31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Open Forensic Science in R

This book is for anyone looking to do forensic science analysis in a data-driven and open way. Whether you are a student, teacher, or scientist, this book is for you. We take the latest research, primarily from the Center for Statistics and Applications in Forensic Evidence (CSAFE) and the National Institute of Standards and Technology (NIST) and show you how to solve forensic science problems in R. The book makes some assumptions about you: You have some experience with R (R Core Team 2019). We don’t assume you are an expert by any means, but we do assume you are comfortable enough with R to install & library packages, load data, identify different data structures, and to follow along with the code we present in each chapter. If you need help getting started with R, there are lots of free resources online, and CSAFE has some resources available here. You can install R for Windows, Mac, and Linux here for free. We also recommend you install RStudio, the wonderful free IDE (Integrated Development Environment) for R. If you want a deeper dive into R, take a walk through R for Data Science. If you really want to explore the depths, Advanced R is an excellent resource. You are interested in forensic science. Hopefully that’s why you’re here! You may only be interested in DNA or firearms, so we’ve split the book up into chapters by forensic science subfield. You also don’t have to be an expert in the field. We will explain the basics of the field in the introduction of each chapter. You can also download this book by clicking here or by cloning it on GitHub and follow along, running the code on your own computer. You care about open source software. This doesn’t really affect your ability to read this book, but it’s a nice quality to have. The purpose of this book is to make forensic science more accessible. Right now, most databases, algorithms, and programs that get used every day in forensic science are proprietary, meaning that only the owners know how these systems work, how they were made, and what the source code looks like. This closed approach has lead to miscarriages of justice. With this free online book that relies solely on open-source software for analysis, we hope to demonstrate the impact open source software can have on forensic science, both in research and in practice. And in this spirit of openness, we ask that you contribute if you find an error or want to add a chapter on a topic we did not cover. You can open an issue here or fork the book’s Github repository and submit your changes via a pull request. If you’d like to contribute, we ask that you follow our contributor code of conduct and these recommended practices from Jenny Bryan and Jim Hester of RStudio. </p

Open Forensic Science in R

This book is for anyone looking to do forensic science analysis in a data-driven and open way. Whether you are a student, teacher, or scientist, this book is for you. We take the latest research, primarily from the Center for Statistics and Applications in Forensic Evidence (CSAFE) and the National Institute of Standards and Technology (NIST) and show you how to solve forensic science problems in R. The book makes some assumptions about you: You have some experience with R (R Core Team 2019). We don’t assume you are an expert by any means, but we do assume you are comfortable enough with R to install & library packages, load data, identify different data structures, and to follow along with the code we present in each chapter. If you need help getting started with R, there are lots of free resources online, and CSAFE has some resources available here. You can install R for Windows, Mac, and Linux here for free. We also recommend you install RStudio, the wonderful free IDE (Integrated Development Environment) for R. If you want a deeper dive into R, take a walk through R for Data Science. If you really want to explore the depths, Advanced R is an excellent resource. You are interested in forensic science. Hopefully that’s why you’re here! You may only be interested in DNA or firearms, so we’ve split the book up into chapters by forensic science subfield. You also don’t have to be an expert in the field. We will explain the basics of the field in the introduction of each chapter. You can also download this book by clicking here or by cloning it on GitHub and follow along, running the code on your own computer. You care about open source software. This doesn’t really affect your ability to read this book, but it’s a nice quality to have. The purpose of this book is to make forensic science more accessible. Right now, most databases, algorithms, and programs that get used every day in forensic science are proprietary, meaning that only the owners know how these systems work, how they were made, and what the source code looks like. This closed approach has lead to miscarriages of justice. With this free online book that relies solely on open-source software for analysis, we hope to demonstrate the impact open source software can have on forensic science, both in research and in practice. And in this spirit of openness, we ask that you contribute if you find an error or want to add a chapter on a topic we did not cover. You can open an issue here or fork the book’s Github repository and submit your changes via a pull request. If you’d like to contribute, we ask that you follow our contributor code of conduct and these recommended practices from Jenny Bryan and Jim Hester of RStudio

Estimating arthropod survival probability from field counts: a case study with monarch butterflies

Survival probability is fundamental for understanding population dynamics. Methods for estimating survival probability from field data typically require marking individuals, but marking methods are not possible for arthropod species that molt their exoskeleton between life stages. We developed a novel Bayesian state‐space model to estimate arthropod larval survival probability from stage‐structured count data. We performed simulation studies to evaluate estimation bias due to detection probability, individual variation in stage duration, and study design (sampling frequency and sample size). Estimation of cumulative survival probability from oviposition to pupation was robust to potential sources of bias. Our simulations also provide guidance for designing field studies with minimal bias. We applied the model to the monarch butterfly (Danaus plexippus), a declining species in North America for which conservation programs are being implemented. We estimated cumulative survival from egg to pupation from monarch counts conducted at 18 field sites in three landcover types in Iowa, USA, and Ontario, Canada: road right‐of‐ways, natural habitats (gardens and restored meadows), and agricultural field borders. Mean predicted survival probability across all landcover types was 0.014 (95% CI: 0.004–0.024), four times lower than previously published estimates using an ad hoc estimator. Estimated survival probability ranged from 0.002 (95% CI: 7.0E−7 to 0.034) to 0.058 (95% CI: 0.013–0.113) at individual sites. Among landcover types, agricultural field borders in Ontario had the highest estimated survival probability (0.025 with 95% CI: 0.008–0.043) and natural areas had the lowest estimated survival probability (0.008 with 95% CI: 0.009–0.024). Monarch production was estimated as adults produced per milkweed stem by multiplying survival probabilities by eggs per milkweed at these sites. Monarch production ranged from 1.0 (standard deviation [SD] = 0.68) adult in Ontario natural areas in 2016 to 29.0 (SD = 10.42) adults in Ontario agricultural borders in 2015 per 6809 milkweed stems. Survival estimates are critical to monarch population modeling and habitat restoration efforts. Our model is a significant advance in estimating survival probability for monarch butterflies and can be readily adapted to other arthropod species with stage‐structured life histories.This article is published as Grant, Tyler J., DT Tyler Flockhart, Teresa R. Blader, Richard L. Hellmich, Grace M. Pitman, Sam Tyner, D. Ryan Norris, and Steven P. Bradbury. "Estimating arthropod survival probability from field counts: a case study with monarch butterflies." Ecosphere 11, no. 4 (2020): e03082. doi: 10.1002/ecs2.3082.</p

Science NextGen Voices: Science-inspired sustainable behavior.

We asked young scientists this question: How has your awareness of science inspired you to adopt more sustainable and environmentally friendly behavior? Respondents from around the world described scientific concepts, images, and research from a range of fields that inspire them to make environmentally friendly decisions and model sustainable behavior for others, in both their personal and professional lives. Read a selection of the best responses here. Follow NextGen on Twitter with hashtag #NextGenSci

Human Sentinel Surveillance of Influenza and Other Respiratory Viral Pathogens in Border Areas of Western Cambodia

<div><p>Little is known about circulation of influenza and other respiratory viruses in remote populations along the Thai-Cambodia border in western Cambodia. We screened 586 outpatients (median age 5, range 1–77) presenting with influenza-like-illness (ILI) at 4 sentinel sites in western Cambodia between May 2010 and December 2012. Real-time reverse transcriptase (rRT) PCR for influenza was performed on combined nasal and throat specimens followed by viral culture, antigenic analysis, antiviral susceptibility testing and full genome sequencing for phylogenetic analysis. ILI-specimens negative for influenza were cultured, followed by rRT-PCR for enterovirus and rhinovirus (EV/RV) and EV71. Influenza was found in 168 cases (29%) and occurred almost exclusively in the rainy season from June to November. Isolated influenza strains had close antigenic and phylogenetic relationships, matching vaccine and circulating strains found elsewhere in Cambodia. Influenza vaccination coverage was low (<20%). Western Cambodian H1N1(2009) isolate genomes were more closely related to 10 earlier Cambodia isolates (94.4% genome conservation) than to 13 Thai isolates (75.9% genome conservation), despite sharing the majority of the amino acid changes with the Thai references. Most genes showed signatures of purifying selection. Viral culture detected only adenovirus (5.7%) and parainfluenza virus (3.8%), while non-polio enteroviruses (10.3%) were detected among 164 culture-negative samples including coxsackievirus A4, A6, A8, A9, A12, B3, B4 and echovirus E6 and E9 using nested RT-PCR methods. A single specimen of EV71 was found. Despite proximity to Thailand, influenza epidemiology of these western Cambodian isolates followed patterns observed elsewhere in Cambodia, continuing to support current vaccine and treatment recommendations from the Cambodian National Influenza Center. Amino acid mutations at non-epitope sites, particularly hemagglutinin genes, require further investigation in light of an increasingly important role of permissive mutations in influenza virus evolution. Further research about the burden of adenovirus and non-polio enteroviruses as etiologic agents in acute respiratory infections in Cambodia is also needed.</p></div