Distinct dissolved organic matter sources induce rapid transcriptional responses in coexisting populations of Prochlorococcus, Pelagibacter and the OM60 clade

Author Posting. © The Author(s), 2013. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Environmental Microbiology 16 (2014): 2815-2830, doi:10.1111/1462-2920.12254.A considerable fraction of the Earth's organic carbon exists in dissolved form in seawater. To investigate the roles of planktonic marine microbes in the biogeochemical cycling of this dissolved organic matter (DOM), we performed controlled seawater incubation experiments and followed the responses of an oligotrophic surface water microbial assemblage to perturbations with DOM derived from an axenic culture of Prochlorococcus, or high-molecular weight DOM concentrated from nearby surface waters. The rapid transcriptional responses of both Prochlorococcus and Pelagibacter populations suggested the utilization of organic nitrogen compounds common to both DOM treatments. Along with these responses, both populations demonstrated decreases in gene transcripts associated with nitrogen stress, including those involved in ammonium acquisition. In contrast, responses from low abundance organisms of the NOR5/OM60 gammaproteobacteria were observed later in the experiment, and included elevated levels of gene transcripts associated with polysaccharide uptake and oxidation. In total, these results suggest that numerically dominant oligotrophic microbes rapidly acquire nitrogen from commonly available organic sources, and also point to an important role for carbohydrates found within the DOM pool for sustaining the less abundant microorganisms in these oligotrophic systems.This work was supported by a National Science Foundation Science and Technology Center Award EF0424599 (E.F.D and D.M.K.), grants to D.M.K., D.J.R and E.F.D from the Gordon and Betty Moore Foundation, a gift from the Agouron Institute (to E.F.D.) and a fellowship (202180) to A.K.S. from the Canadian Institutes of Health Research (CIHR)

Morphological changes in American kestrels (Falco sparverius) at continental migration sites

Many American kestrel (Falco sparverius) populations are declining across North America. Potential causes include mortality from reduction in food availability, a changing climate, habitat degradation, an increase in avian predators, disease, and toxins. We analyzed American kestrel count and banding data from seven raptor migration sites throughout North America with at least 20 years of migration data. We used count data to determine the year at which the kestrel population began a significant decline and then used banding records to determine whether body mass and wing chord declined after this point. We found reductions in kestrel body mass at three sites and reductions in kestrel wing chord at five sites. Our results indicate declines in body size at the majority of sites are consistent with the hypotheses that food availability, impacts of a changing climate, or predation risk may be contributing to population declines

Mutant PIK3CA promotes cell growth and invasion of human cancer cells

SummaryPIK3CA is mutated in diverse human cancers, but the functional effects of these mutations have not been defined. To evaluate the consequences of PIK3CA alterations, the two most common mutations were inactivated by gene targeting in colorectal cancer (CRC) cells. Biochemical analyses of these cells showed that mutant PIK3CA selectively regulated the phosphorylation of AKT and the forkhead transcription factors FKHR and FKHRL1. PIK3CA mutations had little effect on growth under standard conditions, but reduced cellular dependence on growth factors. PIK3CA mutations resulted in attenuation of apoptosis and facilitated tumor invasion. Treatment with the PI3K inhibitor LY294002 abrogated PIK3CA signaling and preferentially inhibited growth of PIK3CA mutant cells. These data have important implications for therapy of cancers harboring PIK3CA alterations

Autonomous tracking and sampling of the deep chlorophyll maximum layer in an open-ocean eddy by a long-range autonomous underwater vehicle

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Zhang, Y., Kieft, B., Hobson, B. W., Ryan, J. P., Barone, B., Preston, C. M., Roman, B., Raanan, B., Marin,Roman,,III, O'Reilly, T. C., Rueda, C. A., Pargett, D., Yamahara, K. M., Poulos, S., Romano, A., Foreman, G., Ramm, H., Wilson, S. T., DeLong, E. F., Karl, D. M., Birch, J. M., Bellingham, J. G., & Scholin, C. A. Autonomous tracking and sampling of the deep chlorophyll maximum layer in an open-ocean eddy by a long-range autonomous underwater vehicle. IEEE Journal of Oceanic Engineering, 45(4), (2020): 1308-1321, doi:10.1109/JOE.2019.2920217.Phytoplankton communities residing in the open ocean, the largest habitat on Earth, play a key role in global primary production. Through their influence on nutrient supply to the euphotic zone, open-ocean eddies impact the magnitude of primary production and its spatial and temporal distributions. It is important to gain a deeper understanding of the microbial ecology of marine ecosystems under the influence of eddy physics with the aid of advanced technologies. In March and April 2018, we deployed autonomous underwater and surface vehicles in a cyclonic eddy in the North Pacific Subtropical Gyre to investigate the variability of the microbial community in the deep chlorophyll maximum (DCM) layer. One long-range autonomous underwater vehicle (LRAUV) carrying a third-generation Environmental Sample Processor (3G-ESP) autonomously tracked and sampled the DCM layer for four days without surfacing. The sampling LRAUV's vertical position in the DCM layer was maintained by locking onto the isotherm corresponding to the chlorophyll peak. The vehicle ran on tight circles while drifting with the eddy current. This mode of operation enabled a quasi-Lagrangian time series focused on sampling the temporal variation of the DCM population. A companion LRAUV surveyed a cylindrical volume around the sampling LRAUV to monitor spatial and temporal variation in contextual water column properties. The simultaneous sampling and mapping enabled observation of DCM microbial community in its natural frame of reference.10.13039/501100008982 - National Science Foundation 10.13039/100000936 - Gordon and Betty Moore Foundation 10.13039/100000008 - David and Lucile Packard Foundation 10.13039/100016377 - Schmidt Ocean Institute 10.13039/100000893 - Simons Foundatio

Probing Metagenomics by Rapid Cluster Analysis of Very Large Datasets

BACKGROUND: The scale and diversity of metagenomic sequencing projects challenge both our technical and conceptual approaches in gene and genome annotations. The recent Sorcerer II Global Ocean Sampling (GOS) expedition yielded millions of predicted protein sequences, which significantly altered the landscape of known protein space by more than doubling its size and adding thousands of new families (Yooseph et al., 2007 PLoS Biol 5, e16). Such datasets, not only by their sheer size, but also by many other features, defy conventional analysis and annotation methods. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we describe an approach for rapid analysis of the sequence diversity and the internal structure of such very large datasets by advanced clustering strategies using the newly modified CD-HIT algorithm. We performed a hierarchical clustering analysis on the 17.4 million Open Reading Frames (ORFs) identified from the GOS study and found over 33 thousand large predicted protein clusters comprising nearly 6 million sequences. Twenty percent of these clusters did not match known protein families by sequence similarity search and might represent novel protein families. Distributions of the large clusters were illustrated on organism composition, functional class, and sample locations. CONCLUSION/SIGNIFICANCE: Our clustering took about two orders of magnitude less computational effort than the similar protein family analysis of original GOS study. This approach will help to analyze other large metagenomic datasets in the future. A Web server with our clustering results and annotations of predicted protein clusters is available online at http://tools.camera.calit2.net/gos under the CAMERA project

Computer-assisted assessment of the Human Epidermal Growth Factor Receptor 2 immunohistochemical assay in imaged histologic sections using a membrane isolation algorithm and quantitative analysis of positive controls

<p>Abstract</p> <p>Background</p> <p>Breast cancers that overexpress the human epidermal growth factor receptor 2 (HER2) are eligible for effective biologically targeted therapies, such as trastuzumab. However, accurately determining HER2 overexpression, especially in immunohistochemically equivocal cases, remains a challenge. Manual analysis of HER2 expression is dependent on the assessment of membrane staining as well as comparisons with positive controls. In spite of the strides that have been made to standardize the assessment process, intra- and inter-observer discrepancies in scoring is not uncommon. In this manuscript we describe a pathologist assisted, computer-based continuous scoring approach for increasing the precision and reproducibility of assessing imaged breast tissue specimens.</p> <p>Methods</p> <p>Computer-assisted analysis on HER2 IHC is compared with manual scoring and fluorescence in situ hybridization results on a test set of 99 digitally imaged breast cancer cases enriched with equivocally scored (2+) cases. Image features are generated based on the staining profile of the positive control tissue and pixels delineated by a newly developed Membrane Isolation Algorithm. Evaluation of results was performed using Receiver Operator Characteristic (ROC) analysis.</p> <p>Results</p> <p>A computer-aided diagnostic approach has been developed using a membrane isolation algorithm and quantitative use of positive immunostaining controls. By incorporating internal positive controls into feature analysis a greater Area Under the Curve (AUC) in ROC analysis was achieved than feature analysis without positive controls. Evaluation of HER2 immunostaining that utilized membrane pixels, controls, and percent area stained showed significantly greater AUC than manual scoring, and significantly less false positive rate when used to evaluate immunohistochemically equivocal cases.</p> <p>Conclusion</p> <p>It has been shown that by incorporating both a membrane isolation algorithm and analysis of known positive controls a computer-assisted diagnostic algorithm was developed that can reproducibly score HER2 status in IHC stained clinical breast cancer specimens. For equivocal scoring cases, this approach performed better than standard manual evaluation as assessed by ROC analysis in our test samples. Finally, there exists potential for utilizing image-analysis techniques for improving HER2 scoring at the immunohistochemically equivocal range.</p

A single-item self-report medication adherence question predicts hospitalisation and death in patients with heart failure

To determine whether a single-item self-report medication adherence question predicts hospitalization and death in patients with heart failure (HF)

Behavioral Finance

Behavioral finance studies the application of psychology to finance, with a focus on individual-level cognitive biases. I describe here the sources of judgment and decision biases, how they affect trading and market prices, the role of arbitrage and flows of wealth between more rational and less rational investors, how firms exploit inefficient prices and incite misvaluation, and the effects of managerial judgment biases. There is need for more theory and testing of the effects of feelings on financial decisions and aggregate outcomes. Especially, the time has come to move beyond behavioral finance to social finance, which studies the structure of social interactions, how financial ideas spread and evolve, and how social processes affect financial outcomes

An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation

BACKGROUND PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. METHODS Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. RESULTS In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40. CONCLUSIONS The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer

Bacterial diversity and community composition from seasurface to subseafloor

© The International Society for Microbial Ecology, 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in ISME Journal 10 (2016): 979–989, doi:10.1038/ismej.2015.175.We investigated compositional relationships between bacterial communities in the water column and those in deep-sea sediment at three environmentally distinct Pacific sites (two in the Equatorial Pacific and one in the North Pacific Gyre). Through pyrosequencing of the v4–v6 hypervariable regions of the 16S ribosomal RNA gene, we characterized 450 104 pyrotags representing 29 814 operational taxonomic units (OTUs, 97% similarity). Hierarchical clustering and non-metric multidimensional scaling partition the samples into four broad groups, regardless of geographic location: a photic-zone community, a subphotic community, a shallow sedimentary community and a subseafloor sedimentary community (greater than or equal to1.5 meters below seafloor). Abundance-weighted community compositions of water-column samples exhibit a similar trend with depth at all sites, with successive epipelagic, mesopelagic, bathypelagic and abyssopelagic communities. Taxonomic richness is generally highest in the water-column O2 minimum zone and lowest in the subseafloor sediment. OTUs represented by abundant tags in the subseafloor sediment are often present but represented by few tags in the water column, and represented by moderately abundant tags in the shallow sediment. In contrast, OTUs represented by abundant tags in the water are generally absent from the subseafloor sediment. These results are consistent with (i) dispersal of marine sedimentary bacteria via the ocean, and (ii) selection of the subseafloor sedimentary community from within the community present in shallow sediment.This study was funded by the Biological Oceanography Program of the US National Science Foundation (grant OCE-0752336) and by the NSF-funded Center for Dark Energy Biosphere Investigations (grant NSF-OCE-0939564)