Diversity of the Unionidae in the Rocky River, Ohio

We surveyed the freshwater mussels (Mollusca: Bivalvia: Unionidae) of the Rocky River, Ohio, USA, a river surrounded by suburban development. The survey produced 253 live unionid specimens and 572 empty shells. Most occurred in the west branch. Species living in the West Branch Rocky River included Anodontoides ferussacianus, Elliptio dilatata, Lampsilis cardium, Lampsilis radiata luteola, Lasmigona compressa, Lasmigona costata, Pyganodon grandis grandis, Strophitus undulatus undulatus, and Toxolasma parvus. Two additional species (Utterbackia imbecillis and Villosa iris iris) were represented as dead shells. Three species (Potamilis alatus, Quadrula quadrula and Leptodea fragilis) were found only near the mouth of the main stem of the river. No live mussels were found in the east branch. Although mussel diversity changed along the river, the presence of healthy mussel populations downstream of two expanding suburban areas suggests that these developments so far have minimally impacted populations

Assessing Causes of Change in the Freshwater Mussels (Bivalvia: Unionidae) in the Black River, Ohio

Habitat destruction is believed to be the number one cause of the decline in unionid mussels. Around the world, cities, towns and agriculture alter the structure of watersheds, and the Black River in Ohio may be a typical example. We investigated the diversity and abundance of unionid mussels in this watershed and compared results to urbanization locations, to site-specific appearance of the habitat and to a 1997 fish survey, as host species are another factor important to the distribution of unionid mussels. Although shells were found for 21 species, only 11 of these species were found alive. Seven of the species represented only by shells occurred only in the urbanized lower main stem of the river and less than five shells were found for each. Most of these shells were old and worn. Furthermore, the present assemblage in the main stem varied from shells obtained at a nearby archeological site, and from a voucher set of species obtained at the turn of the 20th Century. Mussel communities higher in the river and those in tributaries were less diverse, but abundance of the species present was higher than in the main stem. A lack of fish hosts may limit mussel diversity, as hosts for several species present in the main stem do not reside higher in the watershed. Overall, mussel assemblages in the Black River appear typical for the region with relatively abundant, but low diversity communities upstream of the cities that line Lake Erie\u27s coast and diverse, but small and potentially threatened, populations in the urban regions

Long-term retention of radium in female former dial workers

The results of measurements of radium contents in 13 women made over 20 years or more were used to measure radium retention. The women were all employed at different studios of the same Illinois plant as luminous dial workers for up to 4 years. The data for each woman were fitted to an exponential function of time, yielding a biological half-life of approximately 30 to 60 years. There was a strong negative correlation between elimination rate and body content, suggesting an effect of radiation of bone resorption or remodelling. A strong negative correlation was observed between the elimination rate and the reduced x-ray score, a measure of bone damage observed radiographically in the same subjects. Age at first exposure and duration of exposure were not significantly correlated with the elimination rate. The concentration of radium in soft tissues from eight other subjects, expressed as a percentage of the terminal radium content, decreased with increasing body content, providing further evidence that radiation was affecting the elimination rate. An effect of radiation on the late retention of radium has been demonstrated. 10 references, 4 figures, 2 tables

USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since tumor's frequency and severity differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study evaluates the generalization ability of CNN-based architectures on three T2-weighted MRI datasets, each one consisting of a different number of patients and heterogeneous image characteristics, collected by different institutions. The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations. USE-Net is compared against three state-of-the-art CNN-based architectures (i.e., U-Net, pix2pix, and Mixed-Scale Dense Network), along with a semi-automatic continuous max-flow model. The results show that training on the union of the datasets generally outperforms training on each dataset separately, allowing for both intra-/cross-dataset generalization. Enc USE-Net shows good overall generalization under any training condition, while Enc-Dec USE-Net remarkably outperforms the other methods when trained on all datasets. These findings reveal that the SE blocks' adaptive feature recalibration provides excellent cross-dataset generalization when testing is performed on samples of the datasets used during training.Comment: 44 pages, 6 figures, Accepted to Neurocomputing, Co-first authors: Leonardo Rundo and Changhee Ha

Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance.

Funder: National Institute of Health Research Cambridge Biomedical Research CentreFunder: Engineering and Physical Sciences Research Council Imaging Centre in Cambridge and ManchesterFunder: Cambridge Experimental Cancer Medicine CentreFunder: Gates Cambridge Trust; doi: http://dx.doi.org/10.13039/501100005370OBJECTIVES: To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS). METHODS: The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5-16 years' experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong's test. RESULTS: The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34-0.77). CONCLUSIONS: PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients. KEY POINTS: • The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches. • The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up. • The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.The authors acknowledge support from National Institute of Health Research Cambridge Biomedical Research Centre, Cancer Research UK (Cambridge Imaging Centre grant number C197/A16465), the Engineering and Physical Sciences Research Council Imaging Centre in Cambridge and Manchester, and the Cambridge Experimental Cancer Medicine Centre. T. Nazarenko is supported by a Medical Research Council grant (MR/R02524X/1). A. Suvorov is supported by the Ministry of Science and Higher Education of the Russian Federation within the programme developing World-Class Research Centres "Digital Biodesign and Personalized Healthcare" (075-15-2020-926)

Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events

open20siAcknowledgements: EPVL is undertaking a PhD funded by the Cambridge School of Clinical Medicine, Frank Edward Elmore Fund and the Medical Research Council’s Doctoral Training Partnership [award reference: 1966157]. JMT is supported by a Wellcome Trust Clinical Research Career Development Fellowship [211100/Z/18/Z], the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre and the British Heart Foundation Cambridge Centre of Research Excellence. NRE was supported by a Research Training Fellowship from The Dunhill Medical Trust [RTF44/0114]. MMC was supported by fellowships from the Royal College of Surgeons of England, and the British Heart Foundation [BHF; FS/16/29/31957]. HP is undertaking a PhD with a BHF CRE studentship. FJG is an NIHR Senior Investigator. LR and ES were supported by The Mark Foundation for Cancer Research and Cancer Research UK (CRUK) Cambridge Centre [C9685/A25177]. MR is supported by AstraZeneca Oncology R&D. ES receives additional support provided by the NIHR Cambridge Biomedical Research Centre. FAG receives funding from CRUK. EAW receives support from the NIHR CRN. CBS acknowledges support from the Leverhulme Trust project on ‘Breaking the non-convexity barrier’, the Philip Leverhulme Prize, the EPSRC grants EP/S026045/1 and EP/T003553/1, the EPSRC Centre Nr. EP/N014588/1, the Wellcome Innovator Award RG98755, European Union Horizon 2020 research and innovation programmes under the Marie Skodowska-Curie grant agreement No. 777826 NoMADS and No. 691070 CHiPS, the Cantab Capital Institute for the Mathematics of Information and the Alan Turing Institute. JHFR is part-supported by the NIHR Cambridge Biomedical Research Centre, the British Heart Foundation, HEFCE, the Wellcome Trust and the EPSRC grant [EP/N014588/1] for the University of Cambridge Centre for Mathematical Imaging in Healthcare. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.Radiomics, quantitative feature extraction from radiological images, can improve disease diagnosis and prognostication. However, radiomic features are susceptible to image acquisition and segmentation variability. Ideally, only features robust to these variations would be incorporated into predictive models, for good generalisability. We extracted 93 radiomic features from carotid artery computed tomography angiograms of 41 patients with cerebrovascular events. We tested feature robustness to region-of-interest perturbations, image pre-processing settings and quantisation methods using both single- and multi-slice approaches. We assessed the ability of the most robust features to identify culprit and non-culprit arteries using several machine learning algorithms and report the average area under the curve (AUC) from five-fold cross validation. Multi-slice features were superior to single for producing robust radiomic features (67 vs. 61). The optimal image quantisation method used bin widths of 25 or 30. Incorporating our top 10 non-redundant robust radiomics features into ElasticNet achieved an AUC of 0.73 and accuracy of 69% (compared to carotid calcification alone [AUC: 0.44, accuracy: 46%]). Our results provide key information for introducing carotid CT radiomics into clinical practice. If validated prospectively, our robust carotid radiomic set could improve stroke prediction and target therapies to those at highest risk.noneLe E.P.V.; Rundo L.; Tarkin J.M.; Evans N.R.; Chowdhury M.M.; Coughlin P.A.; Pavey H.; Wall C.; Zaccagna F.; Gallagher F.A.; Huang Y.; Sriranjan R.; Le A.; Weir-McCall J.R.; Roberts M.; Gilbert F.J.; Warburton E.A.; Schonlieb C.-B.; Sala E.; Rudd J.H.F.Le E.P.V.; Rundo L.; Tarkin J.M.; Evans N.R.; Chowdhury M.M.; Coughlin P.A.; Pavey H.; Wall C.; Zaccagna F.; Gallagher F.A.; Huang Y.; Sriranjan R.; Le A.; Weir-McCall J.R.; Roberts M.; Gilbert F.J.; Warburton E.A.; Schonlieb C.-B.; Sala E.; Rudd J.H.F

Hyperpolarized13c mri of tumor metabolism demonstrates early metabolic response to neoadjuvant chemotherapy in breast cancer

Purpose: To compare hyperpolarized carbon 13 (13C) MRI with dynamic contrast material–enhanced (DCE) MRI in the detection of early treatment response in breast cancer. Materials and Methods: In this institutional review board–approved prospective study, a woman with triple-negative breast cancer (age, 49 years) underwent13C MRI after injection of hyperpolarized [1–carbon 13 {13C}]-pyruvate and DCE MRI at 3 T at baseline and after one cycle of neoadjuvant therapy. The13C-labeled lactate-to-pyruvate ratio derived from hyperpolarized13C MRI and the pharmacokinetic parameters transfer constant (Ktrans) and washout parameter (kep ) derived from DCE MRI were compared before and after treatment. Results: Exchange of the13C label between injected hyperpolarized [1-13C]-pyruvate and the endogenous lactate pool was observed, catalyzed by the enzyme lactate dehydrogenase. After one cycle of neoadjuvant chemotherapy, a 34% reduction in the13C-labeled lactate-to-pyruvate ratio resulted in correct identification of the patient as a responder to therapy, which was subsequently confirmed via a complete pathologic response. However, DCE MRI showed an increase in mean Ktrans (132%) and mean kep (31%), which could be incorrectly interpreted as a poor response to treatment. Conclusion: Hyperpolarized13C MRI enabled successful identification of breast cancer response after one cycle of neoadjuvant chemotherapy and may improve response prediction when used in conjunction with multiparametric proton MRI

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains

Hyperpolarized 13C-MRI of Tumor Metabolism Demonstrates Early Metabolic Response to Neoadjuvant Chemotherapy in Breast Cancer

Purpose: To compare hyperpolarized carbon-13 (13C)-MRI with dynamic contrast-enhanced MRI (DCE-MRI) for detecting early treatment response in breast cancer. Materials and Methods: In this institutional review board-approved prospective study, one woman with triple-negative breast cancer (age 49) underwent 13C-MRI following injection of hyperpolarized [1-13C]pyruvate and DCE-MRI at 3 T at baseline and after a single cycle of neoadjuvant therapy. The 13C-lactate/13C-pyruvate ratio derived from hyperpolarized 13C-MRI and the pharmacokinetic parameters Ktrans and kep derived from DCE-MRI were compared, before and after treatment. Results: Exchange of the 13C-label between injected hyperpolarized [1-13C]pyruvate and the endogenous lactate pool was demonstrated, catalyzed by the enzyme lactate dehydrogenase. After one cycle of neoadjuvant chemotherapy, a 34% reduction in the 13C-lactate/13C-pyruvate ratio was shown to correctly identify the patient as a responder to therapy, which was subsequently confirmed by a complete pathologic response. However, DCE-MRI showed an increase in the pharmacokinetic parameters Ktrans (132%) and kep (31%), which could be incorrectly interpreted as a poor response to treatment. Conclusion: Hyperpolarized 13C-MRI successfully identified response in breast cancer after a single cycle of neoadjuvant chemotherapy and may improve response prediction when used in conjunction with multiparametric proton MRI.This work was supported by a Wellcome Trust Strategic Award, Cancer Research UK (CRUK; Grants C8742/A18097, C19212/ A16628, C19212/A911376, and C197/A16465), the Austrian Science Fund (Grant J4025-B26), the CRUK Cambridge Centre, the CRUK & Engineering and Physical Sciences Research Council Cancer Imaging Centre in Cambridge and Manchester, the Mark Foundation for Cancer Research and Cancer Research UK Cambridge Centre (Grant C9685/A25177), CRUK National Cancer Imaging Translational Accelerator Award, Addenbrooke’s Charitable Trust, the National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge Experimental Cancer Medicine Centre, and Cambridge University Hospitals National Health Service Foundation Trust

High performance computing for haplotyping: Models and platforms

\u3cp\u3eThe reconstruction of the haplotype pair for each chromosome is a hot topic in Bioinformatics and Genome Analysis. In Haplotype Assembly (HA), all heterozygous Single Nucleotide Polymorphisms (SNPs) have to be assigned to exactly one of the two chromosomes. In this work, we outline the state-of-the-art on HA approaches and present an in-depth analysis of the computational performance of GenHap, a recent method based on Genetic Algorithms. GenHap was designed to tackle the computational complexity of the HA problem by means of a divide-et-impera strategy that effectively leverages multi-core architectures. In order to evaluate GenHap’s performance, we generated different instances of synthetic (yet realistic) data exploiting empirical error models of four different sequencing platforms (namely, Illumina NovaSeq, Roche/454, PacBio RS II and Oxford Nanopore Technologies MinION). Our results show that the processing time generally decreases along with the read length, involving a lower number of sub-problems to be distributed on multiple cores.\u3c/p\u3