International Innovative Methods for Engaging Young Children in Research

A framework of six steps for engaging young children in research is presented in this chapter. This framework was built through collaboration with a team of international researchers funded by the Bernard van Leer Foundation (Johnson, Hart, & Colwell, 2014a and b). Relevant literature and theoretical underpinning is presented with discussion of how creative participatory approaches are relevant to understanding and interacting with children’s geographies. The framework addresses key issues facing researchers wishing to engage young children in research processes and offers ways to overcome some of the challenges researchers may face. In order to illustrate the six steps, a selection of case studies which provide examples of how experts from a wide range of international contexts have worked with children are presented. These case studies have been selected to be of particular relevance to geographic field research with young children and have been developed and tested by experts from a wide range of international contexts

Anthropology, Empathy, and the Need for More Social Science Communication

This is a call to science communicators and science journalists to feature social science research and researchers in their reporting, with an emphasis on anthropology and its potential to increase public empathy, improve the quality of public discourse, and contribute to contextual and narrative news trends. Keywords: social science communication; anthropology; narrative news; empathy.</p

Health‐Damaging Climate Events Highlight the Need for Interdisciplinary, Engaged Research

In 2023 human populations experienced multiple record‐breaking climate events, with widespread impacts on human health and well‐being. These events include extreme heat domes, drought, severe storms, flooding, and wildfires. Due to inherent lags in the climate system, we can expect such extremes to continue for multiple decades after reaching net zero carbon emissions. Unfortunately, despite these significant current and future impacts, funding for research in climate and health has lagged behind that for other geoscience and biomedical research. While some initial efforts from funding agencies are evident, there is still a significant need to increase the resources available for multidisciplinary research in the face of this issue. As a group of experts at this important intersection, we call for a more concerted effort to encourage interdisciplinary and policy‐relevant investigations into the detrimental health effects of continued climate change

AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions

Antibodies have become an important class of therapeutic agents to treat human diseases. To accelerate therapeutic antibody discovery, computational methods, especially machine learning, have attracted considerable interest for predicting specific interactions between antibody candidates and target antigens such as viruses and bacteria. However, the publicly available datasets in existing works have notable limitations, such as small sizes and the lack of non-binding samples and exact amino acid sequences. To overcome these limitations, we have developed AVIDa-hIL6, a large-scale dataset for predicting antigen-antibody interactions in the variable domain of heavy chain of heavy chain antibodies (VHHs), produced from an alpaca immunized with the human interleukin-6 (IL-6) protein, as antigens. By leveraging the simple structure of VHHs, which facilitates identification of full-length amino acid sequences by DNA sequencing technology, AVIDa-hIL6 contains 573,891 antigen-VHH pairs with amino acid sequences. All the antigen-VHH pairs have reliable labels for binding or non-binding, as generated by a novel labeling method. Furthermore, via introduction of artificial mutations, AVIDa-hIL6 contains 30 different mutants in addition to wild-type IL-6 protein. This characteristic provides opportunities to develop machine learning models for predicting changes in antibody binding by antigen mutations. We report experimental benchmark results on AVIDa-hIL6 by using neural network-based baseline models. The results indicate that the existing models have potential, but further research is needed to generalize them to predict effective antibodies against unknown mutants. The dataset is available at https://avida-hil6.cognanous.com

Microbial activity in the marine deep biosphere: progress and prospects

The vast marine deep biosphere consists of microbial habitats within sediment, pore waters, upper basaltic crust and the fluids that circulate throughout it. A wide range of temperature, pressure, pH, and electron donor and acceptor conditions exists—all of which can combine to affect carbon and nutrient cycling and result in gradients on spatial scales ranging from millimeters to kilometers. Diverse and mostly uncharacterized microorganisms live in these habitats, and potentially play a role in mediating global scale biogeochemical processes. Quantifying the rates at which microbial activity in the subsurface occurs is a challenging endeavor, yet developing an understanding of these rates is essential to determine the impact of subsurface life on Earth\u27s global biogeochemical cycles, and for understanding how microorganisms in these “extreme” environments survive (or even thrive). Here, we synthesize recent advances and discoveries pertaining to microbial activity in the marine deep subsurface, and we highlight topics about which there is still little understanding and suggest potential paths forward to address them. This publication is the result of a workshop held in August 2012 by the NSF-funded Center for Dark Energy Biosphere Investigations (C-DEBI) “theme team” on microbial activity (www.darkenergybiosphere.org)

A comparison of taxon co-occurrence patterns for macro- and microorganisms

We examine co-occurrence patterns of microorganisms to evaluate community assembly “rules.” We use methods previously applied to macroorganisms, both to evaluate their applicability to microorganisms and to allow comparison of co-occurrence patterns observed in microorganisms to those found in macroorganisms. We use a null model analysis of 124 incidence matrices from microbial communities, including bacteria, archaea, fungi, and algae, and we compare these results to previously published findings from a meta-analysis of almost 100 macroorganism data sets. We show that assemblages of microorganisms demonstrate nonrandom patterns of co-occurrence that are broadly similar to those found in assemblages of macroorganisms. These results suggest that some taxon co-occurrence patterns may be general characteristics of communities of organisms from all domains of life. We also find that co-occurrence in microbial communities does not vary among taxonomic groups or habitat types. However, we find that the degree of co-occurrence does vary among studies that use different methods to survey microbial communities. Finally, we discuss the potential effects of the undersampling of microbial communities on our results, as well as processes that may contribute to nonrandom patterns of co-occurrence in both macrobial and microbial communities such as competition, habitat filtering, historical effects, and neutral processes