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Abstract Background Targeting exportin1 (XPO1) with Selinexor (SEL) is a promising therapeutic strategy for patients with multiple myeloma (MM). However, intrinsic and acquired drug resistance constitute great challenges. SEL has been reported to promote the degradation of XPO1 protein in tumor cells. Nevertheless, in myeloma, the precise mechanisms underlying SEL-induced XPO1 degradation and its impact on drug responsiveness remain largely undefined. Methods We assessed XPO1 protein and mRNA levels using western blotting and RT-qPCR. Cycloheximide (CHX) chase assays and degradation blockade assays were used to determine the pathway of XPO1 degradation induced by SEL. The sensitivity of MM cell lines to SEL was evaluated using CCK8-based cell viability assays and AV-PI staining-based cell apoptosis assays. The subcellular localization of the cargo protein RanBP1 was assessed via immunofluorescence staining. Immunoprecipitation coupled with mass spectrometry (IP-MS), bioinformatics analysis and ubiquitination assays, were employed to identify the molecular targets responsible for SEL-induced degradation of XPO1. shRNA-mediated knockdown assays and small molecule inhibitors of USP7 were utilized to disrupt the function of USP7. The role of USP7 in modulating SEL sensitivity was analyzed in MM cell lines, primary CD138+ cells, and xenograft mouse models. Results SEL promotes the degradation of XPO1 in MM cells through the ubiquitin–proteasome pathway. There is a positive correlation between XPO1 degradation and sensitivity to SEL in these cells. Inhibiting XPO1 degradation reduces the functional inhibitory effects of SEL on XPO1, as evidenced by decreased nuclear localization of the cargo protein RanBP1. USP7 stabilizes XPO1 in MM cells via its deubiquitinating activity. SEL accelerates the ubiquitination and subsequent degradation of XPO1 by disrupting the interaction between XPO1 and USP7. The expression of USP7 is negatively correlated with patient prognosis and the sensitivity of MM cells to SEL. Inactivating or knocking down USP7 significantly enhances the anti-myeloma effects of SEL both in vitro and in vivo. Conclusion In conclusion, our findings underscore the essential role of XPO1 degradation in the anti-myeloma efficacy of SEL and establish a research foundation for targeting USP7 to improve the effectiveness of SEL-based therapies in MM.

Flight control system is typical safety critical system, and the reliability of flight control system plays an important role in ensuring the safe operation of aircraft. Traditional reliability analysis methods have a heavy reliance on the experience of analysts, which makes it easy for inconsistencies between reliability models and design models. The fault propagation behavior of the system is thoroughly described by the Architecture Analysis Design Language（AADL）and stochastic Petri nets（SPN）, and a method for model-based reliability analysis is proposed. The nominal model and error model of a lateral fly-by-wire flight control system are constructed using AADL. A method for extracting error propagation information from the AADL model is proposed, and the SPN model described the fault propagation behavior of the system is automatically generated by extracting the information of AADL model. Based on the SPN model, Monte Carlo simulation is used to evaluate the reliability of the lateral fly-by-wire flight control system, compared with the fault tree analysis method, the error is less than 0.018%, which can be neglected in practice. Through the method of this study, the reliability model is automatically generated by the AADL model, which ensures the consistency between the reliability model and the design model and avoids reliance on the experience of designers.

IntroductionProliferative glomerulonephritis with monoclonal immunoglobulin deposits (PGNMID) is a chronic glomerular disease caused by monoclonal gammopathy. IgG (mainly IgG3) is the most commonly involved isotype of PGNMID. Here we illustrated a novel medication regimen for the rare variant of PGNMID with deposition of monoclonal immunoglobulin light chain only (PGNMID-LC). Daratumumab has been proved effective in the treatment of plasma cell myeloma while its effect for PGNMID-LC has rarely been reported.MethodsA daratumumab combination therapy (D-VCd regimen, specifically are daratumumab + dexamethasone + bortezomib + cyclophosphamide) was adopted to treat a patient diagnosed with PGNMID-LC.ResultsThe utility of D-VCd regimen showed a favorable effect in this patient. After the fixed course, his clinical symptom, laboratory parameters, neoplastic plasma cells clonity all restored to normal range, and no obvious disease progression was observed throughout the treatment. After a follow up of 14 months, no significant renal or hematological disease progression has been observed.ConclusionThis case underscores the utility of D-VCd regimen in treatment of PGNMID-LC, and it’s inferred that daratumumab regimen has clinical effects in the disease primarily through targeting tumor clonity. However, data on the use of daratumumab (either in monotherapy or in combination) in clinical trials of PGNMID-LC is currently so limited that that more experiments are needed to support the inference.

Abstract Lung cancer is a leading cause of cancer deaths and imposes an enormous economic burden on patients. It is important to develop an accurate risk assessment model to determine the appropriate treatment for patients after an initial lung cancer diagnosis. The Cox proportional hazards model is mainly employed in survival analysis. However, real-world medical data are usually incomplete, posing a great challenge to the application of this model. Commonly used imputation methods cannot achieve sufficient accuracy when data are missing, so we investigated novel methods for the development of clinical prediction models. In this article, we present a novel model for survival prediction in missing scenarios. We collected data from 5,240 patients diagnosed with lung cancer at the Weihai Municipal Hospital, China. Then, we applied a joint model that combined a BN and a Cox model to predict mortality risk in individual patients with lung cancer. The established prognostic model achieved good predictive performance in discrimination and calibration. We showed that combining the BN with the Cox proportional hazards model is highly beneficial and provides a more efficient tool for risk prediction.

Pseudomonas, an important spoilage bacterium in animal-derived foods as well as fruits and vegetables in the cold chain, can cause huge economic losses to the food industry. The food spoilage ability of Pseudomonas is closely related to the formation of biofilm, which can enhance the survival and adaptability to the food processing and storage environment of Pseudomonas, causing persistent contamination. However, a systematic review of the biofilm formation of Pseudomonas and its mechanism is still lacking at present. This article describes the contamination status of Pseudomonas in fresh foods, analyzes the biofilm formation and extracellular matrix composition of Pseudomonas, and elucidates the major regulatory factors of the biofilm formation of Pseudomonas such as cyclic diguanylate, quorum sensing system and small RNAs. We believe that this review will provide a theoretical reference for uncovering the mechanism of food spoilage caused by Pseudomona and controlling its biofilm contamination.

Background Traditional risk evaluation models have been applied to guide public health and clinical practice in various studies. However, the application of existing methods to data sets with missing and censored data, as is often the case in electronic health records, requires additional considerations. We aimed to develop and validate a predictive model that exhibits high performance with data sets that contain missing and censored data. Methods and Results This is a retrospective cohort study of coronary heart disease at Weihai Municipal Hospital on unique patients aged 18 to 96 years between 2013 and 2021. A total of 169 692 participants formed our study population, of which 10 895 participants were diagnosed with coronary heart disease. Models were built for the risk of coronary heart disease based on demographic, laboratory, and medical history variables. All complete samples were assigned to the training set (n=110 325), whereas the remaining samples were assigned to the validation set (n=59 367). The area under the receiver operating characteristic curve value was 0.800 (95% CI, 0.794–0.805), and the C statistic was 0.796 (95% CI, 0.791–0.801) in the derivation cohort, and the corresponding values were 0.837 (95% CI, 0.821–0.853) and 0.838 (95% CI, 0.822–0.854) in the validation cohort. The calibration curve demonstrated its good calibration ability, and decision curve analysis showed its clinical usefulness. Conclusions Our proposed risk prediction model has demonstrated significant effectiveness in handling the complexities of electronic health record data, which often involve extensive missing data and censoring. This approach may offer potential assistance in the use of electronic health records to enhance patient outcomes.

Abstract Background Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of malignant plasma cells. Despite extensive research, molecular mechanisms in MM that drive drug sensitivity and clinic outcome remain elusive. Results Single-cell RNA sequencing was applied to study tumor heterogeneity and molecular dynamics in 10 MM individuals before and after 2 cycles of bortezomib–cyclophosphamide–dexamethasone (VCD) treatment, with 3 healthy volunteers as controls. We identified that unfolded protein response and metabolic-related program were decreased, whereas stress-associated and immune reactive programs were increased after 2 cycles of VCD treatment. Interestingly, low expression of the immune reactive program by tumor cells was associated with unfavorable drug response and poor survival in MM, which probably due to downregulation of MHC class I mediated antigen presentation and immune surveillance, and upregulation of markers related to immune escape. Furthermore, combined with immune cells profiling, we uncovered a link between tumor intrinsic immune reactive program and immunosuppressive phenotype in microenvironment, evidenced by exhausted states and expression of checkpoint molecules and suppressive genes in T cells, NK cells and monocytes. Notably, expression of YBX1 was associated with downregulation of immune activation signaling in myeloma and reduced immune cells infiltration, thereby contributed to poor prognosis. Conclusions We dissected the tumor and immune reprogramming in MM during targeted therapy at the single-cell resolution, and identified a tumor program that integrated tumoral signaling and changes in immune microenvironment, which provided insights into understanding drug sensitivity in MM.

Abstract The rapid rollout of the COVID-19 vaccine raises the question of whether and when the ongoing pandemic could be eliminated with vaccination and non-pharmaceutical interventions (NPIs). Despite advances in the impact of NPIs and the conceptual belief that NPIs and vaccination control COVID-19 infections, we lack evidence to employ control theory in real-world social human dynamics in the context of disease spreading. We bridge the gap by developing a new analytical framework that treats COVID-19 as a feedback control system with the NPIs and vaccination as the controllers and a computational model that maps human social behaviors into input signals. This approach enables us to effectively predict the epidemic spreading in 381 Metropolitan statistical areas (MSAs) in the US by learning our model parameters utilizing the time series NPIs (i.e., the stay-at-home order, face-mask wearing, and testing) data. This model allows us to optimally identify three NPIs to predict infections accurately in 381 MSAs and avoid over-fitting. Our numerical results demonstrate our approach’s excellent predictive power with R 2 > 0.9 for all the MSAs regardless of their sizes, locations, and demographic status. Our methodology allows us to estimate the needed vaccine coverage and NPIs for achieving R e to a manageable level and how the variants of concern diminish the likelihood for disease elimination at each location. Our analytical results provide insights into the debates surrounding the elimination of COVID-19. NPIs, if tailored to the MSAs, can drive the pandemic to an easily containable level and suppress future recurrences of epidemic cycles.

ObjectiveWe aimed to investigate the effect of Cyclocarya paliurus leaves extracts (CP) on glucose and blood lipid metabolism and its relationship with intestinal flora in type 2 diabetes mellitus (T2DM) patients.MethodsIn this open-label, 84-day randomized controlled trial, a total of 38 T2DM patients were randomly assigned to the CP group or the Glipizide group (G group) in a 2:1 ratio. T2DM-associated metabolic phenotypes, gut microbiota and metabolites including short-chain fatty acids (SCFAs) and bile acids (BAs) were detected.ResultsAt the end of intervention, CP, like Glipizide, significantly improved HbA1c level and other glucose metabolism parameters (fasting plasma glucose (FBG), 2-hour post-meal blood glucose (2hPBG), the area under curve of oral glucose tolerance test glucose (OGTT glucose AUC)). Moreover, CP also resulted in the significant improvement in the levels of blood lipid and blood pressure. Notably, the improvement in blood lipid(triglycerides (TG) and high-density lipoprotein cholesterol (HDL-c)) and blood pressure (diastolic blood pressure (DBP)) was significantly greater in the CP group compared with the G group. Furthermore, the liver and kidney function parameters did not significantly change in both CP group and the G group over the 84-day period. Additionally, the enrichment of potentially beneficial bacteria (Faecalibacterium and Akkermansia), SCFAs and unconjugated BAs and the depletion of potential pathogenic bacteria (Prevotella_9) and conjugated BAs were observed in the CP group, while the abundances of the gut microbial were kept stable in the G group after intervention.ConclusionCP displays a more beneficial effect in the alleviation of T2DM-associated metabolic phenotypes than glipizide by regulating gut microbiota and metabolites in T2DM patients, with no significant effects on liver and kidney function.

In view of the demand for abnormal disposal of safety and stability control devices in the actual operation of the power grid, a structured modeling method for the disposal plan of safety and control devices is proposed with the safety and control fault disposal plan as the research object. Based on the specified grid operating conditions, it automatically matches and identifies the disposal plans and generates corresponding disposal suggestions, as well as provides technical support for rapid handling and maintenance arrangements of safety control device abnormalities, so as to effectively improve the efficiency and management level of safety control device operation and maintenance.

Abstract Backgrounds Decreased cytotoxicity of natural killer (NK) cells has been shown in multiple myeloma (MM). However, the underlying molecular mechanisms remain unclear. Here, by using single‐cell RNA sequencing analysis and in vitro experiments, we aim to uncover and validate molecularly distinctive insights into identifying regulators for NK cell exhaustion and provide potential targets for novel immune therapies in MM. Methods Single‐cell RNA sequencing was conducted in the bone marrow and peripheral blood samples from 10 newly diagnosed MM patients and three healthy volunteers. Based on the cluster‐defining differentially expressed genes, we named and estimated functional states of each cluster via bioinformatics analyses. Functional significance of key findings obtained from sequencing analysis was examined in a series of in vitro experiments, including luciferase reporter assay, lentiviral expression vector construction, NK cell transfection, RT‐qPCR, flow cytometry, and cytotoxicity assay. Results We classified NK cells into seven distinct clusters and confirmed that a subset of ZNF683+ NK cells were enriched in MM patients with ‘exhausted’ transcriptomic profile, featuring as decreased expression of activating receptors and cytolytic molecules, as well as increased expression of inhibitory receptors. Next, we found a significant downregulation of SH2D1B gene that encodes EAT‐2, an adaptor protein of activating receptor SLAMF7, in ZNF683+ NK cells from MM patients versus healthy volunteers. We further proved that ZNF683 transfection in NK cells significantly downregulated SH2D1B expression via directly binding to the promoter of SH2D1B, leading to NK cell cytotoxic activity impairment and exhausted phenotypes acquisition. In contrast, ZNF683 knockout in NK cells from MM patients increased cytotoxic activity and reversed NK cell exhaustion. Conclusions In summary, our findings uncover an important mechanism of ZNF683+ NK cell exhaustion and suggest that transcriptional suppressor ZNF683 as a potential useful therapeutic target in immunotherapy of MM.

In recent years, the rapid development of artificial intelligence has increased the power of personalized learning. This study aimed to provide personalized intervention for each group participating in computer-supported collaborative learning. The personalized intervention adopted a deep neural network model, Bidirectional Encoder Representations from Transformers (BERT), to automatically classify online discussion transcripts and provide classification results in real time. Personalized feedback and recommendations were automatically generated from the classification results. A quasi-experimental research design was adopted to examine the effects of the proposed personalized intervention approach on collaborative knowledge building, group performance, socially shared metacognitive regulation, and cognitive load. Sixty-six college students participated in this study and were randomly assigned to the experimental and control groups. For online collaborative learning, students in the experimental group adopted the personalized intervention approach, whereas those in the control group used the conventional approach. Both quantitative and qualitative research methods were adopted to analyze data. The results indicated significant differences in the level of collaborative knowledge building and group performance between the experimental and control groups. Furthermore, the experimental group demonstrated more socially shared metacognitive regulation than the control group. There was no significant difference in cognitive load between the experimental and control groups. The results obtained from interviews were consistent with the quantitative data. The main findings together with the implications for practitioners are discussed in depth.

Network approaches are key to understand epidemic spreading, inherently driven by human mobility patterns and constrained by transport systems. In this work, the authors develop a country distance framework to capture the spread of COVID-19 on top of the airline network, analyzing the effectiveness of mobility restrictions in the presence of multiple outbreaks and suggesting strategies for optimized coordinated travel restrictions.

The therapeutic efficiency of active targeting nanoparticulate drug delivery systems (nano-DDS) is highly compromised by the plasma proteins adsorption on nanoparticles (NPs) surface, which significantly hinders cell membrane receptors to recognize the designed ligands, and provokes the off-target toxicity and rapid clearance of NPs in vivo. Herein, we report a novel dihydroartemisinin (DHA)-decorating nano-DDS that in situ specifically recruits endogenous apolipoprotein E (apoE) on the NPs surface. The apoE-anchored corona is able to prolong PLGA-PEG2000-DHA (PPD) NPs circulation capability in blood, facilitate NPs accumulating in tumor cells by the passive enhanced permeability and retention (EPR) effect and low-density lipoprotein receptor (LDLr)-mediated target transport, and ultimately improve the in vivo antitumor activity. Our findings demonstrate that the strategy of in situ regulated apoE-enriched corona ensures NPs an efficient LDLr-mediated tumor-homing chemotherapy.

Ershiwuwei Lvxue Pill (ELP, མགྲིན་མཚལ་ཉེར་ལྔ།), a traditional Tibetan medicine preparation, has been used hundreds of years for the clinical treatment of rheumatoid arthritis (RA) in the highland region of Tibet, China. Nevertheless, its chemical composition and therapeutic mechanism are unclear. This study aimed to uncover the potentially effective components of ELP and the pharmacological mechanisms against RA by combing UPLC-Q-TOF/MS and network pharmacology. In this study, 96 compounds of ELP were identified or tentatively characterized based on UPLC-Q-TOF/MS analysis. Then, a total of 22 potential bioactive compounds were screened by TCMSP with oral bioavailability and drug-likeness. Preliminarily, 10 crucial targets may be associated with RA through protein-protein interaction network analysis. The functional enrichment analysis indicated that ELP exerted anti-RA effects probably by synergistically regulating many biological pathways, such as PI3K-Akt, Cytokine-cytokine receptor interaction, JAK-STAT, MAPK, TNF, and Toll-like receptor signaling pathway. In addition, good molecular docking scores were highlighted between five promising bioactive compounds (ellagic acid, quercetin, kaempferol, galangin, coptisine) and five core targets (PTGS2, STAT3, VEGFA, MAPK3, TNF). Overall, ELP can exert its anti-RA activity via multicomponent, multitarget, and multichannel mechanisms of action. However, further studies are needed to validate the biological processes and effect pathways of ELP.

Although it is reported that the targeting ability of hyaluronic acid (HA)-based nanoparticles (NPs) is molecular weight (MW) dependent, the influence of HA MW on targeting efficiency of HA-functionalized NPs and the underlying mechanism remain elusive. In this study, we constituted three HA-functionalized Dox-loaded NPs (Dox/HCVs) different HA MWs (7, 63, and 102 kDa) and attempted to illustrate the effects of HA MW on the targeting efficiency. The three Dox/HCVs had similar physiochemical and pharmaceutical characteristics, but showed different affinity to CD44 receptor. Furthermore, Dox/HCV-63 exerted the best targeting effect and the highest cytotoxicity compared with Dox/HCV-7 and Dox/HCV-102. It was interesting to found that both the HA-CD44 binding affinity and induced CD44 clustering by HA-based NPs were HA MW-dependent, the two of which determine the apparent targeting efficacy of Dox/HCV NPs in the conflicting directions. Those results laid a good foundation for rationally designing HA-based NPs in cancer therapy. Keywords: Hyaluronic acid, Molecular weight, Active targeting efficiency, CD44 receptor, Potential mechanism

Hyaluronic acid (HA) is a natural ligand of tumor-targeted drug delivery systems (DDS) due to the relevant CD44 receptor overexpressed on tumor cell membranes. However, other HA receptors (HARE and LYVE-1) are also overexpressing in the reticuloendothelial system (RES). Therefore, polyethylene glycol (PEG) modification of HA-based DDS is necessary to reduce RES capture. Unfortunately, pegylation remarkably inhibits tumor cellular uptake and endosomal escapement, significantly compromising the in vivo antitumor efficacy. Herein, we developed a Dox-loaded HA-based transformable supramolecular nanoplatform (Dox/HCVBP) to overcome this dilemma. Dox/HCVBP contains a tumor extracellular acidity-sensitive detachable PEG shell achieved by a benzoic imine linkage. The in vitro and in vivo investigations further demonstrated that Dox/HCVBP could be in a ''stealth'' state at blood stream for a long circulation time due to the buried HA ligands and the minimized nonspecific interaction by PEG shell. However, it could transform into a ''recognition'' state under the tumor acidic microenvironment for efficient tumor cellular uptake due to the direct exposure of active targeting ligand HA following PEG shell detachment. Such a transformative concept provides a promising strategy to resolve the dilemma of natural ligand-based DDS with conflicting two processes of tumor cellular uptake and in vivo nonspecific biodistribution. KEY WORDS: Hyaluronic acid, Benzoic imine linkage, Active-targeting, Cancer therapy, Natural ligand, Supramolecular nanoplat-form, Transformative nanoparti-cles, PEG dilemma

Imbalance of gut microbiota can induce or aggravate intestinal inflammation. To enhance our understanding of the molecular mechanisms of gut microbiota and inflammatory bowel disease (IBD), we studied the role of repulsive guidance molecule b (RGMb) in gut microbiota and colitis in mice. We generated Rgmb knockout mice and inducible Rgmb knockout mice and induced colitis using dextran sulfate sodium (DSS) in these mice. 16S ribosomal RNA (rRNA) high-throughput sequencing was performed to acquire the gut microbiota composition and abundance. We found that Rgmb deficiency significantly altered the diversity of gut microbiota and also induced dysbiosis. In sharp contrast to the balanced distribution of various bacteria in control mice, Prevotellaceae was almost exhausted in Rgmb-deficient mice under both basal and inflammatory conditions. Correlation analysis indicated that Prevotellaceae was negatively associated with inflammation in Rgmb-deficient mice with colitis. Similar results were obtained at the early inflammatory stage of colitis associated colon cancer (CAC). Taken together, our results reveal that Rgmb deficiency leads to dysbiosis of predominant gut microbiota under basal and inflammatory conditions. Rgmb-deficiency-mediated Prevotellaceae loss may render mice more susceptible to intestinal inflammation. Therefore, RGMb may be a novel potential target for reconstruction of the gut microbiota for the treatment of IBD.

A large body of literature has examined specificity and transfer of perceptual learning, suggesting a complex picture. Here, we distinguish between transfer over variations in a task-relevant feature (e.g., transfer of a learned orientation task to a different reference orientation) and transfer over a task-irrelevant feature (e.g., transfer of a learned orientation task to a different retinal location or different spatial frequency), and we focus on the mechanism for the latter. Experimentally, we assessed whether learning a judgment of one feature (such as orientation) using one value of an irrelevant feature (e.g., spatial frequency) transfers to another value of the irrelevant feature. Experiment 1 examined whether learning in eight-alternative orientation identification with one or multiple spatial frequencies transfers to stimuli at five different spatial frequencies. Experiment 2 paralleled Experiment 1, examining whether learning in eight-alternative spatial-frequency identification at one or multiple orientations transfers to stimuli with five different orientations. Training the orientation task with a single spatial frequency transferred widely to all other spatial frequencies, with a tendency to specificity when training with the highest spatial frequency. Training the spatial frequency task fully transferred across all orientations. Computationally, we extended the identification integrated reweighting theory (I-IRT) to account for the transfer data (Dosher, Liu, & Lu, 2023; Liu, Dosher, & Lu, 2023). Just as location-invariant representations in the original IRT explain transfer over retinal locations, incorporating feature-invariant representations effectively accounted for the observed transfer. Taken together, we suggest that feature-invariant representations can account for transfer of learning over a task-irrelevant feature.

Chemical pesticides play crucial roles in the management of crop diseases and pests. However, excessive and irrational use of pesticides has become a major concern and obstacle to sustainable agriculture. As a result, the quality and security of agricultural products are reduced, and the ecological and environmental integrities are threatened. Recently, environment-friendly pest management measures have been introduced and adopted to manage rice insect pests and reduce the use of insecticides. This paper reviewed the advancements in development and application of non-chemicaltechnologies for insect pest management during rice production in China.

Perceptual learning is a multifaceted process, encompassing general learning, between-session forgetting or consolidation, and within-session fast relearning and deterioration. The learning curve constructed from threshold estimates in blocks or sessions, based on tens or hundreds of trials, may obscure component processes; high temporal resolution is necessary. We developed two nonparametric inference procedures: a Bayesian inference procedure (BIP) to estimate the posterior distribution of contrast threshold in each learning block for each learner independently and a hierarchical Bayesian model (HBM) that computes the joint posterior distribution of contrast threshold across all learning blocks at the population, subject, and test levels via the covariance of contrast thresholds across blocks. We applied the procedures to the data from two studies that investigated the interaction between feedback and training accuracy in Gabor orientation identification over 1920 trials across six sessions and estimated learning curve with block sizes L = 10, 20, 40, 80, 160, and 320 trials. The HBM generated significantly better fits to the data, smaller standard deviations, and more precise estimates, compared to the BIP across all block sizes. In addition, the HBM generated unbiased estimates, whereas the BIP only generated unbiased estimates with large block sizes but exhibited increased bias with small block sizes. With L = 10, 20, and 40, we were able to consistently identify general learning, between-session forgetting, and rapid relearning and adaptation within sessions. The nonparametric HBM provides a general framework for fine-grained assessment of the learning curve and enables identification of component processes in perceptual learning.