditioned to avoid condensation. Ultimately, as the flowrate or initial RH setting increases, the stabilized RH drop also increases.For higher flowrates and humidity levels the lack of aerator surface area could limit the interaction between the air and water preventing enough water content in the air thus resulting in greater decrease in RH level when stabilization is achieved. Aeration efficiency can be increased by increasing the aerator surface area, decreasing pore size to create smaller, more abundant bubbles, and increasing the water column length of the sparger. These changes increase travel path of air or number of bubbles allowing for more interaction between the water and air, creating more water saturated airFurther experiments are needed to determine if the decrease in RH levels at stabilization can be compensated for at high flowrates and RH levels. The highest observed RH after stabilization with the system during initial testing was 81% at 5 L/min with the wet valve fully open and dry valve closed. Overall, lower and higher humidity levels are achievable and sustainable using the split flow system with the knowledge that at RH levels of 80%, there is a considerable decrease once equilibrium is reached.A split flow humidity generator and controller provides a simple method to create a controlled humidity stream for chemical vapor sensor testing. Initial RH instability in the system prompted further investigation into equilibration and the factors that affect the stability. Starting with a target of 50% RH, the time to achieve stability across a range of 1–10 L/min flowrate was tested and it was determined that as the flow went beyond 3 L/min stabilization could take several hours, though once reached, remained stable for over 90 h. This opens up the possibility to perform longer term, multiday sensor evaluations. One complication to the equilibration process is a drop in the stabilized RH, however this decrease can be compensated for as shown experimentally. Similar trends were observed with a starting RH of 20 and 80%, where stabilization took upwards of 8 h with a decrease from the starting RH level, but remained stable for several days after. Future work would look into different types of aeration media that may more efficiently generate saturated water vapor at higher air flowrates as well as automating the needle valves to adjust for humidity drift.


Within the Interstitial Lung Diseases (ILD), patients with idiopathic pulmonary fibrosis (IPF) and a subset of those with non-IPF fibrotic ILD have a distinct clinical phenotype of progression despite management. This group of patients has been collectively termed the progressive fibrotic phenotype (PFP). Their early recognition may facilitate access to antifibrotic therapies to prevent or slow progression. Macrophages/monocytes within the lung orchestrate the progression and maintenance of fibrosis. A novel role for monocyte-derived macrophages during tissue damage and wound healing is the expression of collagens. We examined Collagen 1a1 expression in airway macrophages from ILD patients at diagnosis. COL1A1 mRNA levels from BAL cells were elevated in IPF and Non-IPF patients. The presence of a UIP pattern and a subsequent progressive phenotype were significantly associated with the higher BAL COL1A1 levels. In Non-IPF patients, higher COL1A1 levels were associated with a more than twofold increase in mortality. The intracellular localisation of COL1A1 in airway macrophages was demonstrated by confocal microscopy in CD45 and CD163 co-staining assays. Additionally, airway macrophages co-expressed COL1A1 with the profibrotic SPP1 gene product osteopontin. The levels of SPP1 mRNA and OPN in the BAL were significantly higher in IPF and Non-IPF patients relative to healthy. Our results suggest that profibrotic airway macrophages are increased in the BAL of patients with IPF and other ILDs and co-express COL1A1 and OPN. Importantly, COL1A1 expression by pro-fibrotic airway macrophages could be a marker of disease progression and poor survival in ILDs. Interstitial Lung Disease (ILD) is a broad term currently used to include more than 200 different disease entities 1). Connective tissue associated ILD (CTD-ILD) is the most common subtype, whilst idiopathic ILDs lie within the orphan characterization of rare lung diseases. Amongst idiopathic interstitial pneumonias, the most characterized and well-studied is IPF, a lethal chronic disease . Connect. A subseCommonalities in clinical behaviour in IPF and non-IPF ILDs with the PFP suggest common underlying pathogenetic pathways that drive progression, irrespective of the initial trigger , 7. GiveThe role of the innate and adaptive immune systems in the pathogenesis of IPF is not clearly defined  and immuMacrophages were long believed not to produce collagens. However there is cumulative evidence that they express almost all known collagen mRNAs , 21. FurSPP1 gene that encodes for osteopontin (OPN) in fibrotic lungs  were recruited in the Department of Respiratory Medicine, University Hospital of Heraklion, Crete, Greece from May 2012 to December 2018. 65 subjects all with ILD, were recruited in the Royal Brompton Hospital from March 2003 to October 2009.Patient characteristics are summarised in The diagnosis of IPF was based on ATS/ERS criteria or on multidisciplinary discussion according to the Fleischer criteria , 37. PatThis category of patients included patients with a known or new diagnosis of Connective tissue disease (CTD-ILD); namely Systemic Sclerosis (SSc), Rheumatoid arthritis (RA), Systemic Lupus erythematous (SLE) or Undifferentiated CTD. Patients with CTD-ILD were enrolled at the initial stages of there ILD diagnosis. Additionally, included patients with Idiopathic Non-Specific Pneumonia (NSIP), Hypersensitivity Pneumonitis (HP) and organizing pneumonia/NSIP overlap (OP/NSIP).Control subjects were undergoing bronchoscopy for the investigation of haemoptysis, without any overt pulmonary comorbidities and with normal bronchoscopy findings and cytology results. Since controls were healthy subjects, no PFTs were performed.All patients were evaluated with complete pulmonary function tests (PFTs) within one month of bronchoscopy. Lung volumes were measured using body plethysmography and the diffusion capacity  was measured using the single breath technique. The computerized system  was used and predicted values were obtained from the standardized lung function testing of the European Coal and Steel Community, Luxembourg (1993).Patients were classified as non-smokers, current smokers or former smokers .All patients provided written informed consent. The study was approved by the Ethics Committees of the University Hospital of Heraklion (IRB number: 1045 and 17030) and the Royal Brompton Hospital (REC reference 13/LO/0857).BAL fluid was obtained at room temperature. A flexible bronchoscope was wedged into a sub-segmental bronchus of a predetermined region of interest based on radiographical findings. A BAL technique was performed by instilling 180 ml of normal saline in 60-mL aliquots, retrieved by low suction. BAL samples were kept on ice and processed within two hours of collection. Samples were filtered through sterile 70nm cell strainers (BD) and centrifuged at 500g for 5 minutes at 4°C. Cell pellets were re-suspended with cold PBS. Total cell count and cell viability were assessed using Trypan blue (ICN). Differential cell population count was analysed following May-Grunewald-Giemsa staining .Mice were bred under SPF conditions at the local animal facility at “20–22°C, 55 ± 5% humidity, and a 12-h light/dark cycle; water and food were given ad libitum”. All experimentation in mice, in line with the ARRIVE guidelines, was approved by the Veterinary service and Fishery Department of the local governmental prefecture (#2816), following the positive opinion of the Institutional Protocol Evaluation Committee of BSRC Alexander Fleming.via the oropharyngeal (OA) route and bronchoalveolar Lavage Fluid (BALF) was collected as previously described (Pulmonary fibrosis was induced through the administration of 0.8U/Kg of bleomycin (Nippon Kayaku) to anesthetized mice  escribed . In brieQuantitative real-time polymerase chain reaction (QRT-PCR) was performed using SoFAst EvaGreen Supermix on a Bio-Rad CFX96 Touch™ Real-Time PCR Detection System .Values were normalized to β2-microglobulin (B2M).http://www.universalprobelibrary.com. COL1A1: Fwd:5’GGGATTCCCTGGACCTAAAG 3’, Rev: 5’ GGAACACCTCGCTCTCCA 3’, SPP1: Fwd: 5’ GGGCTTGGTTGTCAGCAG 3’, Rev: 5’TGCAATTCTCATGGTAGTGAGTTT 3’, GAPDH: Fwd: 5’AGCCACATCGCTCAGACAC3’, Rev: 5’GCCCAATACGACCAAATCC 3’. Primer pairs correspond to gene specific assays #67, 63, and 60 respectively. GAPDH levels were used as an endogenous control for the normalization of mRNA expression levels in BAL samples. Gene expression analysis was performed following incorporation of relative expression values in average (duplicates) normalized by GAPDH. Relative expression values for the patient cohort were calculated by 2-ΔΔCt method, where ΔΔCt =(sample Ct GOI-sample Ct GAPDH)-, GOI= gene of interest, calibrator= mean of all Cts. Relative gene expressions were log transformed.1-1.5 million cells were centrifuged and cell pellets were homogenised in TriReagentTM(MBL) for total RNA, followed by storage at -80°C. Total RNA was isolated as previously described . Oligos Mouse Q-Real-time PCR was performed on a BioRad CFX96 TouchReal-Time PCR Detection System . For the detection of collagen 1α1 transcript we used the following primers: 5’-CTACTACCGGGCCGATGATG-3’ (F) & 5’-CGATCCAGTACTCTCCGCTC-3’ (R). Values were normalized to the expression of b-2 microglobulin .2 for 30min, with washes to remove non-adherent cells, such as lymphocytes and eosinophils. In order to avoid changes in gene expression caused by the contact of cells with rigid surfaces that may lead to detectable altered protein levels of COL1A1 or OPN, cells were cultured for a short period of thirty minutes. Cells were fixed with 4% formaldehyde (FA) for 20 min at RT and were stored overnight in 1% FA.0.5 million freshly isolated BAL cells were cultured in DMEM, high glucose, with stable glutamine and sodium pyruvate (Biosera) supplemented with 2% Fetal Calf Serum (FCS) (Biosera) and 1x concentration of penicillin-streptomycin  in a humidified incubator at 37°C containing 5% COFA fixed cells were washed three time with PBS and permeabilization buffer  was added for 10 minutes followed by antibody blocking buffer  for 10 minutes at RT. Primary antibody incubations were carried out for 60 minutes at room temperature in antibody blocking buffer followed by washing with TBS. Secondary antibody incubations were performed for 30 minutes followed by washing with TBS. ToPro-633 or DAPI was added for nuclear staining. Finally, TrueBlack (Biotium) was added for 30 seconds to eliminate autofluorescence signal. COL1A1 was detected with rabbit anti-Col1a1 antibody  at final concentration 5μg/ml, CD45 with mouse anti-CD45  diluted 1/100 times, CD163 with mouse anti-CD163  at final concentration 3.5μg/ml, osteopontin was detected with mouse anti-OPN  at final concentration 3μg/ml. Secondary antibodies were: for CD45 and CD163 staining, anti-Mouse Cy3 , for OPN staining of fresh BAL cultures Alexa-Anti-mouse 633 (A-21126 Thermo Scientific) or Alexa anti-mouse 488 and for COL1A1, Alexa-anti-rabbit 488 . All secondary antibodies were used in 1/500 dilution. Secondary antibody controls were also performed with no background fluorescence. Images were taken by Sp2 Leica confocal microscopy with 63x Plan-Apochromat oil lens. Mean intensity of protein levels/cell were calculated with ImageJ v2.0.0-rc-69/1.52i as follows, photomicrographs, with identical acquisition settings, were adjusted for background, AMs were contoured and mean pixel signal intensity was measured for each cell. 5-8 photomicrographs, depending on cell density and an average of 100 cells per patient sample were measured.Data were analysed using Prism 8 (Graph Pad) software. Gene expression comparisons between two groups of a normal distribution were performed with Welch’s T test, when sample numbers between the populations were unequal or a t-test when population numbers were similar When populations were of a not normal distributions Mann-Whitney test was performed. Gene expression comparisons between more than two groups were performed with one-way Anova and multiple comparisons were performed with Two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli recommended in Prism 8 software. Comparisons of intracellular protein levels/cell obtained by immunofluorescence between the groups were performed with Kruskal-Wallis test and multiple comparisons were performed with Two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli recommended in Prism 8 software.p value less than 0.05 was considered statistically significant .Survival was analysed using SPSS 25 (IBM) software. Survival analysis was performed using Cox proportional hazard analysis. We used receiver operating characteristics (ROC) curve analysis to select a cut point for COL1A1 that predicted progression and generated hazard ratios (HRs) to compare patients assigned to groups based on the optimal cut-point. A We have previously observed that mRNA levels of COL1A1 are detectable in BAL cells and are elevated in IPF patients relative to controls , 25. WhoWe also measured COL1A1 expression by RT-PCR in BAL cells from bleomycin treated mice and found that COL1A1 mRNA was induced by the bleomycin treatment , showed COL1A1 expression in the BAL was upregulated during fibrosis in both humans and animal models. We therefore sought to examine whether COL1A1 upregulation was specific to IPF or was a characteristic of Non-IPF ILD patients as well. Whole BAL cell mRNA analysis showed that COL1A1 mRNA levels were higher not only in IPF but showed a similar trend in other ILD diagnostic subgroups, collectively compared to controls  A. WithiNext, we examined the expression of COL1A1 according to fibrosis pattern on CT, and found that in the whole ILD cohort a UIP pattern was associated with higher COL1A1 levels compared to all other patterns combined  B. ImporThe cellular source of COL1A1 in the BAL was examined by immunofluorescence in freshly isolated AMs from BAL of IPF and non IPF f-ILD patients. COL1A1 staining demonstrated a characteristic ER localization, illustrated by a membranous web in the cells Figure 2Furthermore, COL1A1 protein levels were semi-quantified by immunofluorescence in formalin fixed whole BAL cell cytospins from samples of IPF, N-IPF and healthy subjects. As shown in SPP1 gene encodes for the cytokine OPN that regulates COL1A1 expression and independent recent studies have shown an increase in a population of pulmonary macrophages expressing SPP1 mRNA in IPF and other ILDs . In IPF, SPP1 levels although elevated, did not correlate with COL1A1. However, co-expression of OPN and COL1A1 could be observed in AMs in IPF and N-IPF BAL cultures as shown in We subsequently tested possible associations of increased COL1A1 and SPP1 expression with disease severity in IPF and non-IPF patients. In the subgroup of patients with available serial PFTs (n=66), higher COL1A1 relative mRNA expression was associated with 12 month-FVC trends in the whole ILD population . Subgroup analysis revealed that FVC decline was largely associated with higher COL1A1 mRNA in non-IPF patients (n=36) , but not in IPF (n=33)  A–C.WhenCOL1A1 as a continuous variable was linked to higher mortality risk in ILDs , independently of age or disease severity at baseline, CPI (marker of severity) and UIP  . Subgroup analysis of the IPF and N-IPF groups showed that COL1A1 was mostly associated with mortality in the non-IPF cohort with a HR ratio of 1.4 . After adjustment for age, gender, CPI and UIP pattern, COL1A1 remained independently associated with worse survival  in Non-IPF patients. This was not observed in IPF .ROC curve-defined cut-off showed that a relative mRNA expression of log 0.5382 was associated with 1.8 times worse risk of mortality in all ILDs . This remained significant after adjusting for age  or CPI , but not UIP . Subgroup analysis revealed that the COL1A1 expression above this cut-off was associated with 2.2 times increased risk of death in the non-IPF ILD cohort  but not in IPF . In the Non-IPF group this significant association was independent of age, CPI and UIP . Kaplan Meier curves for the patients with collagen expression above and below the cut-off are shown in SPP1 mRNA expression by airway macrophages in unadjusted analysis was not associated with survival in either group and after adjustment for age, CPI and UIP pattern, SPP1 associate with better outcomes in IPF  but not in the Non-IPF group .In this study, we show that alveolar macrophages express collagen 1a1 and this is associated with worse outcomes in ILDs. We examined a cohort of 115 ILD patients and found that the expression of COL1A1 increased in IPF and other progressive ILDs. A UIP pattern of fibrosis was characterised by higher COL1A1 expression in all ILDs and the higher expression was largely associated with worse survival in Non-IPF ILDs. COL1A1 and OPN were abundantly co-expressed in airway macrophages from all ILDs and were significantly higher relative to healthy individuals.For years, IPF has been considered a distinct disease, of unknown cause, characterised by a UIP pattern of fibrosis and progressive fibrosis leading to dismal prognosis . New eviBiomarkers that could predict progression in fibrotic ILD patients include UIP pattern on CT across different entities such as CTD-ILD , 49, hypAlthough the expression of collagens by macrophages may be considered controversial it has been previously demonstrated in animal models. The COL1A1 expression atlas of fibrotic mouse lungs demonstrated that cells with hematopoietic marker CTPRC (encoding for CD45 antigen), CCR2 and CD68 macrophage markers expressed COL1A1 . AdditioIn order to exclude that this higher COL1A1 expression in the BAL might represent the presence of fibroblasts, we showed that COL1A1 was intracellularly expressed in airway macrophages with immunofluorescence and confocal microscopy. Cells with macrophage morphology isolated from BAL and positively stained for CD45 or CD163, were shown to contain high levels of COL1A1. CD163 positive macrophages accumulate in the tissue of fibrotic patients as previously shown  and in tvβ3 engagement and activation of the phosphoinositide 3-kinase/phosphorylated Akt/nuclear factor kappa B (PI3K/pAkt/NFκB)–signalling pathway at least in hepatic stellate cells . Interestingly SPP1 levels correlated with COL1A1 expression in the Non-IPF ILDs but not within the IPF group suggesting a possible uncoupling of collagen expression from OPN in the IPF macrophages. Additionally, we observed that SPP1 mRNA was elevated in the BAL of most ILDs tested, irrespective of disease severity and CT patterns. Intriguingly, increased levels of SPP1 were associated with more favorable outcomes in IPF, in contrast to the rest of the ILDs. Circulating OPN was recently associated with immune complexes-driven profibrotic macrophage activity in SSc-ILD  research award 2018 to ET and KA.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.


Twisted bilayer graphene is a fascinating system due to the possibility of tuning the electronic and optical properties by controlling the twisting angle  The interest in the study of TBGs was recently revived after the observation of superconductivity in samples with 6. In this so-called magic angle, the interaction between the cones generates flat electronic bands with energy width of few meV and, in this regime, the electron–electron interactions can dominate the many body interactions and give rise to correlated phenomena such as the superconductivity.The superposition of adjacent layers in twisted bilayer graphene (TBG) generates a Moiré pattern superlattice whose size depends on the twisting angle 16. For monolayer graphene, only one optical phonon mode is active in the first-order Raman spectrum and it gives rise to the G band around 1580 19 and (ii) the appearance of extra peaks in the spectra associated with phonons in the interior of Brillouin zone (BZ) of monolayer graphene that are activated by the Moiré pattern in the spectra of TBGs26. Other Raman bands are also observed in graphene systems due to the double-resonance (DR) mechanism, such as the 2D band and the disorder-induced D band, being the dependence of the band positions and intensities on the laser excitation energy their main characteristics27.Raman spectroscopy is a powerful and extremely useful technique to characterize any kind of graphene related system20 several works in the literature have also reported the appearance of new peaks in the Raman spectra of TBGs, and have shown that their frequencies depend on the twisting angle 26. These peaks come from phonons within the interior of the Brillouin zone (BZ) of monolayer graphene that have the wavevector 28. In one case, the enhancement of these peaks occurs in the same range of excitation energies where the G band is hugely enhanced, and, in the other case, the enhancement occurs for excitation laser energies where the G band is not enhanced. These results were explained considering the existence of two resonance mechanisms: the intralayer and interlayer electron–phonon (el–ph) interaction processes. The intralayer process involves electronic scattering between states in the Dirac cone of the same layer and in the interlayer case the scattering occurs between states from different layers28.Since the first report by Gupta et al. in 201027 by imposing the momentum conservation for the intralayer and interlayer el–ph processes. We also show that the positions of the Moiré peaks from the acoustic phonon branches can be used to measure accurately the twisting angle In the present work, we study experimentally and theoretically the enhancement of the Moiré peaks of TBGs coming from the acoustic and optical phonon branches of graphene by both the intralayer and the interlayer el–ph processes. We present experimental results obtained from many different TBG samples with small and intermediate twisting angles  Raman model for graphene and the specific momentum conservation conditions for the intralayer and interlayer electron–phonon processes. In graphene, phonons with 27. Additionally, it is known that the wave vectors in the BZ of graphene along the T. The electronic bands have either 32. Two excited states in the same valley have necessarily different symmetries. Pair of excited states with same symmetry  peak with respect to the LO (LA), while the red regions show the opposite. The intermediate situation, where the intensities of the iTO and LO (TA and LA) peaks are comparable to each other, is represented by the purple color. Figure In order to achieve a deeper understanding regarding the contribution of each phonon branch for the calculations shown in Fig. The Raman spectra of twisted bilayer graphene exhibit a number of extra peaks associated with phonons of the acoustic and optical branches of graphene with the wave vector of the Moiré pattern, which can be activated by both the intralayer and the interlayer electron–phonon processes. In this work, we studied how the intralayer and the interlayer el–ph processes enhance the intensity of each extra peak in the Raman spectrum of twisted bilayer graphene (TBG). CVD grown samples of TBGs with different twisting angles Peaks associated with all phonon branches of graphene were observed experimentally in the Raman spectra of TBG samples in resonance with the interlayer process. They can be as intense as the G band of single layer graphene, but much weaker than the TBG G band. Their positions are nicely described by the calculated dependence of phonon frequencies on 34 was used for excitation. The scattered light was collected through a Raman measurements of the TBG samples with different twisting angles 30. The graphene layers were first covered by a thin layer of polycarbonate, followed by etching in HCl aqueous solution to remove the Cu in the transfer process. The polycarbonate film with attached graphene was then transferred onto fused silica. Finally, the polycarbonate film was removed using chloroform.The TBG samples were grown by CVD technique using methane (99.99 per cent) on polycrystalline Cu foilsk-point grid over a graphene BZ and only phonon wave vectors q-point grid for all six phonon branches were used. Despite the restrictions associated to the interlayer and intralayer processes, all Raman calculations were performed following the procedures described in Ref.27.The double-resonance (DR) Raman intensities were computed in the fourth order perturbation theory. The eigenenergies and the eigenstates were obtained through a fifth neighbour tight-binding model fitted to a DFT + GW calculations, as well as the phonon dispersion used in all calculations. The electronic wave vectors were distributed in a


Air pollution is becoming an increasingly important global issue. Toxic gases such as ammonia, nitrogen dioxide, and volatile organic compounds (VOCs) like phenol are very common air pollutants. To date, various sensing methods have been proposed to detect these toxic gases. Researchers are trying their best to build sensors with the lowest detection limit, the highest sensitivity, and the best selectivity. As a 2D material, graphene is very sensitive to many gases and so can be used for gas sensors. Recent studies have shown that graphene with a 3D structure can increase the gas sensitivity of the sensors. The limit of detection (LOD) of the sensors can be upgraded from ppm level to several ppb level. In this review, the recent progress of the gas sensors based on 3D graphene frameworks in the detection of harmful gases is summarized and discussed. In manymmonia NH, and volmissions . Therefo animals . Additiocal smog ,7. Therey 53 ppb . Ammonia process ,10. Specy system ,12,13. Vy system . Aldehydy system ,15. Toxiy system ,17,18,19y system . It is cy system . Because2, NH3, CO2, and so on. Moreover, the conductivity of graphene will change after adsorption of target gas molecules. The concentration of target gas in the environment can be detected by monitoring the change of conductivity. There have been many reports on the application of graphene in gas sensors, including pure graphene [π-π accumulation and Van Der Waals force binding between graphene, the 2D graphene nanocomposites tend to agglomerate, resulting in the reduction of specific surface area [As a 2D material, graphene has many advantages, such as large conjugated structure, high specific surface area, high conductivity, easy to be synthesized, sensitive to the gas molecules, and so on. It has been proven to be a promising high-performance gas detection material . Graphengraphene ,24,25,26graphene ,29,30,31graphene ,33,34, cgraphene ,36,37, igraphene , testinggraphene ,40,41,42graphene ,45,46,47ace area ,49,50. Iace area . In receace area . It is dace area .2. However, the above reviews did not reflect the whole progress of graphene gas sensors, especially for the air pollution monitoring applications. This paper aims to summarize the recent progress of the gas sensors based on 3D graphene frameworks in the detection of air pollutants.Several reviews have presented the main development of graphene-based gas sensors. For example, in 2015, Meng et al.  reviewedGraphene oxide (GO) and reduced graphene oxide (RGO) have a 2D conjugated structure with single-atom thickness and residual oxygen-containing groups, which can be regarded as 2D conjugated macromolecules, structurally. They have rich chemical activities, which are helpful for 3D self-assembly through a series of chemical modification methods to regulate the interaction between the layers ,38.(1)Hydrothermal reduction, which is simple, fast, and free of impurities. At present, the commonly used hydrothermal method is to prepare RGO dispersion by hydrothermal treatment at 180 °C ,54,55.(2)Chemical reduction, which is beneficial for large-scale production, and various reducing agents can be selected ,63,64,65(3)Electrochemical reduction ,67,68. T(4)Vacuum filtration. A simple vacuum filtration method was developed to prepare RGO hydrogels with high conductivity, anisotropy, and responsive stimuli ,70.Graphene hydrogel is one of the major 3D assemblies. Chemically modified graphene (CMG) hydrogels prepared from GO or RGO can be used for large-scale production. As shown in In addition to the 3D self-assembly of graphene in a water system, the assembly of the graphene in an organic system can also be achieved by thermal solvent reduction ,72,73.Graphene aerogel composites are usually prepared by supercritical drying or freeze-drying of hydrogel precursors ,75. For 2 gas sensor with ppb detection limit is an important requirement for continuous environmental monitoring [2, and could not monitor NO2 gas below 100 ppb [2 [2. The response is generally given in the form of relative change of resistance (∆R/R0) or conductance (∆G/G0), which was tested at a set temperature and a set concentration of NO2. S3D and S2D are the sensitivities (responses per ppm) of 3D and 2D graphene, respectively. The graphene with a 3D structure can increase the gas sensitivity to one or two orders of magnitude higher. The LOD of the sensors can be upgraded from ppm level to several ppb level.The development of a highly selective NOnitoring  and earlnitoring . However 100 ppb . Therefo0 ppb [2 . Table 12 is an excellent gas sensing material due to its high surface/volume ratio and excellent electronic properties [2 nanoparticles tend to agglomerate, which limits their applications. Loading MoS2 on 3D RGO is a good choice [2 sensor based on 3D MoS2/RGO composites. The composites were prepared using a novel self-assembly and hydrothermal method. The mild synthesis process enables MoS2 to uniformly disperse on the 3D RGO framework. The agglomeration of MoS2 was significantly alleviated, resulting in excellent low-temperature sensing performance. 2/RGO sensor. The selectivity of the fabricated sensors was characterized using a variety of independent gases. The device shows higher sensing response towards N-based molecules  than the other gases . When the sensor is exposed to NO2 atmosphere, the minority charge carriers (electrons) in the MoS2/rGO composites will transfer to NO2 due to the strong electron negativity of NO2. Thus, the width and height of the heterojunction are reduced, leading to the increased conductivity. A small variation in barrier height and width caused by gas adsorption or desorption can have a significant influence on the resistance. A superior low-temperature NO2 sensing performance with a response of 2483% toward 10 ppm NO2 was achieved at 80 °C.2D-layered MoSoperties ,92,93,94d choice ,96,97. Cd choice  reportedp-type RGO, SnS2 is an n-type semiconductor with an indirect band gap of 2.2 ev [2 has a high affinity to NO2 because of its much weaker electronegativity than NO2 [2 is too high to be measured at room temperature, so SnS2 is not suitable for monitoring NO2 at room temperature. When SnS2 is coupled with 3D graphene, their properties are complementary to each other. Wu et al. [2/RGO heterojunction, which was synthesized through a facile hydrothermal route. 2/RGO sensor. The 3D structure enhances the adsorption and diffusion of small NO2 molecules. SnS2 can facilitate the electron transfer from RGO to NO2 by forming a heterojunction with RGO. The depletion layer with an electrostatic field at the p-n heterojunction region could promote the dissociation of NO2 and thus enhance the NO2 adsorption at low temperatures. Upon NO2 adsorption on SnS2, the electron will transfer from SnS2 to NO2, leading to the increase both of the electron-depletion region and the hole concentration of the p-type RGO. Thus, the resistance of SnS2/RGO is reduced. The sensor displays impressive NO2 sensing performance, including high sensitivity (6.1 ppm−1), low LOD (8.7 ppb), good linearity, and reversibility.Different from f 2.2 ev . SnS2 hathan NO2 ,100. Howu et al.  demonstrπ–π interaction, forming a 3D RGO/Eu(TPyP)(Pc) hybrid aerogel. Eu(TPyP)(Pc) not only acts as a sensor recognition unit, but also helps to enhance the amplification effect of the p-n heterojunction. At the same time, it provides enough space for the efficient transmission of RGO. The resulting aerogel not only effectively integrates the gas sensing of Eu(TPyP)(Pc) and good conductivity of RGO, but also exhibits a prominent synergy effect. Pure RGO sensors have poor selectivity, slow response, and long recovery time, which limits their wide applications . Chemica2 crystals on RGO [2 nanocrystals supported by the 3D mesoporous graphene aerogels to detect NO2 gas at low temperature [2-modified graphene hydrogel (SnO2/RGOH) via the one-step hydrothermal method. 3D SnO2/RGOH was synthesized directly from Sn2+ and GO precursors without any surfactant. The results show that it is feasible to optimize the gas sensing performance by combining reasonable material hybridization, 3D structure, and temperature modulation. The SnO2/RGO hybrid showed a high sensitivity to NO2 [2 nanoparticles and the formation of p-n heterojunction at the interface between SnO2 and RGO [p-n heterojunction formed at the interface of RGOH and SnO2 promotes the charge transfer. A micro-heater is integrated on the other side of the substrate to increase the substrate temperature locally, so as to suppress the interference of humidity in NO2 sensing. 2/RGOH sensor. When exposed to 0.5–5 ppm NO2, SnO2/RGOH showed an immediately increased conductivity.Many studies have been devoted to improving the gas sensitivity by immobilizing SnOs on RGO ,104. Li perature . Wu et aperature  reportedy to NO2 . The imp and RGO . The p-n2 sensor [3BO3) and dicyandiamide (C2H4N4). 2 is more than one order of magnitude higher than that of RGOH. It is worth noting that the response of B- and N-RGOH sensors varies almost linearly with the concentration of NO2.Recent studies have shown that doping is a feasible strategy to adjust the physical, electronic, and chemical properties of graphene by creating a band gap . Under t2 sensor ,113,114.2 sensor  reported3) sensor is indispensable in many industries and daily life. However, due to its complex preparation process, strict environmental requirements, and desorption of residual ammonia molecules, the production cost is high, which hinders the market acceptance [2 sensing, because NH3 is the reductive gas and NO2 is the oxidative gas [3 is mostly reported as inferior to that toward NO2, for example, in 2020, Wu et al. [3 and NO2 at room temperature. They found that the LOD of NH3 and NO2 were 500 and 100 ppb, respectively.The ammonia  conducting polymers ,124,125.3, with an outstanding LOD down to 50 ppb. The average response time is 30 s at room temperature. It is not sensitive to other gases and has good selectivity to NH3. The excellent sensing performance was attributed to 3D interconnected porous structure, remarkable enhancement of charge carriers, and modified π-interactions between molecules [CuO nanoparticles are suitable for the gas detection due to their narrow band gap and adjustable morphology ,131,132.olecules .p-n heterojunction aerogels as efficient 3D sensing frameworks for phenol detection. Upon the detection of phenol at room temperature, the sensor has good sensitivity, repeatability, and stability. The linear relationship is in the range of 10–80 ppb. Compared with the detection results of ethanol, toluene, and methanol, the gas sensor based on RGO/SnO2 composite aerogel has much higher sensitivity to phenol with the LOD down to 5 ppb. In the same year, Gao et al. [Phenol is the natural component of many substances and can be emitted from the combustion of fossil fuels and tobacco . It is ao et al.  reportedo et al.  reportedCompared with the 2D graphene nanosheets, the signal transduction of 3D graphene frameworks are more than ten times higher in most gas detection, due to the increase of the interaction surface area and the number of active adsorption sites . In addiIn addition, 3D graphene has a strong mechanical strength and a high temperature resistance, capable of using in harsh environments. These properties are very promising for practical application. It is a potential research direction to combine 3D graphene with flexible substrate to make wearable flexible sensors.x (main NO2), NH3, CO, CH2O, and phenol. Compared with traditional metal oxide sensing materials, graphene is superior due to the much lower operating temperature and much lower resistance. It requires a low energy consumption in operation [2, SnS2, organic compounds , and semiconductor metal oxides , the sensors based on 3D graphene composites could see an increase of the sensitivity.However, 3D graphene is still limited in wide applications due to its limited gas sensing types. The main toxic pollutants in the air are NOperation ,87,115. peration ,141,142.peration ,144. Com2 and NH3, and more gas sensors need to be discovered. The adsorption and desorption of 3D graphene need to be accelerated so as to reduce the response and recovery times of the sensor. Finally, 3D graphene sensors are still in the stage of laboratory investigation, and more work is needed to put forward these developments to the commercialization stage.At present, 3D graphene is mainly sensitive to NO


The chaperone Tsr2 releases Rps26 in the presence of high Na+ or pH in vitro and is required for Rps26 release in vivo. Moreover, Tsr2 stores free Rps26 and promotes reincorporation of the protein, thereby repairing the subunit after the Na+ stress subsides. Our data implicate a residue in Rps26 involved in Diamond Blackfan Anemia in mediating the effects of Na+. These data demonstrate how different ribosome populations can arise rapidly, without major energy input and without bypass of quality control mechanisms.Although ribosome assembly is quality controlled to maintain protein homeostasis, different ribosome populations have been described. How these form, especially under stress conditions that affect energy levels and stop the energy-intensive production of ribosomes, remains unknown. Here, we demonstrate how a physiologically relevant ribosome population arises during high Na Chaperone-mediated release and reincorporation of proteins from ribosomes enable a physiological translational stress response. Ribosomes synthesize proteins in all living organisms. Well over half of all transcription and translation events are dedicated to the synthesis of the 1000 to 2000 ribosomes/min, which are required to maintain ribosome populations  in mediating salt-dependent effects. In vivo and in vitro experiments show that Rps26 release is enabled by the chaperone Tsr2, which has been previously suggested to deliver Rps26 to ribosomes  stress. This led us to ask next how Rps26 dissociation occurs within cells. Tsr2 is a chaperone for Rps26, which stabilizes the protein outside of the ribosome , thereby separating it from released Tsr2-bound Rps26, which will remain in the supernatant. The data in S subunits. Release from the 40S subunit was specific for Rps26, as other ribosomal proteins were observed in the pellet fraction . Notably, while 500 mM KOAc led to Tsr2-induced release of Rps26, neither Tsr4 or Yar1 is able to release Rps2 or Rps3, respectively. Moreover, as expected, given the Tsr2 dependence for Rps26 release  pairs. Thus, we cloned, expressed, and purified Tsr4, the chaperone for Rps2  impairs binding to Rps26 .Above, we have shown that Tsr2 can release Rps26 from mature ribosomes in the presence of high salt in vitro. We next tested if Tsr2 was also involved in Rps26 release from ribosomes when yeast cells are stressed in vivo. If so, then we predict that the Rps26 released from preexisting ribosomes will be bound to Tsr2 under stress. To confirm this hypothesis, we redesigned the pulse-chase experiment by following preexisting Rps26-HA (hemagglutinin) bound to Tsr2-TAP. In this experiment, the expression of HA-tagged Rps26 is regulated by a galactose-inducible/glucose-repressible promoter (in the background of constitutive untagged Rps26). By switching the media to YP-glucose when NaRps26-HA . If Tsr2S subunits.The data in S binding by Tsr2 was required for Rps26 release. To test whether Tsr2 was required for the formation of Rps26-deficient ribosomes under Na+ stress in vivo, we compared the amount of Rps26 in stress-treated and untreated ribosomes when Tsr2 is present or absent in those cells. Tsr2 was depleted using a galactose-inducible/glucose-repressible strain grown in glucose for ~18 hours. As previously reported (S subunits had somewhat less Rps26 when grown in rich medium (fig. S3C). However, when the Tsr2-depleted ribosomes were exposed to high NaCl stress, the amount of Rps26 in the subunits was no longer reduced, in contrast to what was observed in the presence of Tsr2. Instead, without Tsr2, no change or even an increase in Rps26 occupancy in ribosomes is observed . Thus, the interaction between Rps26 and Tsr2 is required for Rps26 release in vivo.To provide evidence that Tsr2 promotes release of Rps26 and not just captures and then stabilizes Rps26 released from ribosomes in vivo, we carried out three sets of experiments: We tested (i) whether Tsr2 was required for the formation of Rps26-deficient ribosomes in vivo, (ii) whether Tsr2 bound to ribosomes, and (iii) whether 40observed . These ewt Rps26 . This anS ribosomes, as expected, if it releases Rps26 from these subunits, we repeated the Tsr2-TAP pulldown and washed the IgG beads with different salt concentrations after Tsr2-TAP had been bound . Rps26 remains bound to Tsr2, even under these higher Na+ conditions. Similarly, gradient centrifugation experiments also demonstrate that a fraction of Tsr2 interacts with 40S subunits alone, or when in 80S fractions or even polysomes (fig. S3A). Thus, Tsr2 binds 40S subunits.To test whether Tsr2 interacted with 40en bound . The conS subunits was required for release of Rps26, as predicted from a model where Tsr2 actively releases Rps26, as opposed to simply capturing released Rps26. For that purpose, we generated a Tsr2 mutant, where a positively charged patch in the protein was mutated to generate Tsr2_K/E . This mutant displays a slow growth phenotype (fig. S4A). Gradient centrifugation of yeast lysates demonstrates that Tsr2_K/E was unable to bind 40S subunits (fig. S4D), while both wt Tsr2 and Tsr2_DWI cosediment with the subunits . In contrast, the Tsr2_K/E mutant, but not the Tsr2_DWI mutant, retained the ability to bind Rps26 .Next, we tested whether the binding of Tsr2 to 40S subunits. This mutant was unable to release Rps26 from purified 40S subunits . Moreover, no increase in phosphorylation of Rps26 or Tsr2 was observed when phosphorylation of the total proteome was analyzed after NaCl treatment   and low concentrations of K+ (95 mM)]. This Mg2+ also contacts the rRNA backbone and could thus be a candidate ligand for mediating the salt-dependent effect. The D33N mutation leads to DBA , demonstrating the importance of the metal ion at that position for Rps26 binding. Because these ribosomes entirely lack Rps26, we could not directly test whether Rps26 release displayed a different response to high salt. Nonetheless, we note that there are two additional ions within less than 7 Å to the D33-bound ion (fig. S7A). It is conceivable that the differential occupation of these sites by Na+, K+, and Mg2+ is responsible for the observed salt effects on Rps26 release.How do ribosomes sense differences in salt and pH? Release assays at different Mgtructure . Crystal+, and Mg2+, how then do we explain the sorbitol-dependent release of Rps26? Mathematical modeling of cellular responses to different ionic changes predicts that sorbitol reduces the cellular volume by water extrusion, leading to an increase in intracellular Na+ concentration and effectively producing intracellular NaCl stress upon exposure to sorbitol  is above 8, implicating side chains such as lysines and histidines. While histidines can provide ligands to soft metals such as Zn in Zn-finger motifs, neither histidine nor lysine is a typical ligand for Mg ions, thus suggesting that the pH effects are mediated by different side chains than the salt effects, which possibly arise from D33 as discussed above. Moreover, our data indicate that the effects from pH and salt are additive, although the strength of this conclusion is limited because we cannot fully deprotonate the residue that is responsible for the pH dependence.Two lines of evidence also suggest that salt and pH act independently of each other, indicating that the pH-sensitive site is distinct from the salt-dependent site. First, the pKTranscriptional responses to remodel the proteome under stress are well characterized and occur rapidly (Saccharomyces cerevisiae strains used in this study were either purchased from the GE Dharmacon Yeast Knockout Collection or constructed using standard methods (Escherichia coli Rosetta2 (DE3) cells (Novagen) as tobacco etch virus (TEV)–cleavable His6-MBP  fusion proteins. Cells were grown at 37°C in LB (Luria-Bertani) medium supplemented with antibiotics. At OD600  of 0.4, protein expression was induced with 1 mM isopropyl-β-d-thiogalactopyranoside (IPTG) for 16 hours at 18°C. Proteins were purified using Ni-NTA resin (Qiagen) according to the manufacturer’s instructions. Eluted proteins were pooled and dialyzed overnight at 4°C into 50 mM tris (pH 7.4), 100 mM NaCl, and 1 mM dithiothreitol (DTT) with TEV protease. Tsr2, Tsr4, and Yar1 were further purified by MonoQ and Superdex 75 (GE) chromatography. For purification of the **Rps26·Tsr2 complex, the His-MBP tag was removed by a second round of purification with Ni-NTA resin, and the Rps26•Tsr2 complex was further purified by Superdex 75 with complex buffer [50 mM tris (pH 7.4) and 500 mM NaCl]. Concentrated proteins were stored at −80°C.Rps26, Tsr2, Tsr2_DWI, Tsr2_K/E, Tsr4, and Yar1 were expressed in 2O was performed with purified recombinant proteins using Zeba spin desalting columns (Thermo Fisher Scientific). Next acetonitrile and formic acid were added to final concentrations of 50 and 0.1%, and the protein was analyzed by electrospray ionization mass spectrometry. The spectra were analyzed by Q exactive (Thermo Fisher Scientific).To obtain the exact mass of purified recombinant Tsr2_WT and Tsr2_K/E, buffer exchange to dH2. The resulting powder was stored at −80°C until use. At that time, another cell pellet (1 ml/g) of the appropriate lysis buffer was added together with about 0.5 g of beads, and the mixture was thawed while rotating. Cell debris and beads were removed by centrifugation for 10 min at 3000g, and the supernatant was clarified by centrifugation for 10 min at 20,000g.All yeast cells were harvested, washed, and then resuspended in cell pellet (1 ml/g) of the appropriate lysis buffer. The suspension was frozen by dripping into liquid nitrogen to produce pearls. These were then grounded with mortar and pestle under liquid NOne liter of cells was grown to mid-log phase in YPD to produce ribosomes with Rps3-TAP, before shifting them to YPGal in the presence of dox (0.2 μg/ml), to shut off Rps3-TAP and induce untagged Rps3. At the same time, half of the cells were also treated with 1 M NaCl, and all cells were grown for four more hours. For sorbitol stress, cells were treated with 1 M sorbitol and grown for 30 additional minutes. To test the repair of Rps26-deficient ribosomes, cells were further grown in YPGal for 1 hour after stress in the presence of dox.2, 0.075% NP-40, 1 mM benzamidine, and 1 mM phenylmethylsulfonyl fluoride (PMSF)] three times before elution. Elution step was performed with ~2 hours of incubation in 16°C with IgG-binding buffer supplemented with TEV protease , 0.5 mM EDTA, and 1 mM DTT. Samples were further analyzed using Western blot.For TAP purification of the lysate, ~200 μl of prewashed IgG sepharose bead slurry (GE) was added to each lysate and incubated for ~2 hours at 4°C. After binding, each sample was washed with IgG-binding buffer  containing different concentrations of salt or magnesium. Samples were layered onto a 400-μl sucrose cushion [ribosome-binding buffer and 20% sucrose (w/v)] and spun for 2.5 hours at 400,000g at 4°C in a TLA 100.1 rotor (Beckman). Supernatants were precipitated using trichloroacetic acid and resuspended in the same volume as pellets. Total resuspended sample was loaded on SDS–polyacrylamide gel electrophoresis (SDS-PAGE) followed by Western blotting. For pH-dependent release assays, bis-tris propane was used to adjust the pH to the indicated value.Pelleting release assays were performed as previously described  per the manufacturer’s instructions, generating complementary DNA. Quantitative polymerase chain reaction (qPCR) was performed with Excella 2× SYBR master mix per the manufacturer’s instructions using primers listed in table S3.Transcriptional repression of dox-repressible Rps3-TAP was assessed by inoculating YPGal media [with or without dox (0.2 μg/ml)] with a preculture grown in YPD to mid-log phase. Ten milliliters of cells was harvested at different time points. Harvest cells were resuspended in 400 μl of TES  buffer, and total RNA was isolated by hot-phenol extraction. After RNA extraction, each sample was precipitated and resuspended in 50 μl of deoxyribonuclease (DNase) master mix  supplemented with cycloheximide (0.1 mg/ml), 1 mM benzamidine, 1 mM PMSF, and complete protease inhibitor cocktail (Roche). Cleared lysate was applied to 10 to 50% sucrose gradients, centrifuged in an SW41Ti rotor for 2 hours at 40,000 rpm, and then fractionated as in figs. S3A and S4 (B to D). Western blot was performed to probe proteins as indicated.pKK30528  containing YKK856 (Tsr2-TAP) cells were grown in galactose media to coexpress Rps26-HA in addition to endogenous Rps26. For experiments with YKK1109  containing Tsr2 variant plasmids, cells were grown in YPD media. At mid-log phase, cycloheximide (0.1 mg/ml) was directly added to culture, and cells were harvested by centrifugation. Cell pellet was further washed and lysed in gradient buffer  supplemented with heparin (1 mg/ml), 1 mM benzamidine, 1 mM PMSF, and complete protease inhibitor cocktail (Roche) was layered over 500 μl of sucrose cushion  and spun in a Beckman TLA 110 rotor at 70,000 rpm for 65 min. The resulting pellet was resuspended in high salt buffer , layered over 500 μl of sucrose cushion, and spun in a Beckman TLA 110 rotor at 100,000 rpm for 70 min. The flowing pellet was resuspended in ribosome storage buffer  and analyzed on SDS-PAGE followed by Western blotting.Three milliliters of each clarified lysate in ribosome buffer , and 1 mM puromycin (Sigma-Aldrich) was added. Subunits were isolated by loading onto 5 to 20% sucrose gradients  and centrifuged at 19,600 rpm for 16 hours. 40S subunits were collected, and buffer was exchanged into ribosome storage buffer during concentration.For further subunit separation, the ribosome pellet after the second centrifugation was resuspended in subunit separation buffer [50 mM Hepes/KOH (pH 7.4), 500 mM KCl, 2 mM MgClWestern blots were scanned using the ChemiDoc MP Imaging System from Bio-Rad after applying luminescence substrates (Invitrogen) and quantified using its built-in image laboratory software (v 6.0.1). Intensity of each band was analyzed after local background subtraction. To detect TAP-tagged (Rps3 or Tsr2) or HA-tagged proteins (Rps26 variants), anti-TEV cleavage site from Invitrogen (PA1-119) or anti-HA antibody from Abcam (ab18181) or Sigma-Aldrich (ab1603) was used, respectively. For Rps10 and Rps26 detection, antibodies were raised by New England Peptide. Polyclonal antibodies were gifts from V. Panse (Tsr2/Rps26), A. Link (Asc1), M. Seedorf (Rps3), and J. Warner (Rps2).