Cortico-basal-ganglia-thalamic (CBGT) networks are critical for adaptive decision-making, yet how changes to circuit-level properties impact cognitive algorithms remains unclear. Here we explore how dopaminergic plasticity at corticostriatal synapses alters competition between striatal pathways, impacting the evidence accumulation process during decision-making. Spike-timing dependent plasticity simulations showed that dopaminergic feedback based on rewards modified the ratio of direct and indirect corticostriatal weights within opposing action channels. Using the learned weight ratios in a full spiking CBGT network model, we simulated neural dynamics and decision outcomes in a reward-driven decision task and fit them with a drift diffusion model. Fits revealed that the rate of evidence accumulation varied with inter-channel differences in direct pathway activity while boundary height varied with overall indirect pathway activity. This multi-level modeling approach demonstrates how complementary learning and decision computations can emerge from corticostriatal plasticity. Cognitive process models such as reinforcement learning (RL) and the drift diffusion model (DDM) have helped to elucidate the basic algorithms underlying error-corrective learning and the evaluation of accumulating decision evidence leading up to a choice. While these relatively abstract models help to guide experimental and theoretical probes into associated phenomena, they remain uninformative about the actual physical mechanics by which learning and decision algorithms are carried out in a neurobiological substrate during adaptive choice behavior. Here we present an “upwards mapping” approach to bridging neural and cognitive models of value-based decision-making, showing how dopaminergic feedback alters the network-level dynamics of cortico-basal-ganglia-thalamic (CBGT) pathways during learning to bias behavioral choice towards more rewarding actions. By mapping “up” the levels of analysis, this approach yields specific predictions about aspects of neuronal activity that map to the quantities appearing in the cognitive decision-making framework. The flexibility of mammalian behavior showcases the dynamic range over which neural circuits can be modified by experience and the robustness of the emergent cognitive algorithms that guide goal-directed actions. Decades of research in cognitive science has independently detailed the algorithms of decision-making  and reiUnderstanding how the cognitive algorithms for adaptive decision-making emerge from the circuit-level dynamics of CBGT pathways requires a careful mapping across levels of analysis , from ciwithin-channel competition, competition between action channels may also contribute to the drift rate, such that greater differences between the activation of opposing direct pathways lead to faster evidence accumulation.Converging lines of evidence from human and non-human animal experiments have identified the striatum as playing a critical role in the process of accumulating evidence during decision-making , particuTo see how the state of corticostriatal synaptic weights can impact decision process parameters, we adopted an upwards mapping approach to modeling adaptive choice behavior across neural and cognitive levels of analysis . Using aOur goal was to fit a DDM to behavioral data from our model CBGT network under different corticostriatal synaptic weight schemes. The first step was to find parameter settings for these synaptic weights, which are known sites of plasticity associated with dopaminergic signals about reward outcomes , that coL) and right (R), was selected by the spiking of model striatal medium spiny neurons (MSNs). These model MSNs were grouped into action channels receiving inputs from distinct cortical sources .DL and DR) associated with the more highly rewarded action increased, (b) firing rates of the indirect pathway MSNs  remained quite similar ),ertainty , 23, 24,While our present findings provide key insights into the links between implementation mechanisms and cognitive algorithms during adaptive decision-making, they are constrained by the nature of the multi-level modeling approach itself. Our goal was to evaluate a specific hypothesis under the competing striatal pathways framework about the combined role of corticostriatal pathways in learning and decision-making, and our simulations demonstrate that strengthening corticostriatal synapses is one way that the brain can adjust striatal firing to shape the drift rate and accumulation threshold, promoting faster and more frequent selection of actions with a higher expected value. We do not presume, however, that the impacts of dopaminergic plasticity at corticostriatal synapses on striatal activity are singularly responsible for setting the drift rate during value-based decision-making. Indeed, the manipulation of a single CBGT parameter can have a compounding effect on the downstream network dynamics  that ultimately contributes to parametric changes at the cognitive level . Moreover, because the CBGT network has many more parameters than the DDM, many different properties of the CBGT network, aside from corticostriatial weights and measures of striatal activity, could potentially be manipulated to cause analogous behavioral patterns and inferred effects on the drift rate and boundary height parameters in the DDM. For instance, in contrast to the striatal iMSN modulation of boundary height observed in the current study, Ratcliff and Frank  found thL and R direct pathway corticostriatal weights were mirrored by the relative firing rates of L- and R-dMSNs, the iMSN firing rates expressed channel-specific differences, despite the invariance of the corticostriatal weights to iMSNs across conditions. Critically, the observed increase in iMSN firing disparity between the L and R channels in our simulations emerged due to the thalamostriatal feedback assumed in the CBGT network, where dMSN activation leads to disinhibition of the thalamus, thereby increasing excitatory feedback to both MSN subtypes within a given channel. Thus, by design, our model assumes that feedback connections from the thalamus should adhere to a channel-specific  topology. This assumption of focality should be examined by future neuroanatomical research. In addition, if the topological predictions of our model are confirmed, the simulations presented here make the second prediction that activating the thalamic cells during the decision window should counterintuitively increase iMSN, as well as dMSN, firing rates while at the same time increasing decision speeds. One important caveat to these predictions is that thalamostriatal feedback was not incorporated into the STDP simulations used to generate the post-learning corticostriatal weights of the full CBGT network in each reward condition. Thus, additional potentiation of both dMSN and iMSN populations arising from increased thalamic gain in the optimal channel could interact with dopaminergic feedback during the learning process and thus lead to alternative downstream effects. Indeed, little is known about the role that thalamostriatal inputs play during reinforcement learning, requiring future experimental data to address these questions. Third, our simulations showed that the difference in firing rates of the dMSNs across action channels modulated the rate of value-based evidence accumulation. According to our simulations, increasing the relative magnitude of dMSN activity in the R channel compared to the L channel, for example using optogenetic stimulation of dMSNs in the dorsolateral striatum, should speed and facilitate the selection of contralateral lever presses. Choice and RT data could then be fit with the DDM to determine if the behavioral effects of laterally-biased dMSN stimulation were best described by a change in the drift rate. Analogous experiments targeting iMSNs but without channel specificity could be used similarly to evaluate our prediction that overall iMSN activity level modulates DDM boundary height.Our simulations make several novel predictions for future experiments. First, while the learning-related changes in Here we characterize the effects of dopaminergic feedback on the competition between direct and indirect CBGT pathways and how this plasticity impacts the evaluation of evidence for alternative actions during value-based choice. Using simulated neural dynamics to generate behavioral data for fitting by the DDM and determining how measures of striatal activity influence this fit, we show how the rate of evidence accumulation and the decision boundary height are modulated by the direct and indirect pathways, respectively. This multi-level modeling approach affords a unique combination of biological plausibility and mechanistic interpretability, providing a rich set of testable predictions for guiding future experimental work at multiple levels of analysis.cortico-basal ganglia-thalamic (CBGT) network, comprising neurons and synaptic connections from the key cortical and subcortical areas within the CBGT computational loops that take sensory evidence from cortex and make a decision to select one of two available responses. We fit this data using the drift diffusion model (DDM), a cognitive model of decision-making that describes the accumulation-to-bound dynamics underlying the speed and accuracy of simple choice behavior  until time tr, resulting in an unbiased evidence accumulation process when z = a/2. In perceptual decision tasks, v reflects the signal-to-noise ratio of the stimulus. However, in a value-based decision task, v can be taken to reflect the difference between Q-values for the left and right actions. Thus, an increase (decrease) in QL − QR from 0 would correspond to a proportional increase (decrease) in v, leading to more rapid and frequent terminations of θ at the upper (lower) boundary a (0).The DDM assumes that evidence is stochastically accumulated as the log-likelihood ratio for two competing decision outcomes. Evidence is tracked by a single decision variable Fits of the DDM were performed using hierarchical DDM, an open source Python package for Bayesian estimation of DDM parameters , as well as an intercept term for that parameter, resulting in a total of four free parameters per selected DDM parameter or 8 free parameters altogether. For example, in a model where drift rate is estimated as function of the difference between dMSN firing rates in the left and right action channels, the drift rate on trial t is given by j, and Xj(t) is the observed difference in dMSN firing rates between action channels on trial t in condition j; see also Eqs   a, v, orSimulations of experimental scenarios and Drift diffusion model, respectively, with additional variability in the parameterization of CBGT connection strengths. The functional outputs of the spiking CBGT network depend on two critical parameters:To assess the robustness of our findings to alterations in the connection weights used in the CBGT network, we repeated the simulation and fitting procedures described in sections Pij: the probability that a cell in population i projects to a cell in population j,gij: the conductance strength associated with inputs from cells in population i to cells in population j.ωij) of connections from population i to population j. The core simulations in this work were performed using a single ωij for each connection that was held constant for all simulated trials. We also introduced variability in ωij in additional simulations to explore robustness of results to variability in the underlying network connectivity.The product of these two values can be taken to represent the weight (k (N = 15), g(k)ij was independently sampled from a normal distribution with mean μij was set equal to the value of gij in i to population j in subject network k was given by the equation ω(k)ij = Pij ⋅ g(k)ij.For our robustness check, rather than simulating 2500 trials per condition with a single set of CBGT connectivity parameters, we generated multiple “subject” networks, simulating 200 trials per condition with each. For each subject L, optimal) and right  action channels were determined by scaling the baseline value of gCtx-MSN by the proportional change in synaptic strength ϕ observed in the STDP simulations in reward condition C  is sampled synaptic efficacy for subject network k, and ϕ(C) is the proportional change due to DA-mediated STDP in condition C indicated in As in the original single network simulations, the dopaminergic effects on synaptic strengths of corticostriatal inputs to left  iTo confirm that variability in the underlying connection weights of the network did not alter the upwards mapping between CBGT and DDM parameters, all single and dual static parameter models and regression models I-XII were re-fit to the data generated by the sampled networks. In contrast to the original model fits, which were performed on data from a single network, model fits to the sampled network data were performed hierarchically. Hierarchical model fits allow for simultaneous and conditional estimation of parameters at the individual and group levels. At the individual level, samples were drawn from the joint posterior distribution given each individual subject’s data. At the group level, these individual subject samples were aggregated to derive estimates of the mean and variance for each parameter. Finally, these estimates of group-level summary statistics served as prior distributions at the subject-level, reducing the weight of outlier subjects that deviate significantly from the group.All single and dual parameter models were defined in the same way as those fit to the original network, but with all free parameters estimated for each individual network and with corresponding estimates of the mean and variance of each parameter at the group level. Regression DDM fits were also performed hierarchically, with a random intercept for drift rate and boundary-height estimated for each individual subject network, and with regression weights estimated as fixed effects .S1 FigL (solid black); dMSN selecting action R (solid red); iMSN countering action L (dashed black); iMSN countering action R (dashed red). B: Averaged evolution of the action values QL (black trace) and QR (red trace) over 7 different realizations. C: Average neuronal firing rate (spike count within a time bin divided by bin duration) across neurons in the dMSN population selecting action L (black) and R (red), respectively, over time. D: Firing rates of the iMSN populations countering actions L (black) and R (red) over time. Data in C,D was discretized into 50 ms bins. The transparent regions depict standard deviations.A: Averaged weights over 7 different realizations and over each of the four specific populations of neurons, which are dMSN selecting action (TIF)Click here for additional data file.S2 FigTimecourse of average firing rates of dMSN (upper) and iMSN (lower) populations in left (black) and right (red) action channels are shown for low (left), medium (middle), and high (right) reward conditions.(TIF)Click here for additional data file.S3 FigDL − DR (left) and DL − IL (center-left); RT as a function of IL − IR (center-right) and Iall (right). Each dot represents data from an individual trial in the low (blue), medium (cyan), and high (yellow) reward conditions. Logistic  and linear  regression predictions for each condition are shown as lines. B. The same data as shown in panel A from multiple (N = 15) randomly sampled CBGT networks.A. Simulated decision outcomes  and RTs  from the original CBGT network are plotted as a function of the four striatal summary statistics used as predictors of trialwise changes in DDM parameters. Choice outcome is plotted as a function of direct pathway measures, (TIF)Click here for additional data file.


Post-translational modifications (PTMs) are fundamental traits of protein functionality and their study has been addressed using several approaches over the past years. However, screening methods developed to detect regulators of PTMs imply many challenges and are usually based on expensive techniques. Herein, we described the development and optimization of a western blot-based platform for identification of regulators of a specific PTM—mono-ubiquitylation of proliferating cell nuclear antigen (PCNA). This cell-based method does not require specific equipment, apart from the basic western blot (WB) devices and minor accessories, which are accessible for most research labs. The modifications introduced to the classical WB protocol allow the performance of PTM analysis from a single well of a 96-well plate with minimal sample manipulation and low intra- and inter-plate variability, making this method ideal to screen arrayed compound libraries in a 96-well format. As such, our experimental pipeline provides the proof of concept to design small screenings of PTM regulators by improving the quantitative accuracy and throughput capacity of classical western blots. Post-translational modifications (PTMs) are key marks of proteins that are introduced after their biosynthesis and regulate localization, activity and final functionality. Common modifications are phosphorylation, glycosylation, methylation, acetylation, amidation, ubiquitylation, carboxylation, disulfide bond, hydroxylation, nitrosylation and sumoylation. PTMs can also control protein–protein interactions and are thus able to modulate not only the function of the protein but also its interacting partners . As an aA variety of experimental methods have been used to detect PTMs, such as mass-spectrometry (MS), reverse phase protein arrays (RPA) and western blot (WB) ,6,7. MS-Sample complexity is a general disadvantage in all the previously mentioned methodologies. MS-based methods fraction the peptides to reduce this issue before the LC-MS/MS, but this technique decreases sample throughput and increases costs. MS-based methods can detect and quantify with high precision several PTMs, while certain classes of modifications, such as glycosylation with large glycans and nucleotide modifications, can be challenging to detect using this technique. Other limitations to be considered in MS are the abundance of substrate proteins, the co-occurrence of different or equal PTMs in the same protein and the associated stability due to the partial protein decomposition during the analysis . RegardiEven though many aspects of the aforementioned methodologies have significantly improved in the last years, the design of high-throughput screenings (HTS) to identify modulators of PTM still lags behind. Furthermore, several challenges arise in relation to the feasibility to perform screenings for the complex repertoire of PTMs. More importantly, such experimental designs are expensive and unaffordable for many members of the research community, especially in low- and middle-income countries. Most HTS performed to identify PTM regulators are conducted using in vitro-based assays, such as the AlphaScreen technology, Förster resonance energy transfer (FRET)-based platforms and quenching resonance energy transfer (QRET) ,11. ThesA different approach that is increasingly used by researchers is the in situ proximity ligation assay (PLA) reported by Leuchowius et al. . Their gThe outstanding variety of PTMs poses a methodological and technological challenge to studies aimed at identifying their potential regulators. Such a limitation is particularly relevant for those PTMs for which the only types of available antibodies are against the denaturized form of the protein. In such cases, researchers still need to rely upon classic methods such as WB or dot-blot, which impose serious limitations to the throughput capacity of the screenings. Herein, we describe the development and optimization of a western blot-based screening platform for the study of a specific PTM—mono-ubiquitylation of proliferating cell nuclear antigen (PCNA). This platform can easily be adapted to the evaluation of other PTMs and presents a number of advantages. It does not require specific equipment apart from the standard WB devices available in every laboratory, and it involves a single step preparation of samples, requiring only the sample from a single well from a 96-well plate. Together, such modifications of the WB technology allow a significant increase in the throughput capacity and robustness of the method. The importance of this work lies on the several methodological modifications that we introduced to the classical WB protocol, which should help other research groups that wish to establish small screening projects with arrayed compound libraries.The primary antibodies used were α-ubiquityl-PCNA  and α-PCNA . The secondary antibodies used were goat α-mouse IRDye 680RD (Cat# P/N 925-68070) and goat α-rabbit IRDye 800CW (Cat# P/N 925-32211) from LI-COR Biosciences.2 was performed as previously described [U2OS were acquired from ATCC and cultured in DMEM medium  supplemented with 5% FBS (GIBCO). Global UV irradiation with 15J/mFor the experiments, cells were first washed with PBS, incubated with trypsin  at 37 °C for 2 min and then harvested in DMEM medium with 5% FBS. Cells were then manually counted and plated by manual pipette or liquid handling pipette.For direct western blot analysis, samples were washed once with PBS, resuspended and lysed in one single step in commercial Laemmli buffer  containing the reducing agent 2-mercaptoethanol  and benzonase nuclease . No protease inhibitors are used for lysis. For experiments of proof of concept, cells in a 96-multiwell plate were washed once with PBS and then lysed directly in the plate with 30 µL of commercial Laemmli buffer with benzonase per well. Gels at 12% acrylamide:bisacrylamide (29:1) were casted using a mini-protean 3 multi-casting chamber (BioRad). Samples were loaded into gels directly from the 96-multiwell plate and resolved in a mini-protean tetra vertical electrophoresis cell (BioRad) and transferred to PVDF membranes (Roche). Transfer was performed using a semidry system from BioRad. Membrane blocking was performed with a solution containing tris-buffered saline (TBS), Tween 20 at 0.1% and 5% non-fat milk. Primary antibodies were used at a concentration of 1:1000 (α-ubiquityl-PCNA) and 1:2000 (PCNA). Secondary antibodies were used at a concentration of 1:10000. Antibodies were diluted in TBS-Tween at 0.1%.The detection and quantification of the near-infrared fluorescence on the membranes was performed with an Odyssey CLx System (LI-COR Biosciences) through the Image Studio Software. Images were acquired on auto intensity at high resolution with both channels (700 and 800) simultaneously.Cells seeded in a 96-multiwell plate were imaged automatically in a Leica DMI8 microscope. Parameters set for the automatic capture are as follows: fixed starting z-position of 4130; autofocus with 15 captures of 200 µm apart from each other and then selection of the best image according to contrast. All samples were plated as duplicate or triplicate. All experiments were performed two or three times. Statistical analysis and plotting were performed using GraphPad Prism 5.0 .The main goal of this project was to set up a low- to medium-throughput screening (MTS) assay aimed at identifying regulators of PTMs using the technology available in standard academic laboratories. While we focused our screening on a single PTM modification, our method and troubleshooting experience can be used as a tutorial to study any PTM on any protein. We describe the optimization of the method and the challenges we faced while setting up the WB-based MTS assay in our laboratory. Our aim was to identify inhibitors of the mono-ubiquitylation of PCNA (ubi-PCNA), a PTM that plays an important role in translesion DNA synthesis (TLS). It was key for the success of this set-up to count with an antibody that specifically detects the mono-ubiquitylated form of PCNA and another that detects total PCNA (modified and unmodified). As an initial approach, we evaluated the possibility to perform in-cell western blot (IC-WB), because of the advantages that this technology has in terms of robustness and throughput capacity for screening purposes. Such a technique can be summarized in the following few steps: (A) seeding of cells in a 96-multiwell format; (B) treatment of cells with potential PTM inhibitors; (1) fixation with PFA; (2) permeabilization with methanol; (3) blocking; (4) probing with primary and (5) secondary antibodies . The anaThen, the next logical alternative was to assess the enzyme-linked immunoabsorbent assay (ELISA) technology, which is another antibody-based method developed to detect an antigen in a sample in both a quantitative and qualitative manner. The basic ELISA protocol involves the following steps: (A) seeding of cells in a 96-multiwell format; (B) treatment of cells with potential PTM inhibitors; (1) immobilization of the primary antibody on a solid support, e.g., a 96-multiwell plate, and wash of unbound antibodies; (2) addition of a soluble protein sample, e.g., a lysate obtained from cells and washing cycles; (3) addition of a primary antibody against the PTM and a wash of unbound primary antibodies; (4) incubation with a secondary antibody and a final wash of unbound secondary antibodies B. The deThe follow-up alternative to consider was the dot-blot assay, a simple methodology that at many steps resembles the classical WB. Briefly, dot-blot consist in the following steps: (A) seeding of cells in a multiwell format; (B) treatment of cells with potential PTM inhibitors; (1) cell lysis; (2) immobilization of proteins on a PVDF membrane; (3) blocking with bovine serum albumin (BSA) or other protein-rich solution; (4) probing with single primary and (5) secondary antibodies B. The anLastly, we explored the classical western blot (WB) as a possible method for the screening of inhibitors of ubi-PCNA. WB is a technique regularly used for the analysis of proteins. It consists of three main steps: gel electrophoresis, membrane transfer and blotting and probing with antibodies B. While Serratia marcescens that degrades all forms of DNA and RNA [Two main issues were tackled in this optimization:reaching the smallest sample size (in our case a 96-multiwell (MW) plate) and reducing the sample manipulation in the screening plates . The WB  and RNA ,17. ThisA second limitation of WB is the poor throughput associated with the limited number of gels that can be casted at the same time and the comparability between gels (length of stacking and separating gels). To reduce this variability, we recommend the use of a multicasting chamber such as the one offered by BioRad, which allows the casting of up to 12 gels simultaneously, increasing reproducibility . As we wAs a final consideration, at every possible step, such as cell seeding, media removal and sample buffer addition, we recommend automation. For example, we found out that employing electronic liquid handling pipettes and a VIAFLO ASSIST pipetting arm remarkably reduced variability. By using the set-up described above, we reached a final screening capacity of 80 compounds tested within a single experiment conducted in a 96-multiwell format .After optimization of the selected WB-based approach, we pursued the development of quality controls to monitor cell number, intra-well distribution of cells and edge effects prior to launching the actual screening experiments. To this end, we established a U2OS cell line expressing infrared fluorescent protein (iRFP) with the transfection of an iRFP-C1 plasmid and performed three sequential rounds of sorting. Such a number of rounds were required to ensure a population with high and homogenous expression of the plasmid A. Given As mentioned previously, a great source of variability in the final cell number was encountered when evaluating different methods to seed the cells. When a manual system for sample manipulation was used, the final cell number measured in A.U associated with iRFP-C1 emission was notably dispersed, even when cells were seeded with high quality mechanical pipettes. On the contrary, when an electronic liquid handling system such as Integra Viaflow Assist was employed, the variability was minimized C. Such aSince we were also interested in assessing the effect of each compound both on the PTM and on the cellular fitness, we incorporated an extra step in the screening protocol that allowed us to evaluate the general toxicity of each compound. To do so, we developed an automatic capture program for bright field images, taking one picture of each 96-well of the plate prior to sample lysis with a Leica DMI8 microscope equipped with a motorized stage. This allowed the identification of compounds which were either severely toxic at the concentration tested or that somehow affected cellular integrity D.Our proof-of-concept screening using this platform with multiple replicas of non-irradiated and UV-irradiated conditions showed that the coefficient variance (CV%) is acceptable and lies below the one observed for a routine WB . In this work we have approached the major limitations and challenges to study modulators of PTMs in a screening setup within an academic laboratory, with a focus on economically accessible methodologies adapted to available antibodies that detect the PTM of interest. Our conclusion is that when antibodies detect the PTMs in their native confirmation, methods such as in-cell western (ICW) provide an excellent compromise between sensitivity range, loading control capabilities, throughput capacity and small costs for small academic labs . HoweverWe also invested substantial efforts in improving the reproducibility, decreasing the CVs and establishing several quality controls before and after the addition of drugs . AutomatAltogether, in this paper we present a series of strategies that may be employed by other research groups that want to perform small screenings for PTM modulators. Even though an additional setup would certainly be required if a different PTM and/or loading control are used, most of our experimental pipeline will remain similar if an MTS cell-based WB approach is considered. Our findings may be of importance, especially for academic labs with a low budget, which do not have expensive heavy equipment or HTS facilities available. Our detailed protocol should provide strategies to strikingly increase the throughput capacity of classical WB, reaching high levels of accuracy and reproducibility to successfully face small screening projects.


Proteasomes are key proteases in regulating protein homeostasis. Their holo-enzymes are composed of 40 different subunits which are arranged in a proteolytic core (CP) flanked by one to two regulatory particles (RP). Proteasomal proteolysis is essential for the degradation of proteins which control time-sensitive processes like cell cycle progression and stress response. In dividing yeast and human cells, proteasomes are primarily nuclear suggesting that proteasomal proteolysis is mainly required in the nucleus during cell proliferation. In yeast, which have a closed mitosis, proteasomes are imported into the nucleus as immature precursors via the classical import pathway. During quiescence, the reversible absence of proliferation induced by nutrient depletion or growth factor deprivation, proteasomes move from the nucleus into the cytoplasm. In the cytoplasm of quiescent yeast, proteasomes are dissociated into CP and RP and stored in membrane-less cytoplasmic foci, named proteasome storage granules (PSGs). With the resumption of growth, PSGs clear and mature proteasomes are transported into the nucleus by Blm10, a conserved 240 kDa protein and proteasome-intrinsic import receptor. How proteasomes are exported from the nucleus into the cytoplasm is unknown. Saccharomyces cerevisiae is an amenable eukaryotic organism to study basic concepts of highly conserved cellular processes. One of these processes is the regulation of protein homeostasis by ubiquitin-dependent protein degradation in which the proteasome is engaged as key protease  composed of ~ 23 different subunits which are assigned to two subcomplexes, the base and the lid. One or two RPs flank the CP by forming 26 and 30S proteasomes with single-capped RP-CP and double-capped RP-CP-RP configurations, respectively. The RP contains intrinsic ubiquitin receptors, Rpn10 and Rpn13, and docking sites for extrinsic ubiquitin receptors located at the base subunits Rpn1 and Rpn2. With a molecular mass of around 100 kDa Rpn1 and Rpn2 are the largest proteasomal subunits  and Ecm29, are involved in the quality control of proteasome assembly. Blm10 preferentially binds mature CP with constitutively open α rings , protein phosphatase 2A (PP2A), PI3-kinase TOR1) repeat family.In yeast, macromolecules are exchanged between the nucleo- and cytoplasm through the nuclear pore complex (NPC) studded throughout the nuclear envelope (NE)  Wente a. IntrinsThe gradient of the small GTPase Ran across the NE confers directionality of nuclear transport through the NPC. Ran GTPase, in yeast named Gsp1, is GTP-bound in the nucleoplasm and GDP-bound in the cytoplasm, as a result of the activities of nuclear Ran guanine nucleotide exchange factor and cytoplasmic RanGTPase activating protein  but not the srp1-31 (E145K) allele, after we had requested the original strains from Masayasu Nomura  mutant  mutant , suggest) mutant .srp1-49 mutants which are import-proficient for proteins with cNLS prototypes , suggesting that proteasomes are taken up into the nucleus as matured enzymes  was conducted to investigate the spatio-temporal dynamics of proteasome holo-enzymes. First, the distribution of CP, RP base and lid was monitored in exponentially growing yeast cells by expressing GFP-labeled reporter subunits behind the endogenous promoter at the chromosomal locus . A similar overall distribution was determined for all three subunits reflecting equal stoichiometry with 140–200 nM in the cytoplasm and 830–980 nM in the nucleus. Second, dual color fluorescence cross correlation spectroscopy (FCCS) revealed stable proteasome holo-enzymes that were labeled with differently colored fluorescent proteins fused to either CP and RP subunits. Compared with wild-type, their distribution was shifted from the nucleo- to the cytoplasm in Finally, genetically tethered RP-CP-RP were analyzed by FCS. Since it is impossible to distinguish whether RP-CP-RP were assembled in the cytoplasm or nucleoplasm, their nuclear import was monitored in yeast cells upon exit from quiescence. For this purpose, yeast cells were grown to stationary phase. Proteasomes were depleted from the nucleus and accumulated in cytoplasmic storage granules. With the resumption of growth, the proteasome storage granules rapidly clear and genetically tethered RP-CP-RP were detected in the nucleus, providing evidence that cytoplasmic RP-CP-RP can pass the NPC into the nucleus (Pack et al., Interestingly, yeast cells having proteasomes genetically anchored to the plasma membrane or to ribosomes resulted in conditional depletion of nuclear proteasomes and lethality, while yeast cells having proteasomes genetically anchored to the nucleus resulting in the depletion of cytoplasmic proteasomes show mild growth defects (Tsuchiya et al., With the transition from proliferation to quiescence proteasomes are exported from the nucleus into the cytoplasm, which seems to be independent of the canonical exportin Crm1/Xpo1 [(Nemec et al., Notably, Blm10, a conserved 240 kDa protein of the HEAT repeat-like family, is bound to the CP and required to sequester the CP into the PSG. With the resumption of cell growth, Blm10 facilitated nuclear import of matured CP (Weberruss et al., Several pieces of evidence supported our conclusion that Blm10 represents the first CP-dedicated nuclear transporter . Blm10 bblm10Δ mutants also fail to quickly resume cell growth which correlated with delayed nuclear import of the CP. Due to the lack of Blm10 in blm10Δ mutants CP precursor complexes must first be assembled as import cargoes to be transported by the canonical cNLS pathway.Quiescent It is still unknown how the RP is transported from the PSG into the nucleus upon exit from quiescence. Possibly nuclear import of the RP is facilitated by Spg5 which assists in the sequestration of the RP into PSG (Hanna et al., snf1Δ null mutants. Snf1 is an AMP-dependent kinase which senses glucose availability and seems to be involved in targeting the CP and RP into PSGs (Weberruss et al., N-myristoylation and N-acetylation of proteasomal subunits contribute to nuclear proteasome localization and suggest that multiple factors influence nuclear transport of proteasomes (Kimura et al., Post-translational modifications, such as phosphorylation, regulate the activity of nuclear proteasomes (Bose et al., As mentioned above nuclear export of proteasomes is still an unexplored topic.CE wrote the manuscript, PW added critical comments on the manuscript, prepared the Figures and Figure Legends.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.


X-ray absorption spectroscopy (XAS) is a widely-used materials characterization technique. In this work we present a database of computed XAS spectra, using the Green's formulation of the multiple scattering theory implemented in the FEFF code. With more than 500,000 K-edge X-ray absorption near edge (XANES) spectra for more than 40,000 unique materials, this database constitutes the largest existing collection of computed XAS spectra to date. The data is openly distributed via the Materials Project, enabling researchers across the world to access it for free and use it for comparisons with experiments and further analysis. A crucial step in the process of novel materials discovery is the characterization of the synthesized material. There exists a wide array of tools and spectroscopic techniques that are used in the material identification process, e.g. X-ray diffraction (XRD), X-ray emission spectroscopy (XES), and X-ray absorption spectroscopy (XAS). XAS is widely-employed in the characterization of the local structural environment surrounding select elements within a material.1. The availability of relatively inexpensive laboratory-based XAFS system (http://easyxafs.com/) and third generation synchrotron facilities2 have established the groundwork for the broad application of high-resolution XAS in characterization of materials. On the other hand, modern computational resources and methodologies have reached a level of maturity and efficiency to complement as well as to fast-track new discoveries. In the case of XAS, a variety of theoretical frameworks including time-dependent density-functional theory (TDDFT)4, multiple-scattering5, and Bethe-Salpeter equation (BSE) based approaches6 have been implemented, each exhibits its advantages and drawbacks. Leveraging spectroscopic simulation software with large crystal structure databases enables the computation of a large number of reliable theoretical spectra corresponding to well-defined crystal structures7, providing a broad reference dataset with clean unique structural fingerprints that can be used for identification purposes. With the help of carefully crafted software tools, these computations can be performed in a high-throughput fashion and can be used to scan the structural phase space for novel materials. In addition, through proper integration with modern database tools, these scans can be saved for future use and leveraged for training machine learning algorithms to assist the characterization process. Some examples of such publicly available spectroscopic database are the EELS Data Base8, a compilation of valence and core-loss spectra from EELS and XAS experiments containing 271 spectra that covers 39 elements of the periodic table, and XCOM (https://www.nist.gov/pml/xcom-photon-cross-sections-database), which provides photon cross sections for scattering, photoelectric absorption and pair production, as well as total attenuation coefficients, for any element, compound or mixture. Other existing XAS databases10, i.e. https://www.cat.hokudai.ac.jp/catdb/ and http://cars.uchicago.edu/xaslib, covering a few hundred spectra, are hosted across the world and serve as valuable references for analysis.Great progress has been made over the past few years in the development of laboratory-based X-ray spectrometers for high-resolution x-ray absorption near edge structure (XANES) and X-ray emission spectroscopy (XES)ab initio calculation of K-edge X-ray absorption near edge spectra (XANES). Using the parameter settings obtained from recent benchmarking work against experimental spectra11 and the FEFF workflow infrastructure available in the open source materials science workflow package Atomate12, we generate spectra of all the materials available in the publicly accessible and widely used materials database, Materials Project (MP)7.The FEFF framework affords relatively inexpensive calculations compared to other approaches and requires minimum adjustable parameters. It provides an efficient means of generating high quality XAS spectra for a larger amount of chemical systems and structures. Hence, in our study, we selected the FEFF9 ref.  program 11 or other machine learning methods13, the data can be leveraged for on-the-fly characterization. Though the peak positions and amplitudes of the computational K-edge XANES spectral may exhibit differences compared to experimental spectra, theoretically computed XANES spectra provide sufficient information to identify oxidation state and coordination chemistry of the probe atom, and can be highly useful when experimental data are not available or scarce. For example, a previous study by Timoshenko et al.14 showed that ab initio XANES spectra provide excellent input data for training supervised machine learning models aimed at reconstructing metal catalyst structures from their experimental XANES. The current authors have also shown in a previous study11 that an ensemble-learned algorithm to match experimental K-edge XANES spectra in the EELS Data Base to computed spectra can achieve nearly 80% accuracy in identifying the correct oxidation state and coordination environment. In addition, the data is associated with download options and programmatic analyses tools for each structure in the Materials Project database, thereby making it accessible to the broader materials science community. Furthermore, the MP web application enables users to select spectra from the database, upload experimental spectra data and predict the material composition using the matching tool. To date, this is the largest computed XAS dataset available and it is still expanding.A comprehensive database of computed XAS spectra enables comparison between different spectroscopic signatures across chemical systems and structures such that rapid determination of oxidation states, coordination environment, and other local atomic structure information can be obtained. Furthermore, using matching algorithmsThe paper is organized as follows; first we briefly describe the XAS computation methodology as implemented in the FEFF code, and thereafter the high-throughput framework used in the generation of the spectra. We then describe the data storage and dissemination details, followed by the technical validation of the computational methodology and the high-throughput framework.5 code which employs the Green's formulation of the multiple scattering theory to compute the spectra5. The X-ray absorption μ is computed in a manner similar to Fermi's golden rule when written in terms of the projected photoelectron density of final states or the imaginary part of the one-particle Green's function, G. In terms of the Green's function, G, the absorption coefficient, μ, from a given core level c is given by ref. r,r′;E) given byEf and net lifetime Γ, of a one-particle Hamiltonian that includes an optical potential with appropriate core hole screening.The K-edge XANES spectra were computed using the FEFFG incrementally using matrix factorization and uses the spherical muffin-tin approximation for the scattering potential15. For a more detailed description, we direct the readers to the review paper by Rehr et al.15The FEFF code computes the full propagator 12. Atomate provides a high-level interface to compose workflows using open source materials science softwares such as pymatgen16, FireWorks17 and Custodian .For the high-throughput XAS spectra generation, we use the FEFF workflow  availablEach FEFF calculation involves the following 3 steps:Selection of the absorbing site and the cluster of atoms to be included in the scattering calculations.Generation of the FEFF input files for each site and its surrounding atomic cluster.Execution of the FEFF binary on the generated input files.https://materialsproject.org/) website.As shown in https://github.com/materialsproject and https://github.com/hackingmaterials/atomate.Except the for FEFF code, which is proprietary, all the other aforementioned packages used in the high-throughput XAS workflow are open source and can be found at xas.json.tgz (Data Citation 1). The same data is also stored in the Materials Project database and is made freely available to the public. We also provide a user friendly web application called XAS Matcher  in the Materials Project database are available, for all the symmetrically unique sites in each structure. Each structure dataset is stored in the database in the binary JavaScript Object Notation (BSON) format. The keys and respective descriptions are summarized in 18. Data can be downloaded for each element in the selected structure. The downloaded spectrum is provided in a tab separated file format and includes the spectral data for all the symmetrically unique sites of the selected element in the structure. The standard XAS data interchange (XDI) format19 is also available for download, which can be directly imported into most existing XAFS data analysis programs20 for further detailed analysis.The spectral data as well as the input parameters used for the calculations can be downloaded either directly from the Material Project website or using the REST Application Programming Interface (API) available in pymatgenThe workflow described above relies on the default FEFF input parameter settings to generate the K-edge XANES spectra in a high throughput fashion. In this section, we will briefly describe the major FEFF input parameters relevant to the calculation of the XANES spectra, the bench-marking procedure and sample validation cases against experimentally available XANES spectra.8, supplemented by 6 experimental XANES spectra of V2O5, V2O3, VO2, LiNiO2, LiCoO2, and NiO from previous studies22. Compounds included in the earliest commentary5 of FEFF9 software were also evaluated. The benchmark compound dataset has a high structural diversity and covers a wide chemical space. Detailed benchmark information is provided in a previous publication11. For benchmark compounds that contain detailed structural information, we used structures from the Materials Project  database that exhibit an optimized geometry with the same space group as the benchmark compound. For benchmark compounds without provided structural information, MP ground state structures with identical chemical compositions were used11.FEFF9 is capable of achieving quantitative agreement with XAS experimental results with a minimal set of adjustable parameters. The development and implementation of parameter-free models within the FEFF9 code permit consistent calculations across different chemical systems and constitute the main advantage for high-throughput calculations. In the benchmarking process, we included 13 unique compounds and their corresponding high-quality K-edge XAS spectra available in the open EELS/XAS databaseThe following input fields in FEFF9 were subjected to convergence and optimization tests:Self-consistent field (SCF): The SCF card controls FEFF automated self-consistent potential calculations.The self-consistent potential calculation is required in the XANES calculation for the Fermi level E0 estimation. In the convergence test, we varied the number of atoms included in the self-consistent potential calculations through changing the rfms1 value from 2 Å to 8 Å at 1 Å interval.Full multiple scattering (FMS): The FMS card is required in the XANES calculation as the multiple scattering (MS) expansion's convergence might not be stable in the XANES calculation5. To identify the effect of rfms field on XANES calculation results, we varied the rfms value from 3 Å to 11 Å at 1 Å interval.EXCHANGE: The EXCHANGE card specifies the exchange correlation potential model used for XANES calculation. The Hedin-Lundqvist self-energy is chosen as previously recommended for most applications23.COREHOLE: The COREHOLE card is used for specifying how the core is treated during XANES calculation. The default choice in FEFF treats the core-hole interaction based on the Final State Rule (FSR), which could overestimate the strength of the core-hole and excludes the core-hole mixing effect24. To overcome these deficiencies and avoid possible break down of FSR for the L-shell metals25, the random phase approximation (RPA) is used to approximate the core-hole interactions in our high-throughput K-edge XANES calculations.Xi and Yi, is calculated using the following expression:Xi and Yi are the corresponding absorption coefficients.Through the benchmarking study, a set of optimized FEFF parameters were determined to achieve the best balance between the computational cost and convergence performance. The Pearson correlation coefficient is used to compare spectra calculated using different parameters. The Pearson correlation coefficient between two same energy grid spectra, C and B2O3, the FEFF-computed spectra are not in good agreement with experimental spectra. Possible solutions include the adoption of other higher-level real-space full-potential multiple scattering theory or first principles approaches26, which are not ideal for high-throughput implementation due to their high computational cost. We noticed that the Pearson correlation coefficients between FEFF computed spectra and experimental spectra obtained from EELS Data Base are above 0.85 in general see . For C awww.materialsproject.org). Users can also download the data using the REST API that is part of pymatgen. All the codes used to create the high throughput are made freely available at Github. We hope that the users will find the data to be useful and will find novel ways to employ the data to accelerate their research. One such use case would be using machine learning techniques to predict structures from the experimentally measured spectra.We present a database of K-edge XANES spectra computed using FEFF. The data is made freely available to all researchers via the Materials Project.Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Considering the large occupation proportion of image data in both data center and edge devices, a device integration with optical sensing and data storage and processing is highly demanded for future energy-efficient and miniaturized electronic system. Two-dimensional (2D) materials and their heterostructures have exhibited broadband photoresponse and high photoresponsivity in the configuration of optical sensors and showed fast switching speed, multi-bit data storage, and large ON/OFF ratio in memory devices. In addition, its ultrathin body thickness and transfer process at low temperature allow 2D materials to be heterogeneously integrated with other existing materials system. In this paper, we overview the state-of-the-art optoelectronic random-access memories (ORAMs) based on 2D materials, as well as ORAM synaptic devices and their applications in neural network and image processing. The ORAM devices potentially enable direct storage/processing of sensory data from external environment. We also provide perspectives on possible directions of other neuromorphic sensor design ( The advance of digital technology enables data storage and processing in binary form with high speed, accuracy and density. The development of computing hardware has been driven by Moore's law in the past few decades. However, the downward scaling trend is hitting the scaling limits according to the International Technology Roadmap for Semiconductors (ITRS) 2.0. Different from the strategy of device miniaturization involved in the digital components, a new trend of “More-than-Moore” (MtM) is highlighted in the ITRS 2.0, which is featured as a diversified function of data collection and information storage/ processing devices. The function diversification described in MtM can be considered as another form of “scaling down” for the electronic system, which includes noncomputing functions into the digital ones, enabling the interaction of digital devices with the outside world directly. Device integration of noncomputing  and digital components  is one of the main approaches for function diversification of MtM. Considering the large occupation proportion of image data in both data center and edge devices, we will mainly discuss the integrations of optical sensors and digital devices (data storage and processing) in this review.Optoelectronic random-access memories (ORAMs) offer great opportunities for multifunctional integration of optical sensing, data storage and processing into one single device. In contrast to traditional random-access memories (RAMs), optical excitation provides another method to manipulate the memory cell in ORAMs, which enables to store light information with an electronic readout at low programming voltages and is beneficial for the storage and transport of secured information. The combination of both optical stimuli and electrical readout voltage has potential for enlarging the ratio of low resistance state (LRS) and high resistance state (HRS), thus enabling multilevel data storage and improving data storage capacity. From another aspect, the optical trigger is employed in ORAM for the switching, bringing the benefits of wide bandwidth and reduction of electrical loss during sensory data transmission –6. In ade.g., pattern recognition, and multiobject detection) with high energy efficiency. The power consumption in human brain is 5 orders magnitude lower than conventional computers . A . A  et ae to HRS . This opage < 4V .The opti2/PbS van der Waals heterostructure, which can respond to a wide wavelength range from 880 nm to 1940 nm  . In comp et al. first reported an ORAM synaptic device based on graphene/carbon nanotube (CNT) heterostructure, demonstrating light-tunable synaptic behaviors, such as, light-tunable STP, LTP, STLR and STDP , where the conductance increases abruptly and relaxes within seconds. The STP is triggered owing to the electron transfer from CNT to graphene upon the light illumination, tuning the built-in field formed at graphene/CNT interface. Optically tuned paired-pulse facilitation (PPF) function is an important function for temporal visual information encoding, as illustrated in 2 surface owing to the defects. A STDP learning rule (symmetric STDP) is also demonstrated in this work  are mimicked  heterojunction, exhibiting large synaptic weight change and PPF ratio [2/boron-doped Si nanocrystals was employed for ORAM synaptic device with wide absorption spectrum from ultraviolet to near-infrared and low power consumption of 75 fJ, which points the way to the development of low-power artificial visual intelligence beyond human eyes [Other types of 2D materials heterostructures have also been utilized for ORAM synaptic devices, such as MoSPF ratio . To furtman eyes .  Table e.g., pattern recognition). Generally, for the conventional image processing based on neuromorphic computing, the optical signals in the images are first converted to electronic signals through image sensors. The electronic signals are then transferred to the synaptic arrays in the neural network for further processing. In contrast to the conventional memory synaptic devices operated by electrical method, the ORAM synaptic devices respond to the optical stimuli directly and exhibit light-evoked synaptic behaviors. The constructed optic-neural network through ORAM synaptic devices can potentially simplify the circuitry for neuromorphic image processing and reduce the power consumption during the data transmission. Additionally, the high integration level in ORAM synaptic device provides future miniaturized and power-efficient artificial eye design [ et al. first developed an optic-neural network (ONN) with optical synapses based on graphene/2D perovskite, for which the pattern recognition rate in this ONN can reach up to 80 % [ et al. further demonstrated an ONN for mimicking color-mixed pattern recognition in human vision. The ORAM synaptic device is integrated with a WSe2/h-BN photodetector and a synaptic device based on WSe2/weight control layer (WCL)/h-BN /h-BN . The optCL)/h-BN . AccordiCL)/h-BN  is develCL)/h-BN  in ONN iOwing to the strong light-matter interaction, unique physical properties, electrostatic tunability and downward scaling potential, 2D materials and their heterostructures have been widely employed for optoelectronics and memory applications in recent years. Towards the trend of functional diversification in “MtM”, the integration of optical sensing, data storage and processing drives the design of ORAM and optoelectronic synaptic devices, which can potentially simplify the circuit complexity and reduce the power consumption for the photonic circuit and artificial vision. In contrast to the conventional image sensors with light-intensity sensing, the ORAM device with light tunable plasticity (light-dosage sensing) can further be applied for the construction of ONN for pattern recognition, potentially bridging the sensory data and neural network in the neuromorphic visual system. The nonvolatile ORAMs based on 2D materials heterostructures have exhibited high photoresponsivity, stable multilevel data storage and good retention and endurance properties. Although ORAM devices based on 2D materials have exhibited various advantages over other materials, the high programming voltage is still a main issue, which causes high power consumption. Additionally, the three-terminal structure in most of the 2D material ORAMs restricts the integration density in the integrated circuits. To develop appropriate 2D material ORAMs with two-terminal structure can be a potential approach for low-power and high-integration-density devices in the future.., sound detectors) can also be potentially developed as neuromorphic sensors with synaptic behaviors through appropriate structure design. For both sound detectors and gas sensors, 2D materials have also exhibited their potentials in achieving high sensitivity. More specifically, 2D materials  are adopted as promising candidates for high-sensitive gas sensors because of the large surface-to-volume ratio and tunable functionality of the surface through decorating transition metals or functional groups [The ORAM synaptic device that integrates optical sensing and synaptic functions have exhibits great potentials in neuromorphic computing and visual information processing. For the future artificial intelligence, to mimic the sensing and processing of auditory or olfactory information like the human brain is also demanded for energy-efficient and miniaturized processing system. As the conventional visual, olfactory and auditory sensors generate a large amount of redundant data, neuromorphic sensory devices potentially allow for the real-time storage and processing of the sensory data with reduced power consumption. Similar to the extension from optical sensors to ORAM synaptic devices, olfactory sensors  and the auditory sensors (e.gl groups –69. The l groups –72. The


Rosai-Dorfman disease (RDD) is a rare proliferative histiocytic disorder, most commonly presenting with cervical lymphadenopathy. When extranodal, a rare manifestation of the disease is the presence of cutaneous lesions. Surgical excision has shown promising results in patients with cutaneous RDD; however, no optimal management has been elucidated. We present a 60-year-old female with recurrence of left thigh cutaneous lesions consistent with extranodal RDD whose optimal management required combined used of excision and chemotherapy. Rosai-Dorfman disease (RDD) is a rare proliferative histiocytic disorder first reported in 1969 by Juan Rosai and Ronald Dorfman. Most commonly, the clinical presentation of RDD is massive, painless cervical lymphadenopathy with varying symptoms dependent upon involved organ systems . We presAt initial presentation, the patient was a 58-year-old African American female who presented to a general surgical clinic with a left lateral proximal thigh cutaneous nodules for one-year duration. She complained of 15+ mildly tender nodules that were gradually increasing in size and unrelated to trauma or infection. Previous excisional biopsy at an outside institution had revealed RDD.Our general surgery team carried out a wide local full-thickness excision of the left thigh tumor-involved skin and subcutaneous tissue with a split-thickness skin graft (STSG) performed in February 2017, encompassing all nodules and with grossly clear margins. Histopathology showed predominant histiocytes and macrophages with abundant eosinophilic cytoplasm that ranged from round- to spindle-shaped. Emperiolysis was noted in histiocytes and macrophages throughout the samples. These morphological features with concomitant positive reactivity for S100 (family of soluble in 100% saturated solution of ammonium sulfate) and cluster of differentiation (CD)68 supported the diagnosis of RDD  in virgin tissue. Physical examination revealed non-tender, rubbery, grape-sized nodule that was freely mobile in the lateral plane and fixed to underlying tissues in the vertical plane. Wide local excision revealed diseased tissue extending and invading into the left iliotibial band. During the operation, additional lesions were palpated within the subcutaneous tissues, and dissection was continued until grossly negative margins were obtained. What was believed to be a minor lesion resulted in the excision of most of the iliotibial band.Specimens and slides were submitted to a specialized cutaneous pathologist. The pathology demonstrated atypical mononuclear and multinuclear cell proliferation with multifocal emperipolesis most consistent with RDD with extensive areas of expansive aggregates of histiocytes positive for S100 and CD68 consistent with RDD with sarcomatous features.On September 2018 as a component of follow up, a positron emission tomography/computed tomography (PET/CT) was conducted which was significant for a third recurrence this time with multiple subcutaneous soft tissue nodules noted in the lower back at the level of L4 and in bilateral thighs, demonstrating increased fluorodeoxyglucose (FDG) uptake suggestive of cutaneous extranodal RDD  daily for one week. At the first post-chemotherapy follow up with the general surgeon two months later, the palpable masses near the patient’s spine and in bilateral thighs had decreased in size to a negligible amount. Thereafter, she remained asymptomatic. RDD is identified histologically as histiocytes containing a vesicular nucleus and abundant cytoplasm with accompanying emperipolesis (i.e. lymphophagocytosis) of lymphocytes or plasma cells. The histiocytes are predominantly S100-positive and also tend to express positivity for macrophage lineage marker CD68 while the plasma cells are typically polyclonal .Although most-commonly confined within the cervical lymph nodes, one study showed up to 23%-40% of patients with RDD may present with extranodal proliferation, with the most common sites of being the skin, bones, and upper respiratory tract .The cutaneous form of the disease generally presents as deep red papules, plaques or nodules that may change in appearance over time. The cutaneous form of the disease has been reported to have a marked female predominance and a wide age distribution and without a predilection for a specific site of the body . AlthougFor the purposes of evaluation and staging, a widely utilized method is 18F-FDG PET/CT. Reports have shown that RDD lesions have considerable avidity for FDG as a result of their intense glucose dependence secondary to the energy requirements of proliferating histiocytes/lymphocytic inflammatory cells . ImagingOur patient benefited from follow-up PET/CT which revealed the metastatic-like behavior, prompting successful chemotherapy treatment.Optimal treatment guidelines for RDD have not been established. Treatment plans are generally dependent on the extent and severity of the individual patient’s disease. Treatment guidelines have included operation, chemotherapy, steroids or other immunomodulatory medications. Reports have indicated a promising potential for the use of surgical intervention in the management of the cutaneous form of the RDD with no recurrence at distant follow-ups -8.Our patient’s case was refractory to multiple excisions due to the metastatic-like behavior of this subset of Rosai-Dorfman lesions. Thus, chemotherapy with cladribine was necessary to effect a cure.To our knowledge, we present the second case of recurrent cutaneous RDD and the first such disease with metastatic- and sacroma-like behavior. For similar cases, we recommend the early combined use of wide local excision and intravenous cladribine chemotherapy.Cutaneous RDD is an extremely rare and complex benign soft tissue disorder. We present the second case of recurrent cutaneous RDD and one with rare metastatic- and sarcoma-like behavior. Chemotherapy with intravenous cladribine in combination with wide local excisions led to regression of disease, and this combination may be the optimal treatment for such aggressive cutaneous RDD.


The tumor microenvironment is increasingly recognized as an active participant in tumor progression. A recent pan-cancer genomic profile analysis has revealed that gene signatures representing components of the tumor microenvironment are robust predictors of survival. A stromal gene signature representing fibroblasts and extracellular matrix components has been associated with good survival in diffuse large B-cell lymphoma (DLBCL). Paradoxically, a closely related gene signature has been shown to correlate with poor survival in carcinomas, including breast, ovarian, pancreatic, and colorectal cancer. To date, there has been no explanation for this paradoxical inverse correlation with survival outcomes in DLBCL and carcinomas. Using public gene data sets, we confirm that the DLBCL stromal gene signature is associated with good survival in DLBCL and several other B-cell lymphomas while it is associated with poor survival in ovarian cancer and several other solid tumors. We show that the DLBCL stromal gene signature is enriched in lymphoid fibroblasts in normal lymph nodes and in cancer-associated fibroblasts (CAFs) in ovarian cancer. Based on these findings, we propose several possible mechanisms by which CAFs may contribute to opposite survival outcomes in B-cell lymphomas and carcinomas. PREdiction of Clinical Outcomes from Genomic Profiles (PRECOG) analysis revealed that genes in the tumor microenvironment are better predictors of survival than genes expressed in malignant tumor cells  are stromal cells that produce ECM, provide scaffolding, and exert regulatory functions through growth factors, cytokines, and chemokines that can promote tumor growth, angiogenesis, invasion, and metastasis . Recent Table 1). This pattern of inverse association with survival between B-cell lymphomas and carcinomas/brain tumors was specific to the DLBCL stromal-1 gene signature, and was not associated with the DLBCL stromal-2 gene signature represented by 34 genes . Closer examination of the stromal cell subtypes revealed that the DLBCL stromal-1 and stromal-2 signature genes were preferentially expressed in different types of stromal cells. DLBCL stromal-1 signature genes were particularly enriched in cells characterized by expression of podoplanin (PDPN) and platelet-derived growth factor receptor α (PDGFRα), including FRC from mesenteric and subcutaneous lymph nodes and the so-called double-negative stromal cells, while stromal-2 signature genes were enriched in blood and lymphatic endothelial cells .To identify immune cell types that express the DLBCL stromal-1 and stromal-2 signature genes, we looked for enrichment of these genes in the transcriptomes of 249 normal immunological cell types that had been isolated from mice and characterized by the Immunological Genome Project (ImmGen) . This anFigure 2). The DLBCL stromal-2 gene signature was enriched in CAFs but also in the normal ovary stroma .To identify cells that express the DLBCL stromal-1 and stromal-2 signature genes in an epithelial tumor, we selected ovarian cancer because of the existing microarray data set (GSE40595) in which a large number of ovarian cancers have been laser capture microdissected into epithelial and stromal components . For comIt is unusual for a gene signature to be associated with inverse survival outcomes in B-cell lymphomas and carcinomas. This is unlikely to be a technical error related to microarray technology as several individual genes from the DLBCL stromal-1 signature have been validated as predictors of good survival in DLBCL by independent technologies, such as immunohistochemistry and qPCR in formalin-fixed paraffin-embedded tissues . Similarin vivo to avoid tumor detection and rejection by the host immune system . The decrease in stromal gene signature expression in the later stages of DLBCL may indicate that the stroma plays a role in localizing the lymphoma cells to the lymph nodes during the earlier stages of the disease. In contrast, DLBCL stromal-1 gene signature expression is typically increased with increased tumor stage in epithelial carcinomas, such as ovarian cancer . The increase in CAFs in the later stages of carcinomas may prevent immune cells from reaching the tumor parenchyma by trapping the immune cells in the stroma thereby preventing an anti-tumor response. A recent study of immune cell infiltration in metastatic urothelial carcinomas showed that patients whose tumors were classified as immune-excluded  had increased disease progression and decreased response to immunotherapy . In carcinomas, TGFβ has been shown to promote tumor progression by inhibiting immunosurveillance through multiple mechanisms . TGFβ induces CAF activation and fibroblast-to-myofibroblast transition with consequent linearization of collagen fibers and stiffening of the ECM. In turn, activated CAFs induce TGFβ signaling to perpetually maintain the activated state . Consistchanisms , includichanisms  and limirenchyma . It is lrenchyma . Stroma-renchyma . Thus, tPast clinical trials have taught us that successful targeted therapies in one disease do not always yield the desired results in another disease despite the presence of the same target. One example is the poor response of B-cell-infiltrated carcinomas to rituximab, which has shown remarkable success in lymphomas and rheumatoid arthritis. The opposite survival outcomes associated with the presence of stromal cells in B-cell lymphomas and carcinomas should serve as a warning that targeting the tumor microenvironment may produce opposite effects in B-cell lymphomas and carcinomas.https://www.ncbi.nlm.nih.gov/gds) Immunological Genome Project (https://www.immgen.org) PRECOG – PREdiction of Clinical Outcomes from Genomic Profiles (https://precog.stanford.edu) R2: Genomics Analysis and Visualization Platform (http://hgserver1.amc.nl) The Cancer Genome Atlas Project (https://cancergenome.nih.gov).GEO Data Sets (SO analyzed the public data sets. SO and MH wrote the manuscript.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.


Using this imaging system, 3 whole mouse brains are imaged, and 250 high-resolution An automated dual-resolution serial optical coherence tomography (2R-SOCT) scanner is developed. The serial histology system combines a low-resolution ( The common principle shared by all serial blockface histology methods is the repeated removal of small tissue layers to reveal new sample cross sections. This destructive imaging approach circumvents the limited penetration depth of light in tissue, and the motorized stage enables the acquisition of entire sample cross sections even with an imaging system using limited field-of-view (FOV) optics. Advantages of blockface imaging include the low quantity of sample deformation introduced by the tissue slicing process, its simple sample preparation protocol, and overall short acquisition time when compared to other serial histology methods such as serial histopathology–When designing a serial histology system or when performing an imaging study with such a system, a key aspect to consider is the imaging resolution. Using the tissue intrinsic contrast, high-resolution OCT can resolve fine tissue microstructure, such as the neuronal cell bodies22.1For this study, the Animal Research Ethics Committee of the Montréal Heart Institute approved all surgical procedures in accordance with the Canadian Council on Animal Care recommendations. A total of 2.2All fixed mouse brains were imaged prior to serial histology with a standard 3-D spin echo diffusion MRI sequence,ex vivo mouse brain templateThe dMRI data preprocessing consisted in multiple registration, segmentation, and denoising steps as follows. First, all diffusion weighted images (DWI) and their associated Most preprocessing algorithms and dMRI analysis performed in this study required brain extraction. This was performed using the “antsBrainExtraction” procedure provided in the ANTs toolkit and implemented in the Nipype neuroimaging data processing framework.,,,ex vivo option was used, which adds a fourth constrained water compartment to the NODDI model. This represents the water trapped by the tissue fixation. The choice of ex vivo diffusivity values for the isotropic and intracellular compartments was guided by previous values used in the literature.–Three dMRI analyses were performed on the data. First, in-house implementations2.3A dual-resolution swept-source serial optical coherence tomography (2R-SOCT) microscope was developed . This syThe fiber-based Michelson interferometer outputs were connected to dual-resolution free-space sample and reference arms by fixed focus fiber collimators . The laser beam was scanned laterally with a small beam diameter galvanometer system . For low-resolution imaging configuration, the galvanometer mirrors’ output was sent directly to a The automated histology apparatus was the last component of this dual-resolution serial OCT system. The sample was placed in a water-filled acrylic glass container and was maintained in place using a custom-made 3-D printed sample holder. The focus depth position within the tissue was initialized by first manually adjusting the sample stage height until the tissue appeared in a live OCT B-scan. Then the focus depth was set automatically using the Fibonacci search method.Using python, the OCT volume reconstruction was achieved during the acquisition. After each OCT volumetric tile acquisition by the waveform digitizer, the data were transferred directly into memory. The reference interference fringe was computed from the data as the average fringe for a given tile, and this reference was then subtracted from the measured signal. To limit the side lobes introduced by the wavelength swept-source profile, a Gaussian apodization function [2.4The data reconstruction method used to assemble the low-resolution OCT volumetric tiles into a single 3-D brain was presented in a previous publication.2.5Two types of dual-resolution acquisitions were performed: (1) fixed focus A second automatic ROI selection strategy, shown in en face planes only.Once all ROIs were selected for a given slice, the automatic dual-resolution acquisition began. For the OCM acquisitions, the sample was first moved to its 2.6,,ex vivo samples prior to serial histology. This multimodal registration technique required intermediate registration templates for each imaging modality (dMRI and OCT) as represented in ex vivo mouse brain atlas originally published by Dorr et al.,The precise alignment of the OCT and dMRI data is crucial in order to compare these imaging modalities. In previous works,2.7The precise positioning of the OCM volumes within the dMRI mouse brain was obtained by a combination of acquisition-related procedures and postacquisition registration techniques. First, the motorized sample stage was used to ensure an accurate and consistent correspondence of the Another calibration was performed in order to directly relate the images acquired by the jectives . Next, t2.8The last part of this methodology was to compare the istology . The goa33.1The axial and lateral resolutions of the dual-resolution serial OCT system were measured using an USAF1951 resolution target  and an OCT calibration phantom . All measurements were done in water. The average lateral and axial resolutions of the 3.2,,ex vivo OCT images as dark areas. The vessels have a tubular or circular shape based on their orientation with the slicing plane. The microvasculature density could thus be another factor that affects the measured OCT contrast in ex vivo brain tissue.An example of a fixed-focus dual-resolution mosaic acquisition is shown in ue slice . IndividAn example of the second type of dual resolution acquisition (dynamic-focusing OCT or OCM) is shown in For the second type of dual-resolution acquisition, the ROI positions could either be selected using the same GUI as for the dual-resolution mosaic images, or they could be generated automatically from the last acquired 3× OCT tissue slice. 3.3A multimodal comparison was performed between the OCM ROIs and the dMRI data. A total of For this demonstration, First, FA is known to be affected by the presence of crossing fibers, fiber dispersion, fiber bundle density, and by the microstructural architecture of the cellular membranes.A quantitative comparison between the OCM and the dMRI values was performed . Image f4The novelty of our proposed dual resolution serial OCT scanner lies in the fully automated acquisition procedure that allows accurate and repeatable positioning of high-resolution images within whole mouse brains. The two-objective configuration of the 2R-SOCT system benefits from the advantages of both types of OCT: the ,A few design choices of the 2R-SOCT system introduced limitations that could be improved in future implementations of similar serial microscopes. First, the same swept-source laser was used for both Another design choice that increased the complexity of the system was the low-NA air-based in situ slice-to-volume registration procedureFor this demonstration of the 2R-SOCT imaging pipeline, the dual resolution ROIs were either selected manually by the operator during the acquisition or in a fully automated way by employing an ROI selection algorithm. The manual selection method is useful to target specific brain areas and to investigate at high-resolution particular details observed during the serial histology acquisition. As for the automated ROI selection strategy, it offers the advantage of eliminating operator bias in the ROI positions, and it diminishes the total acquisition time as no user interaction is required, thus allowing for more dual-resolution ROIs to be acquired during an imaging session. Furthermore, the probability maps used to guide the random position selection can be adapted to use various image features and thus provide an additional degree of freedom during experimental design, such as local texture or the output of a multilayer convolutional network. In future work, the automatic ROI selection methodology could be combined with an Upon inspection of the brain structures associated with each ROI, it appears that each structure’s volume fraction is often lower than could be anticipated when observing the The larger size of the 5A dual-resolution serial OCT imaging system was developed. The low-resolution OCT volumes acquired with a 66.1,The fixed mouse brains were kept in their skull and stored in PFA at 4°C until the dMRI acquisition session. On the acquisition day, the sample to be imaged was removed from PFA and pat-dried, placed in a modified 10-ml syringe filled with Fomblin and placed in the dMRI machine. A sample was thus colder than room temperature at the beginning of the dMRI acquisition, and it reached temperature equilibrium during the imaging experiment. The sample temperature variation throughout the acquisition induced time-varying water diffusivity. This is a known effect associated with fixed sample imaging.6.2The multimodal registration results were evaluated visually by comparing the aligned whole mouse brains with the dMRI data and by comparing the identified structures within the Upon visual inspection of the OCM images classified in the low-/high-dMRI metric groups –16, one Click here for additional data file.


The in situ-generated H2O2 can then promote ozone (O3) transformation to hydroxyl radicals (•OH), and thus enhance the abatement of ozone-refractory pollutants compared to conventional ozonation. In this study, a chemical kinetic model was employed to simulate micropollutant abatement during the E-peroxone treatment of various water matrices . Results show that by following the O3 and •OH exposures during the E-peroxone process, the abatement kinetics of a variety of model micropollutants could be well predicted using the model. In addition, the effect of specific ozone doses on micropollutant abatement efficiencies could be quantitatively evaluated using the model. Therefore, the chemical kinetic model can be used to reveal important information for the design and optimization of the treatment time and ozone doses of the E-peroxone process for cost-effective micropollutant abatement in water and wastewater treatment.The electro-peroxone (E-peroxone) process is an emerging electrocatalytic ozonation process that is enabled by in situ producing hydrogen peroxide (H Then water samples were analyzed with an ultra-performance liquid chromatography/tandem mass spectrometry . The separation of target compounds was performed with a Waters XBridgeTM C18 column . The mobile phase consisted of methanol (solvent A) and 10 mM ammonium acetate in ultrapure water (solvent B) at a flow rate of 0.35 L/min. The mass spectrometer was operated in negative electrospray ionization (ESI) and multiple reaction monitoring mode  of the E-peroxone process for micropollutant abatement. In addition, the specific ozone doses required to abate micropollutants to a certain degree  can be estimated using the model. This may offer a valuable tool for water utilities to balance between micropollutant abatement objectives and operation costs of water and wastewater treatment, thus optimizing ozone doses of the E-peroxone process for real applications.The results of this study demonstrate that the chemical kinetic model can provide a useful tool to design and optimize the E-peroxone process for cost-effective and energy-efficient micropollutant abatement in water and wastewater treatment. By monitoring the variations of O


Hydrogels are three-dimensional cross-linked polymer networks which can absorb and retain large amount of water. As representative soft materials with tunable chemical, physical and biological properties, hydrogels with different functions have been developed and utilized in a broad range of applications, from tissue engineering to soft robotics. However, conventional hydrogels usually suffer from weak mechanical properties and they are easily deformed or damaged when they are subjected to mechanical forces. The accumulation of the damage may lead to the permanent structural change and the loss of the functional properties of the hydrogels. Therefore, it is important to develop mechanically robust hydrogels with autonomous self-healing property in order to extend their lifespan for various applications. In this mini review, we focus on the discussion about the appropriate molecular design of the hydrogel network for achieving self-healing and excellent mechanical properties, respectively as well as the corresponding self-healing and toughening mechanisms. We conclude with perspectives on the remaining challenges in the field as well as the recommendations for future development. Hydrogels are three-dimensional networks consisting of cross-linked polymer chains, which form matrices with high water content , which limits certain applications such as tissue engineering (cartilages or tendon), since these tissues are natural hydrogels possessing high toughness  and high tensile strength (30 MPa)  (PEG) and diol-terminated four-armed PEG  of phenylboronic ester complex also depends on the conformation of the diol groups in the polymer chains. Schiller et al. determined binding constants between phenylboronic acid and different polysaccharides with different diol conformations  of the complexation strongly depends on the pKDynamic imine bonds (dynamic Schiff base) are formed between the aldehyde groups and the primary amine groups. Wei et al. reported a series of self-healing hydrogels based on dynamic Schiff base during the past few years  (PVA) hydrogel through facile freezing/thawing method  within their structure. PDA based materials are well known for their remarkable adhesive property through synergestic effect of various interactions such as hydrogen bonds, metal-catechol coordination, cation–π interactions, π-π interactions, etc.,  and polyacrylamide (PAAm) hydrogel throughγ-radiation-induced polymerization  on the alginate as the first “sacrificial network” and a chemically cross-linked PAM network as the second network  are considered as one class of tough hydrogels which employ mechanical energy dissipation mechanism Figure . The higin-situ free radical polymerization of the NIPAM monomers in well-dispersed clay aqueous solution without using chemical cross-linkers. A mechanism was proposed for the hydrogel formation: First, the initiator could bind to the surface of clay through electrostatic interactions which allows polymer chains to be grafted from the clay nanosheets. In addition, the nucleophilic N(H)CO in PNIPAm chains could coordinate with the Si on the clay. Therefore, the clay nanosheets can act as multifunctional cross-linkers which allow multiple polymer chains to be attached through various interactions. The clay/PNIPAM NC hydrogels possessed excellent mechanical properties with tensile stress of 0.109 MPa and elongation ratio of 1,424%. The outstanding mechanical properties of the NC hydrogel is due to the synergistic effect of the multiple reversible intermolecular interactions between nanomaterials and polymer chains for mechanical energy dissipation  are the hydrogels formed between nano-scaled materials and polymer chains Figure . The conIt is worth mentioning that through special design of NC hydrogels, one can fabricate hydrogels with both outstanding mechanical properties and excellent self-healing properties. Lu et al. adopted mussel-inspired surface chemistry to modify the surfaces of the nanomaterials with polydopamine (PDA) and developed a series of tough self-healing NC hydrogels for various applications such as reduced graphene oxide (rGO)/polyacrylamide (PAM) NC hydrogels for implantable bioelectronics  to quantitatively study the polymer interactions which account for the self-healing behavior of the hydrogel (Yan et al., It is also necessary to develop self-healing tough hydrogels into multifunctional “smart” materials, which should integrate other properties such as electrical conductivity (Wu et al., WW wrote the manuscript. RN and HZ helped to revise the manuscript.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.