Assessing the spatial and temporal variability of the Detroit River and harmful algal blooms in western Lake Erie

Despite efforts to reduce the occurrence of harmful algal blooms (HABs) in western Lake Erie, blooms recur annually due to agricultural runoff, storms with high winds and heavy rains, and weak lake circulation patterns. The influence from river inputs on the spatial and temporal characteristics of HABs remains relatively unknown. The Detroit River, which contributes about 80% of the basin\u27s total inflow can have a large influence on the spatial and temporal distribution of the bloom. To understand this, optically classified imagery, in situ water measurements, and meteorological and river discharge observations were compiled and synthesized to examine the spatiotemporal variability of the Detroit River, HABs, and their interaction. Results indicate the presence of a defined Detroit River plume, which varies in size depending on wind and water current conditions within the lake. While high discharge of the river has an impact on the entire basin, strong winds in the spring, fall, and during summer pushes the Detroit River further south into the basin. This increases the spatiotemporal interaction between the Detroit River and HAB by limiting northerly bloom extent and diluting bloom water conditions. These results reveal the importance of Detroit River impact on blooms. Here, I present a greater understanding of the Detroit River and its role in the lake aiding the ability to improve predictions of bloom spatial variability

Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.

The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Toward this, we use an existing brain parcellation atlas in the Montreal Neurological Institute (MNI) space and map this atlas to the individual subject data. This mapped atlas in the subject data space is integrated with structural Magnetic Resonance (MR) imaging data, and patch-based neural networks, including 3D U-Net and DeepMedic, are trained to classify the different brain lesions. Multiple state-of-the-art neural networks are trained and integrated with XGBoost fusion in the proposed two-level ensemble method. The first level reduces the uncertainty of the same type of models with different seed initializations, and the second level leverages the advantages of different types of neural network models. The proposed location information fusion method improves the segmentation performance of state-of-the-art networks including 3D U-Net and DeepMedic. Our proposed ensemble also achieves better segmentation performance compared to the state-of-the-art networks in BraTS 2017 and rivals state-of-the-art networks in BraTS 2018. Detailed results are provided on the public multimodal brain tumor segmentation (BraTS) benchmarks

Upregulation of Id1 by Epstein-Barr Virus-encoded LMP1 confers resistance to TGFβ-mediated growth inhibition

BACKGROUND: Epstein-Barr virus (EBV)-encoded LMP1 protein is commonly expressed in nasopharyngeal carcinoma (NPC). LMP1 is a prime candidate for driving tumourigenesis given its ability to activate multiple signalling pathways and to alter the expression and activity of variety of downstream targets. Resistance to TGFβ-mediated cytostasis is one of the growth transforming effects of LMP1. Of the downstream targets manipulated by LMP1, the induction of Id1 and inactivation of Foxo3a appear particularly relevant to LMP1-mediated effects. Id1, a HLH protein is implicated in cell transformation and plays a role in cell proliferation, whilst Foxo3a, a transcription factor controls cell integrity and homeostasis by regulating apoptosis. The mechanism(s) by which LMP1 induces these effects have not been fully characterised. RESULTS: In this study, we demonstrate that the ability of LMP1 to induce the phosphorylation and inactivation of Foxo3a is linked to the upregulation of Id1. Furthermore, we show that the induction of Id1 is essential for the transforming function of LMP1 as over-expression of Id1 increases cell proliferation, attenuates TGFβ-SMAD-mediated transcription and renders cells refractory to TGFβ-mediated cytostasis. Id1 silencing in LMP1-expressing epithelial cells abolishes the inhibitory effect of LMP1 on TGFβ-mediated cell growth arrest and reduces the ability of LMP1 to attenuate SMAD transcriptional activity. In response to TGFβ stimulation, LMP1 does not abolish SMAD phosphorylation but inhibits p21 protein expression. In addition, we found the induction of Id1 in LMP1-expressing cells upon stimulation by TGFβ. We provide evidence that LMP1 suppresses the transcriptional repressor ATF3, possibly leading to the TGFβ-induced Id1 upregulation. CONCLUSION: The current data provide novel information regarding the mechanisms by which LMP1 suppresses TGFβ-induced cytostasis, highlighting the importance of Id1 in LMP1 mediated cell transformatio

Expanding understanding of optical variability in Lake Superior with a 4-year dataset

Lake Superior is one of the largest freshwater lakes on our planet, but few optical observations have been made to allow for the development and validation of visible spectral satellite remote sensing products. The dataset described here focuses on coincidently observing inherent and apparent optical properties along with biogeochemical parameters. Specifically, we observe remote sensing reflectance, absorption, scattering, backscattering, attenuation, chlorophyll concentration, and suspended particulate matter over the ice-free months of 2013-2016. The dataset substantially increases the optical knowledge of the lake. In addition to visible spectral satellite algorithm development, the dataset is valuable for characterizing the variable light field, particle, phytoplankton, and colored dissolved organic matter distributions, and helpful in food web and carbon cycle investigations. The compiled data can be freely accessed at https://seabass.gsfc.nasa.gov/archive/URI/Mouw/LakeSuperior/

Corrigendum: Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.

[This corrects the article DOI: 10.3389/fnins.2019.01449.]

Seeded x-ray free-electron laser generating radiation with laser statistical properties

The invention of optical lasers led to a revolution in the field of optics and even to the creation of completely new fields of research such as quantum optics. The reason was their unique statistical and coherence properties. The newly emerging, short-wavelength free-electron lasers (FELs) are sources of very bright coherent extreme-ultraviolet (XUV) and x-ray radiation with pulse durations on the order of femtoseconds, and are presently considered to be laser sources at these energies. Most existing FELs are highly spatially coherent but in spite of their name, they behave statistically as chaotic sources. Here, we demonstrate experimentally, by combining Hanbury Brown and Twiss (HBT) interferometry with spectral measurements that the seeded XUV FERMI FEL-2 source does indeed behave statistically as a laser. The first steps have been taken towards exploiting the first-order coherence of FELs, and the present work opens the way to quantum optics experiments that strongly rely on high-order statistical properties of the radiation.Comment: 24 pages, 10 figures, 37 reference

Induction EGFR tyrosine kinase inhibitors prior to definitive chemoradiotherapy in unresectable stage III EGFR-mutated non-small cell lung cancer

INTRODUCTION: Increasing evidence suggests that consolidation durvalumab confers limited benefits for patients with stage III EGFR-mutated NSCLC. Induction or maintenance EGFR tyrosine kinase inhibitors (TKIs) added to concurrent chemoradiotherapy (CRT) may optimize definitive treatment, but there are limited data supporting an induction TKI strategy. METHODS: We evaluated the efficacy and safety of induction EGFR TKIs administered before concurrent CRT in a retrospective series of patients with unresectable locally advanced EGFR-mutated NSCLC. Circulating tumor DNA (ctDNA) analysis was performed on a patient subset using CAPP-seq and correlated with outcomes. RESULTS: Of six patients, three received erlotinib and three osimertinib as induction therapy before CRT. Induction TKIs were administered for a median of 2.5 months. The objective response rate after induction TKI was 83%. One patient had a complete response to induction erlotinib and continued erlotinib for 4 years until local progression, which was treated with CRT. Two patients completed maintenance erlotinib after CRT, and another received consolidation durvalumab. After a median follow-up of 20.5 months, only one patient developed disease recurrence, with rising ctDNA coinciding with recurrence. ctDNA remained undetectable in patients without recurrence, or low-level in a patient receiving maintenance erlotinib. Adverse events were mild and expected, and none developed pneumonitis. CONCLUSION: Induction EGFR TKI before CRT may achieve high disease control rates with promising signs of durability in patients with locally advanced EGFR-mutated NSCLC. ctDNA analysis after CRT can correlate well with clinical outcomes. Prospective studies are needed to define the role of induction EGFR TKIs in this setting

Restriction landmark genomic scanning (RLGS) spot identification by second generation virtual RLGS in multiple genomes with multiple enzyme combinations.

BackgroundRestriction landmark genomic scanning (RLGS) is one of the most successfully applied methods for the identification of aberrant CpG island hypermethylation in cancer, as well as the identification of tissue specific methylation of CpG islands. However, a limitation to the utility of this method has been the ability to assign specific genomic sequences to RLGS spots, a process commonly referred to as "RLGS spot cloning."ResultsWe report the development of a virtual RLGS method (vRLGS) that allows for RLGS spot identification in any sequenced genome and with any enzyme combination. We report significant improvements in predicting DNA fragment migration patterns by incorporating sequence information into the migration models, and demonstrate a median Euclidian distance between actual and predicted spot migration of 0.18 centimeters for the most complex human RLGS pattern. We report the confirmed identification of 795 human and 530 mouse RLGS spots for the most commonly used enzyme combinations. We also developed a method to filter the virtual spots to reduce the number of extra spots seen on a virtual profile for both the mouse and human genomes. We demonstrate use of this filter to simplify spot cloning and to assist in the identification of spots exhibiting tissue-specific methylation.ConclusionThe new vRLGS system reported here is highly robust for the identification of novel RLGS spots. The migration models developed are not specific to the genome being studied or the enzyme combination being used, making this tool broadly applicable. The identification of hundreds of mouse and human RLGS spot loci confirms the strong bias of RLGS studies to focus on CpG islands and provides a valuable resource to rapidly study their methylation