Methane Production Pathway Regulated Proximally by Substrate Availability and Distally by Temperature in a High-Latitude Mire Complex

Projected 21st century changes in high-latitude climate are expected to have significant impacts on permafrost thaw, which could cause substantial increases in emissions to the atmosphere of carbon dioxide (CO2) and methane (CH4, which has a global warming potential 28 times larger than CO2 over a 100-year horizon). However, predicted CH4 emission rates are very uncertain due to difficulties in modeling complex interactions among hydrological, thermal, biogeochemical, and plant processes. Methanogenic production pathways (i.e., acetoclastic [AM] and hydrogenotrophic [HM]) and the magnitude of CH4 emissions may both change as permafrost thaws, but a mechanistic analysis of controls on such shifts in CH4 dynamics is lacking. In this study, we reproduced observed shifts in CH4 emissions and production pathways with a comprehensive biogeochemical model (ecosys) at the Stordalen Mire in subarctic Sweden. Our results demonstrate that soil temperature changes differently affect AM and HM substrate availability, which regulates magnitudes of AM, HM, and thereby net CH4 emissions. We predict very large landscape-scale, vertical, and temporal variations in the modeled HM fraction, highlighting that measurement strategies for metrics that compare CH4 production pathways could benefit from model informed scale of temporal and spatial variance. Finally, our findings suggest that the warming and wetting trends projected in northern peatlands could enhance peatland AM fraction and CH4 emissions even without further permafrost degradation

Expression of mRNA for the neurotrophin receptor trkC in neuroblastomas with favourable tumour stage and good prognosis.

Childhood neuroblastoma tumours of the sympathetic nervous system show a remarkable clinical heterogeneity ranging from spontaneous regression to unfavourable outcome despite intensive therapy. Favourable neuroblastomas often express high levels of trkA mRNA, encoding the tyrosine kinase receptor for nerve growth factor. We have investigated mRNA expression for the neurotrophin receptor trkC in 23 primary neuroblastomas using a sensitive RNAase protection assay. TrkC expression was detected in 19 of these tumours at highly variable levels with a 300-fold difference between the highest and lowest values. Significantly higher levels of trkC mRNA were found in tumours from patients with favourable features such as low age (P < 0.012), favourable tumour stage (P < 0.012) and favourable prognosis (P < 0.05). Children with intermediate or high trkC mRNA expression had better prognosis compared with those with low or undetectable levels (83.3% vs 20%, P = 0.005). Further characterisation of trkC mRNA expression by reverse transcriptase-polymerase chain reaction (RT-PCR) showed that mRNA encoding the full-length cytoplasmic tyrosine kinase domain of the receptor was only expressed in a subset of favourable tumours. These data show that favourable neuroblastomas may express the full trkC receptor while advanced tumours, in particular MYCN-amplified neuroblastoma, seem to either express no trkC or truncated trkC receptors of as yet unknown biological function. These data are suggestive of a role for trkC and its preferred ligand neutotrophin-3, NT-3, in neuroblastoma differentiation and/or regression

Cardiovascular autonomic neuropathy associated with carotid atherosclerosis in Type 2 diabetic patients.

AimsTo clarify if cardiovascular autonomic neuropathy is associated with carotid artery atherosclerotic plaques in Type 2 diabetic patients. MethodsCardiovascular autonomic nerve function was related to carotid artery ultrasound in 61 Type 2 diabetic patients 5-6 years after diagnosis of diabetes. ResultsCardiovascular autonomic neuropathy [abnormal age corrected expiration/inspiration (E/I) ratio or acceleration index (AI)] was found in 13/61 (21%) patients. Patients with cardiovascular autonomic neuropathy showed increased degree of stenosis in the common carotid artery (24.6 ± 13.2% vs. 14.7 ± 9.2%; P = 0.014) and a tendency towards a higher plaque score (4.0 ± 1.7 vs. 3.2 ± 1.6; P = 0.064). Controlled for age, AI correlated inversely with degree of stenosis (r = -0.39; P = 0.005), plaque score (r = -0.39; P = 0.005), and mean (r = -0.33; P = 0.018) and maximum (r = -0.39; P = 0.004) intima-media thickness in the common carotid artery. In contrast, E/I ratio correlated only slightly with mean intima-media thickness in the common carotid artery (r = -0.28; P = 0.049). ConclusionsCardiovascular autonomic neuropathy was associated with carotid atherosclerosis in Type 2 diabetic patients. Abnormal E/I ratios reflect efferent structural damage to parasympathetic nerves whereas abnormal AI reflects afferent autonomic dysfunction possibly due to impaired baroreceptor sensitivity secondary to carotid atherosclerosis

An Image-Analysis-Based Method for the Prediction of Recombinant Protein Fiber Tensile Strength

Silk fibers derived from the cocoon of silk moths and the wide range of silks produced by spiders exhibit an array of features, such as extraordinary tensile strength, elasticity, and adhesive properties. The functional features and mechanical properties can be derived from the structural composition and organization of the silk fibers. Artificial recombinant protein fibers based on engineered spider silk proteins have been successfully made previously and represent a promising way towards the large-scale production of fibers with predesigned features. However, for the production and use of protein fibers, there is a need for reliable objective quality control procedures that could be automated and that do not destroy the fibers in the process. Furthermore, there is still a lack of understanding the specifics of how the structural composition and organization relate to the ultimate function of silk-like fibers. In this study, we develop a new method for the categorization of protein fibers that enabled a highly accurate prediction of fiber tensile strength. Based on the use of a common light microscope equipped with polarizers together with image analysis for the precise determination of fiber morphology and optical properties, this represents an easy-to-use, objective non-destructive quality control process for protein fiber manufacturing and provides further insights into the link between the supramolecular organization and mechanical functionality of protein fibers

Artificial Intelligence Meets IS Researchers: Can It Replace Us?

Given we live in an era with accelerating digitization and rapid advances in artificial intelligence (AI), AI may eventually automate more job tasks. However, researchers have scarcely if at all critically analyzed how AI will automate such tasks and what professions it will automate more than others. Some studies suggest that AI cannot conduct highly creative and knowledge-intensive tasks. Yet, AI algorithms have generated creative art pieces that even art critics could not distinguish from human-drawn paintings. As IS (and most other) researchers, we pride ourselves on our work’s scarcity, novelty, and creativity. In this context, we report on a panel at the 40th International Conference for Information Systems that debated whether AI can and will replace our major activity, IS research, or even IS researchers themselves

An integral method for solving nonlinear eigenvalue problems

We propose a numerical method for computing all eigenvalues (and the corresponding eigenvectors) of a nonlinear holomorphic eigenvalue problem that lie within a given contour in the complex plane. The method uses complex integrals of the resolvent operator, applied to at least $k$ column vectors, where $k$ is the number of eigenvalues inside the contour. The theorem of Keldysh is employed to show that the original nonlinear eigenvalue problem reduces to a linear eigenvalue problem of dimension $k$. No initial approximations of eigenvalues and eigenvectors are needed. The method is particularly suitable for moderately large eigenvalue problems where $k$ is much smaller than the matrix dimension. We also give an extension of the method to the case where $k$ is larger than the matrix dimension. The quadrature errors caused by the trapezoid sum are discussed for the case of analytic closed contours. Using well known techniques it is shown that the error decays exponentially with an exponent given by the product of the number of quadrature points and the minimal distance of the eigenvalues to the contour

RNA sequencing-based single sample predictors of molecular subtype and risk of recurrence for clinical assessment of early-stage breast cancer

BackgroundMultigene expression assays for molecular subtypes and biomarkers can aid clinical management of early invasive breast cancer. Based on RNA-sequencing we aimed to develop single-sample predictor (SSP) models for conventional clinical markers, molecular intrinsic subtype and risk of recurrence (ROR).MethodsA uniformly accrued breast cancer cohort of 7743 patients with RNA-sequencing data from fresh tissue was divided into a training set and a reserved test set. We trained SSPs for PAM50 molecular subtypes and ROR assigned by nearest-centroid (NC) and SSPs for conventional clinical markers from histopathology data. Additionally, SSP classifications were compared with Prosigna® in two external cohorts. Prognostic value was assessed using distant recurrence-free interval.ResultsIn the test set, agreement between SSP and NC classifications for PAM50 (five subtypes) and Subtype (four subtypes) was high (85%, Kappa=0.78) and very high (90%, Kappa=0.84) respectively. Accuracy for ROR risk category was high (84%, Kappa=0.75, weighted Kappa=0.90). The prognostic value for SSP and NC was assessed as equivalent. Agreement for SSP and histopathology was very high or high for receptor status, while moderate and poor for Ki67 status and Nottingham histological grade, respectively. SSP concordance with Prosigna® was high for subtype and moderate and high for ROR risk category. In pooled analysis, concordance between SSP and Prosigna® for emulated treatment recommendation for chemotherapy (yes vs. no) was high (85%, Kappa=0.66). In postmenopausal ER+/HER2-/N0 patients SSP application suggested changed treatment recommendations for up to 17% of patients, with nearly balanced escalation and de-escalation of chemotherapy.ConclusionsSSP models for histopathological variables, PAM50, and ROR classifications can be derived from RNA-sequencing that closely matches clinical tests. Agreement and outcome analyses suggest that NC and SSP models are interchangeable on a group-level and nearly so on a patient level. Retrospective evaluation in postmenopausal ER+/HER2-/N0 patients suggested that molecular testing could lead to a changed therapy recommendation for almost one-fifth of patients

Trends in HbA1c thresholds for initiation of hypoglycemic agents:Impact of changed recommendations for older and frail patients

Aims: Less strict glycated hemoglobin (HbA1c) thresholds have been recommended in older and/or frail type 2 diabetes (T2D) patients than in younger and less frail patients for initiating hypoglycemic agents since 2011. We aimed to assess trends in HbA1c thresholds at initiation of a first hypoglycemic agent(s) in T2D patients and the influence of age and frailty on these trends. Materials and methods: The groningen initiative to analyze type 2 diabetes treatment (GIANTT) database was used, which includes primary care T2D patients from the north of the Netherlands. Patients initiating a first non-insulin hypoglycemic agent(s) between 2008 and 2014 with an HbA1c measurement within 120 days before initiation were included. The influence of calendar year, age, or frailty and the interaction between calendar year and age or frailty were assessed using multilevel regression analyses adjusted for confounders. Results: We included 4588 patients. The mean HbA1c threshold at treatment initiation was 7.4% up to 2010, decreasing to 7.1% in 2011 and increasing to 7.4% in 2014. This quadratic change over the years was significant (P 0.05). Conclusions: HbA1c thresholds at initiation of a first hypoglycemic agent(s) changed significantly over time, showing a decrease after 2010 and an increase after 2012. The HbA1c threshold at initiation was not influenced by age or frailty, which is in contrast with recommendations for more personalized treatment

Applying REWIND cardiovascular disease criteria to SUSTAIN 6 and PIONEER 6: An exploratory analysis of cardiovascular outcomes with semaglutide

In the REWIND trial, dulaglutide reduced cardiovascular (CV) risk versus placebo in patients with type 2 diabetes in both the “established CV disease” (CVD) and “CV risk factor” subgroups. The SUSTAIN 6 and PIONEER 6 trials of semaglutide used different criteria for established CVD from those used in REWIND. The present post hoc analysis assessed the effect of semaglutide on major adverse CV events (MACE) in a pooled population of SUSTAIN 6 and PIONEER 6 patients, re-categorized into CV risk subgroups using the REWIND CVD criteria. In the pooled analysis (n = 6480), a lower percentage of patients were in the established CVD subgroup, when using the REWIND CVD criteria, compared with the original trial CVD criteria (66.5% vs. 83.8%, respectively). After re-categorization, the risk of MACE was significantly lower with semaglutide versus placebo in the established CVD subgroup (hazard ratio [HR] 0.74, 95% confidence interval [CI] 0.59, 0.92) and nonsignificantly lower in the CV risk factor subgroup (HR 0.84, 95% CI 0.55, 1.28) (P-interaction = 0.60). These results suggest that the CV effects of semaglutide may extend to patients with type 2 diabetes across the CV risk continuum