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Abstract Background Prostate cancer is an extremely heterogeneous disease. Despite being clinically similar, some tumours are more likely to recur after surgery compared to others. Distinguishing those that need adjuvant or salvage radiotherapy will improve patient outcomes. The goal of this study was to identify circulating microRNA that could independently predict prostate cancer patient risk stratification after radical prostatectomy. Methods Seventy-eight prostate cancer patients were recruited at the Odette Cancer Centre in Sunnybrook Health Sciences Centre. All patients had previously undergone radical prostatectomy. Blood samples were collected simultaneously for PSA testing and miRNA analysis using NanoString nCounter technology. Of the 78 samples, 75 had acceptable miRNA quantity and quality. Patients were stratified into high- and low-risk categories based on Gleason score, pathological T stage, surgical margin status, and diagnostic PSA: patients with Gleason ≥ 8; pT3a and positive margin; pT3b and any margin; or diagnostic PSA > 20 µg/mL were classified as high-risk (n = 44) and all other patients were classified as low-risk (n = 31). Results Using our patient dataset, we identified a four-miRNA signature (miR-17, miR-20a, miR-20b, miR-106a) that can distinguish high- and low-risk patients, in addition to their pathological tumour stage. High expression of these miRNAs is associated with shorter time to biochemical recurrence in the TCGA dataset. These miRNAs confer an aggressive phenotype upon overexpression in vitro. Conclusions This proof-of-principle report highlights the potential of circulating miRNAs to independently predict risk stratification of prostate cancer patients after radical prostatectomy.

The descriptive biology of torrent mites (Parasitengona: Torrenticolidae: Torrenticola) of North America (north of Mexico) is investigated using integrative methods. Material examined includes approximately 2,300 specimens from nearly 500 localities across the United States and Canada, and a few collections in Mexico and Central America. Species hypotheses are derived from a phylogenetic analysis of the barcoding region of cytochrome c oxidase subunit 1 (COI) for 476 specimens and supported with morphology and biogeography. Relationships between species are examined with a combined analysis of COI and two expansion regions (D2–3) of the large ribosomal subunit (28S rDNA) for 57 specimens. All previously described species from the US and Canada are examined. Our results indicate the need to synonymize four species: T. mercedensis (Marshall, 1943) is a junior synonym of T. sierrensis (Marshall, 1943); T. rectiforma Habeeb, 1974 is a junior synonym of T. ellipsoidalis (Marshall, 1943); T. neoconnexa Habeeb, 1957 is a junior synonym of T. magnexa Habeeb, 1955; and T. esbelta Cramer, 1992 is a junior synonym of T. boettgeri KO Viets, 1977. We describe 66 new species and re-describe all previously described regional species. Our findings indicate that total diversity of Torrenticola in the United States and Canada comprises 90 species, 57 known from the east and 33 from the west. We organize these species into four species complexes that include 13 identification groups. An additional 13 species do not fit within an identification group. The southern Appalachians are suspected to contain the highest concentration of remaining undescribed diversity. A key is provided to all known species in the US and Canada.

The simultaneous measurement of cortisol with its downstream metabolites in human eccrine sweat is a sensitive approach to capture minute-to-minute stress responses. This study investigates exercise stress induced time dependent dynamic changes in cortisol, cortisone and downstream inactive cortisol metabolites in human eccrine sweat using a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. Cortisol and metabolite production (change in concentration over time) was measured in sweat at different time points during an administered exercise stress session with four healthy volunteers. Biomarker production plots were found to be highly individualized and sensitive to stress interventions such as exercise, and corresponded with stress response measures such as increases in heart rate. The LC-MS/MS method yielded baseline resolution between cortisol and cortisol metabolites with lower levels of detection and quantitation for each compound below 1 partper-billion (ppb). Cortisol and cortisol metabolites were found at concentrations ranging from 1 – 25 ppb in human eccrine sweat. They were also found to be stable in sweat with respect to temperature (37 C for up to 5 hours), pH (3-9) and freeze/thaw cycles (up to 4) This indicates that changes in these biomarker concentrations and their rate of production are due to stress-related physiological enzyme activation, rather than passive degradation in sweat. The physiological status of enzyme activation is thus captured and preserved in human eccrine sweat samples. This is advantageous for the development of wearable devices and methodologies which can assess human health, stress, wellbeing and performance.

Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) and fitting. Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO2 (ffCO2) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO2 emissions and synthetic observations of ffCO2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of 2. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.

BackgroundDespite marked recent improvements in long-read sequencing technology, the assembly of diploid genomes remains a difficult task. A major obstacle is distinguishing between alternative contigs that represent highly heterozygous regions. If primary and secondary contigs are not properly identified, the primary assembly will overrepresent both the size and complexity of the genome, which complicates downstream analysis such as scaffolding.ResultsHere we illustrate a new method, which we call HapSolo, that identifies secondary contigs and defines a primary assembly based on multiple pairwise contig alignment metrics. HapSolo evaluates candidate primary assemblies using BUSCO scores and then distinguishes among candidate assemblies using a cost function. The cost function can be defined by the user but by default considers the number of missing, duplicated and single BUSCO genes within the assembly. HapSolo performs hill climbing to minimize cost over thousands of candidate assemblies. We illustrate the performance of HapSolo on genome data from three species: the Chardonnay grape (Vitis vinifera), with a genome of 490 Mb, a mosquito (Anopheles funestus; 200 Mb) and the Thorny Skate (Amblyraja radiata; 2650 Mb).ConclusionsHapSolo rapidly identified candidate assemblies that yield improvements in assembly metrics, including decreased genome size and improved N50 scores. Contig N50 scores improved by 35%, 9% and 9% for Chardonnay, mosquito and the thorny skate, respectively, relative to unreduced primary assemblies. The benefits of HapSolo were amplified by down-stream analyses, which we illustrated by scaffolding with Hi-C data. We found, for example, that prior to the application of HapSolo, only 52% of the Chardonnay genome was captured in the largest 19 scaffolds, corresponding to the number of chromosomes. After the application of HapSolo, this value increased to ~ 84%. The improvements for the mosquito's largest three scaffolds, representing the number of chromosomes, were from 61 to 86%, and the improvement was even more pronounced for thorny skate. We compared the scaffolding results to assemblies that were based on PurgeDups for identifying secondary contigs, with generally superior results for HapSolo.

The purpose of this descriptive qualitative study was to examine the quality elements of online learning in a regional doctoral program. Utilizing the six quality dimensions of Hathaway's (2009) theory of online learning quality as a framework, the study investigated instructor-learner, learner-learner, learner-content, leaner-interface, learner-instructional strategies, and social presence in order to explore the frequency and importance of these elements. The study's findings were in alignment with the review of literature. Course design, instructor's facilitation, and student interaction were factors impacting learning outcomes (Eom, Wen, & Ashill, 2006). Faculty participation was found to dramatically improve the performance and satisfaction of students (Arbaugh & Rau, 2007; Hrastinski, 2009). Subsequently, three conclusions emerged from the study. Conclusion one revealed the importance of doctoral students and faculty interaction. Conclusion two revealed that instructor to learner interaction is intentional. Conclusion three revealed that instructor to learner interaction was an important factor in increasing doctoral student performance in the online learning environment.