Native vegetation of the Canterbury Plains of South Island in New Zealand has been heavily modified by agriculture and now occupies less than 0.5% of the total land area. With recent large-scale conversion to intensive dairy farming, restoration of native plants and biodiversity into a modern agricultural matrix creates a significant challenge. Native species are adapted to low nitrogen (N) environments but fertilizers and effluents have substantially raised soil N loadings. We investigated the interactions of selected native species to elevated soil N, using field studies and glasshouse-based nutrient trials. Growth and uptake of N by perennial ryegrass provided a reference. At restoration sites, several native species had similar foliar N concentrations to ryegrass. Deciduous and N-fixing species of tree had highest concentrations. There was significant inter-species variation in soil mineral N concentrations in native plant rhizospheres, differing substantially to the root zone of ryegrass. Pot trials revealed that native species tolerate high N-loadings (up to 1600 kg ha-1), although there was a negligible or no significant growth response. Among the native plants, monocots (tussock grass, sedge and NZ flax) assimilated most N, although total N assimilation by ryegrass would exceed that of native species at field productivity rates. Nevertheless, the deeper rhizospheres of native species may reduce nitrate leaching when planted on the margins of agricultural land or for effluent disposal. Selected native plant species could contribute to the sustainable management of N in intensive agricultural landscapes. Highlights:  Native species are tolerant to elevated soil N, with negligible growth response  Low-N adapted plants show species-specific traits that include luxury N uptake  Differences rhizosphere N pools exist between native species and with ryegrass  Planting natives may help to provide sustainable agricultural management of N

Aims To investigate the effects of sodium‐glucose co‐transporter‐2 (SGLT2) inhibitors vs. dipeptidyl peptidase‐4 (DPP‐4) inhibitors on renal function preservation (RFP) using real‐world data of patients with type 2 diabetes in Japan, and to identify which subgroups of patients obtained greater RFP benefits with SGLT2 inhibitors vs. DPP‐4 inhibitors. Methods We retrospectively analysed claims data recorded in the Medical Data Vision database in Japan of patients with type 2 diabetes (aged ≥18 years) prescribed any SGLT2 inhibitor or any DPP‐4 inhibitor between May 2014 and September 2016 (identification period), in whom estimated glomerular filtration rate (eGFR) was measured at least twice (baseline, up to 6 months before the index date; follow‐up, 9 to 15 months after the index date) with continuous treatment until the follow‐up eGFR. The endpoint was the percentage of patients with RFP, defined as no change or an increase in eGFR from baseline to follow‐up. A proprietary supervised learning algorithm (Q‐Finder; Quinten, Paris, France) was used to identify the profiles of patients with an additional RFP benefit of SGLT2 inhibitors vs. DPP‐4 inhibitors. Results Data were available for 990 patients prescribed SGLT2 inhibitors and 4257 prescribed DPP‐4 inhibitors. The proportion of patients with RFP was significantly greater in the SGLT2 inhibitor group (odds ratio 1.27; P = 0.01). The Q‐Finder algorithm identified four clinically relevant subgroups showing superior RFP with SGLT2 inhibitors (P < 0.1): no hyperlipidaemia and eGFR ≥79 mL/min/1.73 m2; eGFR ≥79 mL/min/1.73 m2 and diabetes duration ≤1.2 years; eGFR ≥75 mL/min/1.73 m2 and use of antithrombotic agents; and haemoglobin ≤13.4 g/dL and LDL cholesterol ≥95.1 mg/dL. In each profile, glycaemic control was similar in the two groups. Conclusion SGLT2 inhibitors were associated with more favourable RFP vs. DPP‐4 inhibitors in patients with certain profiles in real‐world settings in Japan.

Addressing the heterogeneity of both the outcome of a disease and the treatment response to an intervention is a mandatory pathway for regulatory approval of medicines. In randomized clinical trials (RCTs), confirmatory subgroup analyses focus on the assessment of drugs in predefined subgroups, while exploratory ones allow a posteriori the identification of subsets of patients who respond differently. Within the latter area, subgroup discovery (SD) data mining approach is widely used—particularly in precision medicine—to evaluate treatment effect across different groups of patients from various data sources (be it from clinical trials or real-world data). However, both the limited consideration by standard SD algorithms of recommended criteria to define credible subgroups and the lack of statistical power of the findings after correcting for multiple testing hinder the generation of hypothesis and their acceptance by healthcare authorities and practitioners. In this paper, we present the Q-Finder algorithm that aims to generate statistically credible subgroups to answer clinical questions, such as finding drivers of natural disease progression or treatment response. It combines an exhaustive search with a cascade of filters based on metrics assessing key credibility criteria, including relative risk reduction assessment, adjustment on confounding factors, individual feature’s contribution to the subgroup’s effect, interaction tests for assessing between-subgroup treatment effect interactions and tests adjustment (multiple testing). This allows Q-Finder to directly target and assess subgroups on recommended credibility criteria. The top-k credible subgroups are then selected, while accounting for subgroups’ diversity and, possibly, clinical relevance. Those subgroups are tested on independent data to assess their consistency across databases, while preserving statistical power by limiting the number of tests. To illustrate this algorithm, we applied it on the database of the International Diabetes Management Practice Study (IDMPS) to better understand the drivers of improved glycemic control and rate of episodes of hypoglycemia in type 2 diabetics patients. We compared Q-Finder with state-of-the-art approaches from both Subgroup Identification and Knowledge Discovery in Databases literature. The results demonstrate its ability to identify and support a short list of highly credible and diverse data-driven subgroups for both prognostic and predictive tasks.

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Green for arginine-rich basic proteins. The nuclear area of in utero-incubated sperm was increased as compared to the non-incubated one. This increase was especially evident after an 18 to 24 hrs incubation. Differences in staining ability would be due to modifications in chromatin structure. The significance of these changes in relation to the preparation of spermatozoa for fertilization has been discussed. The appearance of abnormalities on some spermatozoa, seen after staining with Fast-Green, might indicate a deleterious effect of the uterine environment on already altered spermatozoa.Introduction.

Différents auteurs (P AR ez, P!r!r, et VuNDpj!iy, 19 6 0 ; S AI , ISBURY , B IRGE , de

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The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, whether it is about a disease or a targeted population in real life. Nevertheless, contrary to the so-called conventional biostatistical methods where numerous guidelines exist, the standardization of data science approaches in clinical research remains a little discussed subject. This results in a significant diversity in the execution of data science projects, whether in terms of algorithms used, reliability and credibility of the designed approach. Taking the path of parsimonious and judicious choice of both algorithms and implementations at each stage, this paper proposes Qluster, a practical workflow for performing clustering tasks. Indeed, this workflow makes a compromise between (1) genericity, as it is suitable regardless of the data volume (small/big) and regardless of the nature of the variables (continuous/qualitative/mixed), (2) ease of implementation, as it is based on few easy-to-use software packages, and (3) robustness, through the stability evaluation of the final clusters and through recognized algorithms and implementations. This workflow can be easily automated and/or routinely applied on a wide range of clustering projects. A synthesis of the literature on data clustering as well as the scientific rationale supporting the proposed workflow is also provided. Finally, a detailed application of the workflow on a concrete use case is provided, along with a practical discussion for data scientists. An implementation on the Dataiku platform is available upon request to the authors.