COMPOUND SPECIFIC CARBON ISOTOPE ANALYSIS FOR BIOMARKERS ASSOCIATED WITH MARINE METHANOTROPHY IN THE ARCTIC

A large reservoir of methane exists in marine sediments. The fate of methane is of particular concern in the Arctic, a region that has already demonstrated sensitivity to climate change. The removal of this potent greenhouse gas from the carbon cycle is largely mediated by microorganisms. In methane bearing ocean sediments where sulfate penetrates the surface sediment, sulfate reducing bacteria (SRB) and archaeal methanotrophs are found and believed to act as a consortium in the anaerobic oxidation of methane (AOM). Despite efforts based on thermodynamic models, rate measurements, and δ13C analysis of microbial biomarkers, the process by which methane is removed from anoxic sediments remains speculative. Sediment samples were collected from the Beaufort Shelf, east of Point Barrow, AK as part of the Methane in the Arctic Shelf/Slope (MITAS) Expedition in 2009. Core PC13 from this cruise was selected for compound specific carbon isotope analysis due the measured sulfate and methane concentrations. Stable carbon isotope analysis of the bacterial biomarkers selected specifically for known SRB phylotypes associated with AOM (i.e., i-C15:0, ai-C15:0 and C16:1 fatty acid methyl esters) resulted in δ13C values ranging from -27.8 to -25.3 /, strongly 13C-enriched relative to the biogenic methane in this core (δ13C = -100.0 to -74.6 /). At AOM sites, the microbial community involved in the process should reflect the carbon isotopic signature of the methane in instances of methanotrophy. In PC13, the bacterial biomarkers were not 13C-depleted like the methane, suggesting the lack of sulfate dependent AOM. The measurement of sulfate reduction rates and phylogenetic investigations corroborated the result from biomarker analysis, that the primary pathway for methanotrophy at this site is not coupled to sulfate reduction. Radiocarbon analyses of the bacterial biomarkers from PC13 were not utilized for the determination of methanotrophic pathways because the biomarkers targeted were for phylotypes whose dominant function at this site is not coupled to methanotrophy. However, the radiocarbon age of the bacterial markers may be useful in determining the sediment deposition rate at this site. For these biomarkers at 396 and 516 cm below the seafloor, the radiocarbon ages are 5805 and 5878 radiocarbon years, respectively. These ages result in an offset of 2500 radiocarbon years older relative to the shell fragments analyzed from the same depth. The biomarker age likely represents the older sediment delivered to the western Arctic via current systems, while the age of the shell fragments were deposited contemporaneously

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource

A Survey Tool for Assessing Student Expectations Early in a Semester

Quality learning is fostered when faculty members are aware of and address student expectations for course learning activities and assessments. However, faculty often have difficulty identifying and addressing student expectations given variations in students’ backgrounds, experiences, and beliefs about education. Prior research has described significant discrepancies between student and faculty expectations that result from cultural backgrounds (1), technological expertise (2), and ‘teaching dimensions’ as described by Trudeau and Barnes (4). Such studies illustrate the need for tools to identify and index student expectations, which can be used to facilitate a dialogue between instructor and students. Here we present the results of our work to develop, refine, and deploy such a tool.<span style="color: black; font-family: 'Times New Roman'; font-size: x-small;"></span

Expectations of Computing and Other STEM students: A Comparison for Different Class Levels, or (CSE &# x2260; STEM-CSE) &# x007C; course level

Students begin each new course with a set of expectations. These expectations are formed from their experiences in their major, class level, culture, skills, etc. However, faculty and the students are often not on the same page with respect to expectations even though faculty provide students with course syllabi. It is crucial for faculty to understand students\u27 expectations to maximize students\u27 learning, satisfaction, and success. Furthermore, it would promote classroom transparency. There would be no hidden unstated expectations; disappointments during the course can potentially be minimized. We present the results of a survey focused on understanding student expectations. Specifically, we focus on examining the differences in expectations of the students of Computer Science and Engineering (CSE) courses and non-computing STEM courses. We present our analysis and observations of the results using aggregate data for all students at all class levels. We observe various differences and similarities among the STEM fields. Identifying differences is crucial since many non-computing STEM majors are enrolled in computing courses, especially in the lower level courses. We provide a detailed comparison among sophomore and senior level courses in computing, biology and chemistry courses. We also compare sophomore and senior CSE courses. Finally, we discuss the importance of paying attention to all students\u27 needs and expectations. Armed with this knowledge, faculty members can increase transparency in the classroom, student satisfaction, and possibly student retention