Seasonal Variations in Water Quality Parameters of the Mississippi River near St. Cloud, MN

ABSTRACT - Water quality parameters were monitored in the Mississippi River and three tributaries (Harris Channel, Watab Creek, and Sauk River) from July 1980 to April 1981. Results were correlated with water temperature and discharge to assess seasonal changes. Effects of tributary inflow on the river were determined. Planktonic carbon was estimated with the firefly luciferin-luciferase system which measures adenosine triphosphate extracted from viable cells. Particulate organic matter, planktonic carbon, and percentage viable carbon were correlated with temperature (p ~ 0.6) while nitrate plus nitrite was inversely correlated with temperature and discharge. Only the Sauk River affected the water quality of the Mississippi River by adding elevated levels of dissolved electrolytes, total phosphorus, particulate organic matter, and planktonic carbon. Relatively low levels of planktonic carbon (155 μg/1) in the river indicated good water quality. Seasonal changes in water quality parameters of rivers must be considered in designing useful monitoring programs

The Radio Sky at Meter Wavelengths: m-Mode Analysis Imaging with the Owens Valley Long Wavelength Array

A host of new low-frequency radio telescopes seek to measure the 21-cm transition of neutral hydrogen from the early universe. These telescopes have the potential to directly probe star and galaxy formation at redshifts $20 \gtrsim z \gtrsim 7$, but are limited by the dynamic range they can achieve against foreground sources of low-frequency radio emission. Consequently, there is a growing demand for modern, high-fidelity maps of the sky at frequencies below 200 MHz for use in foreground modeling and removal. We describe a new widefield imaging technique for drift-scanning interferometers, Tikhonov-regularized $m$-mode analysis imaging. This technique constructs images of the entire sky in a single synthesis imaging step with exact treatment of widefield effects. We describe how the CLEAN algorithm can be adapted to deconvolve maps generated by $m$-mode analysis imaging. We demonstrate Tikhonov-regularized $m$-mode analysis imaging using the Owens Valley Long Wavelength Array (OVRO-LWA) by generating 8 new maps of the sky north of $\delta=-30^\circ$ with 15 arcmin angular resolution, at frequencies evenly spaced between 36.528 MHz and 73.152 MHz, and $\sim$800 mJy/beam thermal noise. These maps are a 10-fold improvement in angular resolution over existing full-sky maps at comparable frequencies, which have angular resolutions $\ge 2^\circ$. Each map is constructed exclusively from interferometric observations and does not represent the globally averaged sky brightness. Future improvements will incorporate total power radiometry, improved thermal noise, and improved angular resolution -- due to the planned expansion of the OVRO-LWA to 2.6 km baselines. These maps serve as a first step on the path to the use of more sophisticated foreground filters in 21-cm cosmology incorporating the measured angular and frequency structure of all foreground contaminants.Comment: 27 pages, 18 figure

Clustering of galaxies around radio quasars at 0.5 < z < 0.8

We have observed the galaxy environments around a sample of 21 radio-loud, steep-spectrum quasars at 0.5<z<0.82, spanning several orders of magnitude in radio luminosity. The observations also include background control fields used to obtain the excess number of galaxies in each quasar field. The galaxy excess was quantified using the spatial galaxy-quasar correlation amplitude, B_gq, and an Abell-type measurement, N_0.5 (Hill & Lilly 1991). A few quasars are found in relatively rich clusters, but on average, they seem to prefer galaxy groups or clusters of approximately Abell class 0. We have combined our sample with literature samples extending down to z=0.2 and covering the same range in radio luminosity. By using Spearman statistic to disentangle redshift and luminosity dependences, we detect a weak, but significant, positive correlation between the richness of the quasar environment and the quasar's radio luminosity. However, we do not find any epoch dependence in B_gq, as has previously been reported for radio quasars and galaxies. We discuss the radio luminosity-cluster richness link and possible explanations for the weak correlation that is seen.Comment: 18 pages, 9 figures, submitted to MNRA

NUTMEG: A randomized phase II study of nivolumab and temozolomide versus temozolomide alone in newly diagnosed older patients with glioblastoma.

BACKGROUND: There is an immunologic rationale to evaluate immunotherapy in the older glioblastoma population, who have been underrepresented in prior trials. The NUTMEG study evaluated the combination of nivolumab and temozolomide in patients with glioblastoma aged 65 years and older. METHODS: NUTMEG was a multicenter 2:1 randomized phase II trial for patients with newly diagnosed glioblastoma aged 65 years and older. The experimental arm consisted of hypofractionated chemoradiation with temozolomide, then adjuvant nivolumab and temozolomide. The standard arm consisted of hypofractionated chemoradiation with temozolomide, then adjuvant temozolomide. The primary objective was to improve overall survival (OS) in the experimental arm. RESULTS: A total of 103 participants were randomized, with 69 in the experimental arm and 34 in the standard arm. The median (range) age was 73 (65-88) years. After 37 months of follow-up, the median OS was 11.6 months (95% CI, 9.7-13.4) in the experimental arm and 11.8 months (95% CI, 8.3-14.8) in the standard arm. For the experimental arm relative to the standard arm, the OS hazard ratio was 0.85 (95% CI, 0.54-1.33). In the experimental arm, there were three grade 3 immune-related adverse events which resolved, with no unexpected serious adverse events. CONCLUSIONS: Due to insufficient evidence of benefit with nivolumab, the decision was made not to transition to a phase III trial. No new safety signals were identified with nivolumab. This complements the existing series of immunotherapy trials. Research is needed to identify biomarkers and new strategies including combinations

The Radio Sky at Meter Wavelengths: m-mode Analysis Imaging with the OVRO-LWA

A host of new low-frequency radio telescopes seek to measure the 21 cm transition of neutral hydrogen from the early universe. These telescopes have the potential to directly probe star and galaxy formation at redshifts 20 ≳ z ≳ 7 but are limited by the dynamic range they can achieve against foreground sources of low-frequency radio emission. Consequently, there is a growing demand for modern, high-fidelity maps of the sky at frequencies below 200 MHz for use in foreground modeling and removal. We describe a new wide-field imaging technique for drift-scanning interferometers: Tikhonov-regularized m-mode analysis imaging. This technique constructs images of the entire sky in a single synthesis imaging step with exact treatment of wide-field effects. We describe how the CLEAN algorithm can be adapted to deconvolve maps generated by m-mode analysis imaging. We demonstrate Tikhonov-regularized m-mode analysis imaging using the Owens Valley Radio Observatory Long Wavelength Array (OVRO-LWA) by generating eight new maps of the sky north of δ = −30° with 15' angular resolution at frequencies evenly spaced between 36.528 and 73.152 MHz and ~800 mJy beam^(−1) thermal noise. These maps are a 10-fold improvement in angular resolution over existing full-sky maps at comparable frequencies, which have angular resolutions ≥2°. Each map is constructed exclusively from interferometric observations and does not represent the globally averaged sky brightness. Future improvements will incorporate total power radiometry, improved thermal noise, and improved angular resolution due to the planned expansion of the OVRO-LWA to 2.6 km baselines. These maps serve as a first step on the path to the use of more sophisticated foreground filters in 21 cm cosmology incorporating the measured angular and frequency structure of all foreground contaminants

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Developing reproducible bioinformatics analysis workflows for heterogeneous computing environments to support African genomics

Background: The Pan-African bioinformatics network, H3ABioNet, comprises 27 research institutions in 17 African countries. H3ABioNet is part of the Human Health and Heredity in Africa program (H3Africa), an African-led research consortium funded by the US National Institutes of Health and the UK Wellcome Trust, aimed at using genomics to study and improve the health of Africans. A key role of H3ABioNet is to support H3Africa projects by building bioinformatics infrastructure such as portable and reproducible bioinformatics workflows for use on heterogeneous African computing environments. Processing and analysis of genomic data is an example of a big data application requiring complex interdependent data analysis workflows. Such bioinformatics workflows take the primary and secondary input data through several computationally-intensive processing steps using different software packages, where some of the outputs form inputs for other steps. Implementing scalable, reproducible, portable and easy-to-use workflows is particularly challenging. Results: H3ABioNet has built four workflows to support (1) the calling of variants from high-throughput sequencing data; (2) the analysis of microbial populations from 16S rDNA sequence data; (3) genotyping and genome-wide association studies; and (4) single nucleotide polymorphism imputation. A week-long hackathon was organized in August 2016 with participants from six African bioinformatics groups, and US and European collaborators. Two of the workflows are built using the Common Workflow Language framework (CWL) and two using Nextflow. All the workflows are containerized for improved portability and reproducibility using Docker, and are publicly available for use by members of the H3Africa consortium and the international research community. Conclusion: The H3ABioNet workflows have been implemented in view of offering ease of use for the end user and high levels of reproducibility and portability, all while following modern state of the art bioinformatics data processing protocols. The H3ABioNet workflows will service the H3Africa consortium projects and are currently in use. All four workflows are also publicly available for research scientists worldwide to use and adapt for their respective needs. The H3ABioNet workflows will help develop bioinformatics capacity and assist genomics research within Africa and serve to increase the scientific output of H3Africa and its Pan-African Bioinformatics Network