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1. Behavioral and physiological responses to instability in group membership in wild male woolly monkeys ( Lagothrix lagotricha poeppigii )

   https://doi.org/10.1002/ajp.23240  

   Pain, Evelyn L. ; Koenig, Andreas ; Di Fiore, Anthony ; Lu, Amy ( February 2021 , American Journal of Primatology)

   In group‐living species, integrating into a new social group after dispersal is an important life history milestone associated with physical and social challenges. Generally, this process seems to be accompanied by heightened glucocorticoid (GC) concentrations; however, most studies of physiological responses to group transfer have been conducted on species with despotic social relationships, where integrating individuals are often targets of frequent aggression. Here we present data on fecal glucocorticoid (fGC) concentrations during periods of unstable group membership for male woolly monkeys (Lagothrix lagotricha poeppigii), a species with extremely low rates of male–male aggression and generally tolerant male–male associations. We collected data on males in four study groups at the Tiputini Biodiversity Station, Ecuador, and observed three attempted transfer events, involving a total of four adult males, in one study group. We observed only three instances of overt aggression (chases) between males across the entire study period, though male display behaviors were more frequent. We tested whether rates of displays were higher during periods of unstable group membership using a generalized linear mixed model (LMM). We also examined whether male status, group stability, and the occurrence of intergroup encounters affected fGC concentrations using LMMs. Contrary to our predictions, rates of display behaviors were not higher during periods of unstable group membership. However, both transient/integrating males and those who were already group members showed elevated fGC concentrations during these unstable periods. Our results suggest that even in species with tolerant male–male relationships, the integration of unfamiliar individuals can provoke an increase in GCs.

    

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2. An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts

   Jew, Brandon ; Li, Jiajin ; Sankararaman, Sriram ; Sul, Jae-Hoon ( January 2021 , 21st International Workshop on Algorithms in Bioinformatics, {WABI})

   Linear mixed models (LMMs) can be applied in the meta-analyses of responses from individuals across multiple contexts, increasing power to detect associations while accounting for confounding effects arising from within-individual variation. However, traditional approaches to fitting these models can be computationally intractable. Here, we describe an efficient and exact method for fitting a multiple-context linear mixed model. Whereas existing exact methods may be cubic in their time complexity with respect to the number of individuals, our approach for multiple-context LMMs (mcLMM) is linear. These improvements allow for large-scale analyses requiring computing time and memory magnitudes of order less than existing methods. As examples, we apply our approach to identify expression quantitative trait loci from large-scale gene expression data measured across multiple tissues as well as joint analyses of multiple phenotypes in genomewide association studies at biobank scale. 

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3. An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts

   Brandon Jew, Jiajin Li ( January 2021 , Proceedings of the 21st International Workshop on Algorithms in Bioinformatics (WABI 2021))

   Alessandra Carbone, Mohammed El-Kebir (Ed.)

   Linear mixed models (LMMs) can be applied in the meta-analyses of responses from individuals across multiple contexts, increasing power to detect associations while accounting for confounding effects arising from within-individual variation. However, traditional approaches to fitting these models can be computationally intractable. Here, we describe an efficient and exact method for fitting a multiple-context linear mixed model. Whereas existing exact methods may be cubic in their time complexity with respect to the number of individuals, our approach for multiple-context LMMs (mcLMM) is linear. These improvements allow for large-scale analyses requiring computing time and memory magnitudes of order less than existing methods. As examples, we apply our approach to identify expression quantitative trait loci from large-scale gene expression data measured across multiple tissues as well as joint analyses of multiple phenotypes in genome-wide association studies at biobank scale. 

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4. Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse

   https://doi.org/10.1136/jitc-2022-005360  

   Liu, Lunan ; Ma, Chao ; Zhang, Zhuoyu ; Witkowski, Matthew T ; Aifantis, Iannis ; Ghassemi, Saba ; Chen, Weiqiang ( December 2022 , Journal for ImmunoTherapy of Cancer)

   Background Adaptive CD19-targeted chimeric antigen receptor (CAR) T-cell transfer has become a promising treatment for leukemia. Although patient responses vary across different clinical trials, reliable methods to dissect and predict patient responses to novel therapies are currently lacking. Recently, the depiction of patient responses has been achieved using in silico computational models, with prediction application being limited. Methods We established a computational model of CAR T-cell therapy to recapitulate key cellular mechanisms and dynamics during treatment with responses of continuous remission (CR), non-response (NR), and CD19-positive (CD19 + ) and CD19-negative (CD19 − ) relapse. Real-time CAR T-cell and tumor burden data of 209 patients were collected from clinical studies and standardized with unified units in bone marrow. Parameter estimation was conducted using the stochastic approximation expectation maximization algorithm for nonlinear mixed-effect modeling. Results We revealed critical determinants related to patient responses at remission, resistance, and relapse. For CR, NR, and CD19 + relapse, the overall functionality of CAR T-cell led to various outcomes, whereas loss of the CD19 + antigen and the bystander killing effect of CAR T-cells may partly explain the progression of CD19 − relapse. Furthermore, we predicted patient responses by combining the peak and accumulated values of CAR T-cells or by inputting early-stage CAR T-cell dynamics. A clinical trial simulation using virtual patient cohorts generated based on real clinical patient datasets was conducted to further validate the prediction. Conclusions Our model dissected the mechanism behind distinct responses of leukemia to CAR T-cell therapy. This patient-based computational immuno-oncology model can predict late responses and may be informative in clinical treatment and management. 

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5. Breath-synchronized electrical stimulation of the expiratory muscles in mechanically ventilated patients: a randomized controlled feasibility study and pooled analysis

   https://doi.org/10.1186/s13054-020-03352-0  

   Jonkman, Annemijn H. ; Frenzel, Tim ; McCaughey, Euan J. ; McLachlan, Angus J. ; Boswell-Ruys, Claire L. ; Collins, David W. ; Gandevia, Simon C. ; Girbes, Armand R. ; Hoiting, Oscar ; Kox, Matthijs ; et al ( December 2020 , Critical Care)

   null (Ed.)

   Abstract Background Expiratory muscle weakness leads to difficult ventilator weaning. Maintaining their activity with functional electrical stimulation (FES) may improve outcome. We studied feasibility of breath-synchronized expiratory population muscle FES in a mixed ICU population (“Holland study”) and pooled data with our previous work (“Australian study”) to estimate potential clinical effects in a larger group. Methods Holland: Patients with a contractile response to FES received active or sham expiratory muscle FES (30 min, twice daily, 5 days/week until weaned). Main endpoints were feasibility (e.g., patient recruitment, treatment compliance, stimulation intensity) and safety. Pooled: Data on respiratory muscle thickness and ventilation duration from the Holland and Australian studies were combined ( N  = 40) in order to estimate potential effect size. Plasma cytokines (day 0, 3) were analyzed to study the effects of FES on systemic inflammation. Results Holland: A total of 272 sessions were performed (active/sham: 169/103) in 20 patients ( N  = active/sham: 10/10) with a total treatment compliance rate of 91.1%. No FES-related serious adverse events were reported. Pooled: On day 3, there was a between-group difference ( N  = active/sham: 7/12) in total abdominal expiratory muscle thickness favoring the active group [treatment difference (95% confidence interval); 2.25 (0.34, 4.16) mm, P  = 0.02] but not on day 5. Plasma cytokine levels indicated that early FES did not induce systemic inflammation. Using a survival analysis approach for the total study population, median ventilation duration and ICU length of stay were 10 versus 52 ( P  = 0.07), and 12 versus 54 ( P  = 0.03) days for the active versus sham group. Median ventilation duration of patients that were successfully extubated was 8.5 [5.6–12.2] versus 10.5 [5.3–25.6] days ( P  = 0.60) for the active ( N  = 16) versus sham ( N  = 10) group, and median ICU length of stay was 10.5 [8.0–14.5] versus 14.0 [9.0–19.5] days ( P  = 0.36) for those active ( N  = 16) versus sham ( N  = 8) patients that were extubated and discharged alive from the ICU. During ICU stay, 3/20 patients died in the active group versus 8/20 in the sham group ( P  = 0.16). Conclusion Expiratory muscle FES is feasible in selected ICU patients and might be a promising technique within a respiratory muscle-protective ventilation strategy. The next step is to study the effects on weaning and ventilator liberation outcome. Trial registration: ClinicalTrials.gov, ID NCT03453944. Registered 05 March 2018—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03453944 . 

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