Reproducible kk-means clustering in galaxy feature data from the GAMA survey

A fundamental bimodality of galaxies in the local Universe is apparent in many of the features used to describe them. Multiple sub-populations exist within this framework, each representing galaxies following distinct evolutionary pathways. Accurately identifying and characterising these sub-populations requires that a large number of galaxy features be analysed simultaneously. Future galaxy surveys such as LSST and Euclid will yield data volumes for which traditional approaches to galaxy classification will become unfeasible. To address this, we apply a robust $k$-means unsupervised clustering method to feature data derived from a sample of 7338 local-Universe galaxies selected from the Galaxy And Mass Assembly (GAMA) survey. This allows us to partition our sample into $k$ clusters without the need for training on pre-labelled data, facilitating a full census of our high dimensionality feature space and guarding against stochastic effects. We find that the local galaxy population natively splits into $2$, $3$, $5$ and a maximum of $6$ sub-populations, with each corresponding to a distinct ongoing evolutionary mechanism. Notably, the impact of the local environment appears strongly linked with the evolution of low-mass ($M_{*} < 10^{10}$ M$_{\odot}$) galaxies, with more massive systems appearing to evolve more passively from the blue cloud onto the red sequence. With a typical run time of $\sim3$ minutes per value of $k$ for our galaxy sample, we show how $k$-means unsupervised clustering is an ideal tool for future analysis of large extragalactic datasets, being scalable, adaptable, and providing crucial insight into the fundamental properties of the local galaxy population

p53 mutations in classic and pleomorphic invasive lobular carcinoma of the breast

Contains fulltext : 110338.pdf (publisher's version ) (Open Access)BACKGROUND: p53 is a tumor suppressor that is frequently mutated in human cancers. Although alterations in p53 are common in breast cancer, few studies have specifically investigated TP53 mutations in the breast cancer subtype invasive lobular carcinoma (ILC). Recently reported conditional mouse models have indicated that functional p53 inactivation may play a role in ILC development and progression. Since reports on the detection of TP53 mutations in the relatively favorable classic and more aggressive pleomorphic variants of ILC (PILC) are rare and ambiguous, we performed a comprehensive analysis to determine the mutation status of TP53 in these breast cancer subtypes. METHODS: To increase our understanding of p53-mediated pathways and the roles they may play in the etiology of classic ILC and PILC, we investigated TP53 mutations and p53 accumulation in a cohort of 22 cases of classic and 19 cases of PILC by direct DNA sequencing and immunohistochemistry. RESULTS: We observed 11 potentially pathogenic TP53 mutations, of which three were detected in classic ILC (13.6%) and 8 in PILC (42.1%; p = 0.04). While p53 protein accumulation was not significantly different between classic and pleomorphic ILC, mutations that affected structure and protein function were significantly associated with p53 protein levels. CONCLUSION: TP53 mutations occur more frequently in PILC than classic ILC.1 april 201

Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to 300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m 2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years

Pooled analysis of prognostic impact of uPA and PAI-I in breast cancer patients

In this report we present an extension of the pooled analysis of the prognostic impact of urokinase-type plasminogen activator (uPA) and its inhibitor PAI-I in breast cancer patients. We analyzed a different endpoint, metastasis-free survival (MFS). We checked the consistency of the estimates for uPA and PAI-I for relapse-free survival (RFS) and MFS exploring possible sources of heterogeneity. Nodal status, the most important prognostic factor for breast cancer, introduced heterogeneity in the uPA/PAI-I survival analyses, reflecting the interaction between nodal status and uPA/PAI-I. The estimates for uPA and PAI-I were found to be consistent, even when a different transformation of their values was used. The heterogeneity of the separate data sets decreased if the levels of uPA and PAI-I were ranked, data sets were pooled, and the analyses corrected for the base model that included all traditional prognostic factors, and stratified by data set. We conclude that uPA and PAI-I are ready to be used in the clinic to help classify breast cancer patients into high and low risk groups