Previously, we developed a novel model for anxiety during motivated behavior by training rats to perform a task where actions executed to obtain a reward were probabilistically punished and observed that after learning, neuronal activity in the ventral tegmental area (VTA) and dorsomedial prefrontal cortex (dmPFC) represent the relationship between action and punishment risk (Park & Moghaddam, 2017). Here we used male and female rats to expand on the previous work by focusing on neural changes in the dmPFC and VTA that were associated with the learning of probabilistic punishment, and anxiolytic treatment with diazepam after learning. We find that adaptive neural responses of dmPFC and VTA during the learning of anxiogenic contingencies are independent from the punisher experience and occur primarily during the peri-action and reward period. Our results also identify peri-action ramping of VTA neural calcium activity, and VTA-dmPFC correlated activity, as potential markers for the anxiolytic properties of diazepam. Methods Subjects Male and female Long-Evans (bred in house n=8) and Sprague-Dawley (Charles River n=5) rats were used. Animals were pair-housed on a reverse 12 h:12 h light/dark cycle. All experimental procedures and behavioral testing were performed during the dark (active) cycle. All studies included both strains of male (n=7) and female (n=6) rats. All experimental procedures were approved by the OHSU Institutional Animal Use and Care Committee and were conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. Initial Training & Punishment Risk Task (PRT) The PRT follows previously published methods (Park & Moghaddam, 2017; Chowdhury et al., 2019). Rats were trained to make an instrumental response to receive a 45-mg sugar pellet (BioServe) under fixed ratio one schedule of reinforcement (FR1). The availability of the nosepoke for reinforcement was signaled by a 5-s tone. After at least three FR1 training sessions, PRT sessions began. PRT sessions consisted of three blocks of 30 trials each. The action-reward contingency remained constant, with one nose-poke resulting in one sugar pellet. However, there was a probability of receiving a footshock (300 ms electrical footshock of 0.3 mA) after the FR1 action, which increased over the blocks (0%, 6%, or 10% in blocks 1, 2 and 3, respectively). To minimize generalization of the action-punishment contingency, blocks were organized in an ascending footshock probability with 2-min timeouts between blocks. Punishment trials were pseudo-randomly assigned, with the first footshock occurring within the first five trials. All sessions were terminated if not completed in 180 mins. Fiber Photometry Analysis Peri-event analysis: Signals from the 465 (GCaMP6s) and 560 (tdTomato) streams were processed in Python (Version 3.7.4) using custom-written scripts similar to previously published methods (Jacobs & Moghaddam, 2020). Briefly, 465 and 560 streams were low pass filtered at 3 Hz using a butterworth filter and subsequently broken up based on the start and end of a given trial. The 560 signal was fitted to the 465 using a least-squares first order polynomial and subtracted from 465 signal to yield the change in fluorescent activity (ΔF/F= 465 signal - fitted 560 signal/ fitted 560 signal). Peri-event z-scores were computed by comparing the ΔF/F after the behavioral action to the 4-2 sec baseline ΔF/F prior to a given epoch. To investigate potential different neural calcium responses to receiving the footshock vs. anticipation, punished (i.e. shock) trials and unpunished trials were separated. Trials with a z-score value > 40 were excluded. From approximately 3,000 trials analyzed, this occurred on < 1% of trials. Area under the curve (AUC) analyses: To represent individual data we calculated the AUCs for each subject. To quantify peri-cue and peri-action changes we calculated a change or summation score between 1 sec before (pre-event) and 1 sec after (post-event) cue onset or action execution. For the reward period, we calculated a change score by comparing 2 sec after reward delivery to the 1 sec prior to reward delivery. For punished trials, response to footshock was calculated as the change from 1 sec following footshock delivery compared to the 1 sec before footshock. Outliers were removed using GraphPad Prism’s ROUT method (Q=1%; Motulsky & Brown, 2006) which removed only three data points from the analysis. Time Lagged Cross-Correlation Analysis: Cross-correlation analysis has been used to identify networks from simultaneously measured fiber photometry signals (Sych et al., 2019). For rats with properly placed fibers in the dmPFC and VTA, correlations between photometry signals arising in the VTA and dmPFC were calculated for the peri-action, peri-footshock and peri-reward periods using the z-score normalized data. The following equation was used to normalize covariance scores for each time lag to achieve a correlation coefficient between -1 and 1: Coef = Cov/(s1*s2*n) Where Cov is the covariance from the dot product of the signal for each timepoint, s1 and s2 are the standard deviations of the dmPFC and VTA streams, respectively, and n is the number of samples. An entire cross-correlations function was derived for each trial and epoch. Comparison to Electrophysiology Results: Fiber photometry data for the third PRT session were compared to the average of the 50 msec binned single unit data (see Figure 4 of Park & Moghaddam, 2017). This third PRT session corresponds to the session electrophysiology data were collected from. To overlay data from the two techniques, data were lowpass filtered at 3 Hz and photometry data were downsampled to 20 Hz (to match the 50 msec binning). Data from both streams were then min-max normalized between 0 and 1 at the corresponding cue and action+reward epochs. To assess the similarity of the two signals, we performed a Pearson correlation analysis between the normalized single unit and fiber photometry data for cue or action+reward epochs at each risk block, as well as between randomly shuffled photometry signals with single-unit response as a control. For significant Pearson correlations, we performed cross-correlation analysis (see above) to investigate if the photometry signal lagged behind electrophysiology given the slower kinetics of GCAMP6 compared to single-unit approaches (Chen et al., 2013). Statistical Analysis For FR1 training, trial completion was measured as the number of food pellets earned. Data were assessed for the first 3-4 training sessions. Action and reward latencies were defined as time from cue onset to action execution or from food delivery until retrieval, respectively. Values were assessed using a mixed-effects model with the training as a factor and post-hoc tests were performed using the Bonferroni correction where appropriate. For the PRT, trial completion was measured as the percentage of completed trials (of the 30 possible) for each block. Action latencies were defined as time from cue onset to action execution. Data were analyzed using a two-way RM ANOVA or mixed effects model. Because there were missing data for non-random reasons (e.g. failure to complete trials in response to punishment risk) we took the average of risk blocks (blocks 2 and 3) and the no-risk block (block 1) to permit repeated measures analysis. We used mixed effects model if data were missing for random reasons. Risk and session were used as factors and post-hoc tests were performed using the Bonferroni correction where appropriate. When only two groups were compared a paired t-test or Wilcoxon test was performed after checking normality assumption through the Shapiro-Wilk test. To assess changes in neural calcium activity, we utilized a permutation-based approach as outlined in (Jean-Richard-dit-Bressel et al., 2020) using Python (Version 3). An average response for each subject for a given time point in the cue, action, or reward delivery period was compared to either the first PRT or saline session. For each time point, a null distribution was generated by shuffling the data, randomly selecting the data into two groups, and calculating the mean difference between groups. This was done 1,000 times for each time-point and a p-value was obtained by determining the percentage of times a value in the null distribution of mean differences was greater than or equal to the observed difference in the unshuffled data (one-tailed for comparisons to 0% risk and FR1 data, two-tailed for all other comparisons). To control for multiple comparisons we utilized a consecutive threshold approach based on the 3 Hz lowpass filter window (Jean-Richard-dit-Bressel et al., 2020; Pascoli et al., 2018), where a p-value < 0.05 was required for 14 consecutive samples to be considered significant. To assess AUC changes in photometry data, we compared all risk blocks and all sessions using ANOVA with factors risk block and session. Because not all subjects completed learning and diazepam data, we used an ordinary two-way ANOVA. Significant main effects and interactions were assessed with post-hoc Bonferroni multiple comparison tests. To assess correlated activity changes as a function of risk or session, we took the peak and 95% confidence interval for the overall cross-correlation function. These values were compared by a two-way ANOVA with factors risk and session and utilized a post-hoc Bonferroni correction. Other than permutation tests, all statistical tests were done using GraphPad Prism (Version 8) and an α of 0.05. Results for all statistical tests and corresponding figures can be found in Table 1 or supplemental figures. Excluded Data Outliers from latency analysis were removed when a data point was > 5 SDs above the mean across all blocks. This removed one data point from the analysis. In FR1 studies, data from one rat’s third and fourth session were excluded because

To determine how DEX treatment in WT and MKO mice leads to muscle atrophy, we measured muscle protein fractional synthesis rates using shot-gun LC-MS/MS analysis after in vivo 2H2O labeling. Mice were treated with PBS or DEX for 10 days. During the final 7 days, mice were also labeled with 2H2O. The sample size for wild type controls treated with PBS (WT-PBS, n=6), wild type treated with DEX (WT-DEX, n=6), MKO treated with PBS (MKO-PBS, n=5) and MKO treated with DEX (MKO-DEX, n=5). The rational for biological replicates of n=5-6 were used to ensure accurate comparison among each group. Biological replicates were chosen to asses biological variability rather than sole reliance on technical replicates. Controls included WT-PBS treated mice (n=6). Two separate dynamic proteomic experiments were completed and combined for final data analysis. This consisted of experiment 1 that included WT-PBS (n=4) and WT-DEX (n=4). Experiment 2 included the final biological replicates of WT-PBS (n=2), WT-DEX (n=2), MKO-PBS (n=5) and MKO-DEX (n=5). The data sets were combined and annotated as described by the following. We used several filtering criteria for inclusion of protein kinetic data that were included in the comparisons: more than one peptide had to be present for any protein; each peptide had to meet analytic accuracy criteria for fractional mass isotopomer abundances and for reproducibility; a protein had to be present in at least 3 animals per group; and these criteria had to be met for the protein in all 4 groups. The number of proteins meeting these criteria was 81 in WT-PBS, 88 in WT-DEX, 124 in MKO-PBS and 134 in MKO-DEX and 57 proteins met these criteria in gastrocnemius muscle in all 4 groups. To determine the difference in protein fractional synthesis rates (f), four groups were categorized as the following: WT-PBS, WT-DEX, MKO-PBS and MKO-DEX. The mean, medium, and standard deviation for each protein (n≥3 for each group) were calculated, and a 2X2 ANOVA analysis (InfernoRDN) was performed to compare the treatment, genotype, and interaction effects. Protein fractional synthesis were averaged within groups and the percent changes were compared across each group. An increase or decrease in fractional synthesis was assessed as ± 0.0 %. A binomial distribution statistical analysis was used to calculate the significance of the relative percent increase or decrease in GA protein fractional synthesis. Average protein fractional synthesis was also assessed by ANOVA followed by the Benjamini and Hochberg test for multiple comparisons (FDR=0.05, p ≤0.05) using GraphPad Prism version 8.0 for Mac, GraphPad Software, La Jolla California USA.This is the combined data filter for protein kinetics data for experiment 1 and 2.

This dataset collected the original data for the manuscript, including the mass data from UPLC-IM-QTOF-MS, the the data from animal experiments. Quantitative analysis was performed with GraphPad Prism 10, and data were expressed as mean ± SD. For multiple comparison, one-way ANOVA with Tukey's post hoc test was employed, with statistical significance established at p < 0.05. Metabolomic analysis identified 23 differential components between the two forms of PMR with 2,3,5,4'-Tetrahydroxystilbene-2-O-β-D-glucoside (TSG) recognized as the principal component contributing to their differences. Network pharmacology and molecular docking analyses revealed that TSG exhibited a strong binding affinity with EGFR. In vivo experiments confirmed the distinct pharmacological efficacy of both PMRs in an osteoporosis model. Micro-CT demonstrated that processed PMR in conjunction with a high dose of TSG, significantly increased the bone mineral density, PINP levels, and ALP activity while markedly reduced the structure model index and CTX-I levels. These interventions showed potential in suppressing the expression of MMP9 and TRAP, while promoting the expression of ALP, osteopontin, and Runx2. Moreover, these treatments resulted in an upregulation of EGFR, p-PI3K, and p-AKT expression. In contrast, neither low nor high doses of raw PMR as well as a low dose of TSG demonstrated significant effects on these parameters.

GraphPad Prism® Software was used to perform a two-way ANOVA of the tumor growth data after treatment (this data is graphed as Tumor Volume from 0-28 days post-treatment in Figure 3.A). Treatment A (first column) was compared to treatment B (second column) and the time is took (in days-post-treatment) to reach a significance level of p

Medians were calculated in Graphpad prism Version 5. Statistical significance was determined using Kruskal Wallis H test. Comparisons were done using Dunns Multiple comparison test.

BackgroundThe BD-FACSPresto™ CD4 is a new, point-of-care (POC) instrument utilising finger-stick capillary blood sampling. This study evaluated its performance against predicate CD4 testing in South Africa.MethodsPhase-I testing: HIV+ patient samples (n = 214) were analysed on the Presto™ under ideal laboratory conditions using venous blood. During Phase-II, 135 patients were capillary-bled for CD4 testing on FACSPresto™, performed according to manufacturer instruction. Comparative statistical analyses against predicate PLG/CD4 method and industry standards were done using GraphPad Prism 6. It included Bland-Altman with 95% limits of agreement (LOA) and percentage similarity with coefficient of variation (%CV) analyses for absolute CD4 count (cells/μl) and CD4 percentage of lymphocytes (CD4%).ResultsIn Phase-I, 179/217 samples yielded reportable results with Presto™ using venous blood filled cartridges. Compared to predicate, a mean bias of 40.4±45.8 (LOA of -49.2 to 130.2) and %similarity (%CV) of 106.1%±7.75 (7.3%) was noted for CD4 absolute counts. In Phase-2 field study, 118/135 capillary-bled Presto™ samples resulted CD4 parameters. Compared to predicate, a mean bias of 50.2±92.8 (LOA of -131.7 to 232) with %similarity (%CV) 105%±10.8 (10.3%), and 2.87±2.7 (LOA of -8.2 to 2.5) with similarity of 94.7±6.5% (6.83%) noted for absolute CD4 and CD4% respectively. No significant clinical differences were indicated for either parameter using two sampling methods.ConclusionThe Presto™ produced remarkable precision to predicate methods, irrespective of venous or capillary blood sampling. A consistent, clinically insignificant over-estimation (5–7%) of counts against PLG/CD4 and equivalency to FACSCount™ was noted. Further field studies are awaited to confirm longer-term use.

An unpaired t-test was performed to compare viral load suppression and CD4 count between clients on EFV based regimen and DTG based regimen at 24 weeks, 48 weeks and 96 weeks. This test was also performed when comparing viral load and CD4 count of clients switched from EFV based regimen to DTG based regimen. In addition, to compare viral and CD4 count within treatment groups on the two regimens at different periods, the Friedman’s test was used; followed by a Pairwise comparison using the Dunn Post Hoc Test with the Bonferroni correction. To assess if clients with viral suppression remain virally suppressed after switching from EFV-based regimens to DTG based regimens VL and CD4+ were analysed 24 weeks after the switch to DTG based regimens and then at 48 weeks. Records of clients’ results before the switch to DTG based regimens were compared to records after switching to DTG based regimens and reported as percentages and proportions. To assess adherence and compliance to treatment, dates of the review were noted. Common side effects of naïve clients on both regimens’ data were collected from records of clients on both regimens and were described in proportions and assessed with the chi-square test; Side effects of experienced clients before and after the switch to DTG-based regimens, data was described using proportions and assessed with the chi-square test.Data were analysed using Microsoft Excel spreadsheets and Graph Pad Prism 7 statistical package. For statistical significance, a p-value less than 0.05 was accepted.

Though dependent on genetic anomalies, clinical manifestations of the human autoimmune disease systemic lupus erythematosus (lupus) can be triggered by environmental exposures including inhalation toxicants such as crystalline silica dust (cSiO2), tobacco smoke, and ambient air particles. Prednisone, a glucocorticoid (GC), is a keystone therapy for managing lupus flaring and progression, however, long-term use is associated with many adverse side effects. Here, we characterized the dose-dependent immunomodulation and toxicity of prednisone in a preclinical model that emulates onset and progression of cSiO2-triggered lupus. Two cohorts of 6-wk-old female NZBWF1 mice were fed either control AIN-93G diet or one of three AIN-93G diets containing prednisone at 5, 15, or 50 mg/kg diet which span human equivalent oral doses (HED) currently considered to be low (PL; 5 mg/d HED), moderate (PM; 14 mg/d HED), or high (PH; 46 mg/d HED), respectively. At 8 wk of age, mice were intranasally instilled with either saline vehicle or 1 mg cSiO2 once weekly for 4 wk. The experimental plan was to 1) terminate one cohort of mice (n=8/group) 14 wk after the last cSiO2 instillation for pathology and autoimmunity assessment and 2) to maintain a second cohort (n=9/group) to monitor glomerulonephritis development and survival. Mean blood concentrations of prednisone’s chief active metabolite, prednisolone, in mice fed PL, PM, and PH diets were 27, 105, 151 ng/ml, respectively, which are consistent with levels observed in human blood ≤ 12 h after single bolus treatments with equivalent prednisone doses. Results from the first cohort revealed that consumption of PM but not PL diet significantly reduced cSiO2-induced pulmonary ectopic lymphoid structure formation, nuclear-specific AAb production, and inflammation/autoimmune gene expression in the lung, splenomegaly, and glomerulonephritis in the kidney. Relative to GC-associated toxicity, PM but not PL diet elicited muscle wasting, but these diets did not affect bone density or cause glucosuria. Importantly, neither PM nor PL diet influenced latency of cSiO2-accelerated death. PH-fed mice in both cohorts displayed robust GC-associated toxicity including body weight loss, reduced muscle mass, and hyperglycemia 7 wk after the final cSiO2 instillation requiring their early removal from the study. Taken together, our results demonstrate that while moderate doses of prednisone can reduce certain pathological endpoints of cSiO2-induced autoimmunity in lupus-prone mice, these ameliorative effects come with unwanted GC toxicity and, crucially, none of these three doses extended survival time. Methods NanoString Autoimmune Profiling RNA was extracted from lungs, kidneys, and blood with RNeasy Mini Kits with DNase treatment (Qiagen, Valencia, CA). RNA was dissolved in nuclease-free water, quantified with Qubit (Thermo Fisher Scientific), and integrity verified with a TapeStation (Agilent Technologies). Samples (RNA integrity > 8) were analyzed with NanoString Autoimmune Gene Expression assay (XT-CSO-MAIP1-12, NanoString Technologies, Seattle, WA) at the MSU Genomics Core. Assays were performed and quantified on the nCounter MAX system, sample preparation station, and digital analyzer (NanoString Technologies) according to the manufacturer’s instructions. Raw gene expression data were analyzed using NanoString’s software nSolver v3.0.22 with the Advanced Analysis Module v2.0. Background subtraction was performed using the eight negative controls included with the module. Genes with counts below a threshold of 2σ of the mean background signal were excluded from subsequent analysis. Data normalization was performed on background-subtracted samples using internal positive controls and selected housekeeping genes that were identified with the geNorm algorithm (https://genorm.cmgg.be/). Differential gene expression analyses were performed using the nSolver Advanced Analysis Module, which employs several multivariate linear regression models (mixture negative binomial, simplified negative binomial, or log-linear model) to identify significant genes. Resulting p values were adjusted using the Benjamini-Hochberg (BH) method to control the false discovery rate. A statistically significant difference in gene expression was defined as 1.5-fold change in expression (log2 > 0.58 or < -0.58) with BH q < 0.05. Four pairwise comparisons within each time point for each tissue examined were determined a priori, as follows: cSiO2/P0 vs VEH/P0, cSiO2/PL vs cSiO2/P0, cSiO2/PM vs cSiO2/P0, and cSiO2/PM vs cSiO2/PL. Venn diagrams of significant differentially expressed genes were generated using BioVenn. To assess the impact of experimental diets on annotated gene sets, global and directed significance scores were calculated for each pathway at each time point. The global score estimates the cumulative evidence for the differential expression of genes in a pathway. Directed significance scores near zero indicate that a pathway may have many highly regulated genes, but no apparent tendency for those genes to be over- or under-expressed collectively. As a complementary method for comparing pathways and discriminating between experimental groups, pathway Z scores were calculated as the Z-scaled first principal component of the pathway genes’ normalized expression. ClustVis was used to perform unsupervised hierarchical cluster analyses (HCC) and principal components analyses (PCA) using log2 transcript count data for DEGs. Spearman rank correlations were performed to examine overall patterns in the gene expression profiles using the pathway Z score compared to other biomarkers of disease in lung or kidney tissues at 14 weeks PI. A significant correlation was inferred when ρ > 0.5 or <-0.5 and p < 0.05. Network analyses for interactions among significant genes were performed using STRING database version 11.5 (http://string-db.org/), with a minimum interaction score > 0.05 and cluster identification using the Markov Cluster (MCL) algorithm with inflation parameter of 1.5. Networks generated by STRING were visualized with Cytoscape v. 3.9. The NanoString nSolver Advanced Analysis software employs the method described by Danaher to measure the abundance of various immune cell populations using marker genes that are expressed stably and specifically in particular cell types. Cell type scores were calculated as the average log-scale normalized expression of their characteristic genes. Relative cell type measurements were based on the total population of infiltrating lymphocytes, which is useful in a sample of heterogenous mix of cell types. Only cell types that exceeded the quality control analysis for correlation of marker gene expression are reported. Statistical Analysis All data were analyzed, and statistical tests were performed using Prism 9 (GraphPad Prism v 9.2, San Diego, CA) except for the NanoString gene expression data discussed above. Data were assessed for outliers using the Grubb’s outlier test (with Q = 1%) and for normality using the Shapiro-Wilk test (p < 0.01). Data of histopathological endpoints were analyzed using an unpaired one-tailed t-test to detect cSiO2-induced inflammation and autoimmunity in lupus-prone mice (VEH/P0 vs cSiO2/P0) and a One-Way ANOVA with Dunnett’s post-hoc test to address our hypothesis that dietary prednisone would dose-dependently suppress cSiO2-triggered responses (cSiO2/P0 vs cSiO2/PL or cSiO2/PM). Non-normal and semi-quantitative data were analyzed using the nonparametric Mann-Whitney U test (for VEH/P0 vs cSiO2/P0) and the nonparametric Kruskal-Wallis test with a Dunn’s post-hoc test (cSiO2/P0 vs cSiO2/PL or cSiO2/PM). Data are presented as mean ± standard error of the mean (SEM), with a p-value ≤ 0.05 being considered as statistically significant.

This study conducts an empirical experiment with 140 subjects and employs statistical analysis using SPSS, GraphPad Prism, and Cursor, confirming four findings: 1. Chinese learners of English show no significant difference in performance when translating symmetrical word order NP + AdjP/AdvP constructions from English to Chinese compared to from Chinese to English. 2. In contrast, their performance on asymmetrical word order NP + AdjP/AdvP constructions is significantly better when translating from English to Chinese than from Chinese to English. 3. The adoption of chunk strategy does not mitigate this asymmetrical translation performance. 4. However, from a confidence perspective, they demonstrate significantly higher confidence in translating both asymmetrical and symmetrical word order NP + AdjP/AdvP constructions from English to Chinese compared to Chinese to English. A model explaining this asymmetrical performance is described, identifying the underutilization of syntactic trial and error parsing among Chinese learners of English as a contributing factor.

aMedian/mean RNAi lifespan of N2 and hyl-1;lagr-1 fed the specified RNAi-bacteria.bSome animals were censored as they crawled of the plate, ruptured, or died as a “bag of worms”, however they are incorporated in the data set up until the day they were censored. The number of individual trials is in parentheses.cMedian/mean control lifespan fed vector-only control bacteria.dP-values were determined using the Gehan-Breslow-Wilcoxon test using GraphPad Prism version 6.0 (GraphPad Software). The Bonferroni method was used to correct for multiple comparisons and P- values below 0.0125 are considered statistically significant equivalent to a significance level of 0.05 with four pair-wise comparisons. Cumulative statistics is shown in this table as experimental animals subjected to the same treatment behaved similarly between trials. Data shown in Figure 1.

Cytokines and BDNF secretion from human iPSC-derived astrocytes. Conditioned media were collected after stimulating cells during 24 h with 10 ng/mL TNF-α. Cytokines and BDNF secretion was measured in the conditioned media and compared with cells treated with vehicle. (a) - Pro-inflammatory cytokines: Interleukin-1 beta (IL-1β), Interleukin-8 (IL-8), Interferon gamma (IFN-γ) and Tumor necrosis factor alpha (TNF-α); (b) - modulatory cytokines: Interleukin-2 (IL-2), Interleukin-4 (IL-4) and Interleukin-6 (IL-6); (c) - anti-inflammatory cytokines Interleukin-10 (IL-10), Interleukin-13 (IL-13) and Brain-derived neurotrophic factor (BDNF). Data are presented as means ± SEM of concentrations in (pg/ml) of secreted factors. Conditioned media were collected from 4 cell lines and the experiments were performed in duplicates. *P < 0.05; **P < 0.01; ***P < 0.001; ns - non-significant. Unpaired Student´s t-test.