Determinants of soil organic matter chemistry in maritime temperate forest ecosystems

While the influence of climate, vegetation, management and abiotic site factors on total carbon budgets and turn-over is intensively assessed, the influences of these ecosystem properties on the chemical complexity of soil organic matter (SOM) remains poorly understood. This study addresses the chemical composition of NaOH-extracted SOM from maritime temperate forest sites in Flanders (Belgium) by pyrolysis-GC/MS. The studied forests were chosen based on dominant tree species (Pinus sylvestris, Fagus sylvatica, Quercus robur and Populus spp.), soil texture and soil-moisture conditions. Differences in extractable-SOM pyrolysis products were correlated to site variables including dominant tree species, management of the woody biomass, site history, soil properties, total carbon stocks and indicators for microbial activity. Despite of a typical high intercorrelation between these site variables, the influence of the dominant tree species is prominent. The extractable-SOM composition is strongly correlated to litter quality and available nutrients. In nutrient-poor forests with low litter quality, the decomposition of relatively recalcitrant compounds (i.e. short and mid-chain alkanes/alkenes and aromatic compounds) appears hampered, causing a relative accumulation of these compounds in the soil. However, if substrate quality is favorable, no accumulations of recalcitrant compounds were observed, not even under high soil-moisture conditions. Former heathland vegetation still had a profound influence on extractable-SOM chemistry of young pine forests after a minimum of 60 year

Practical guidance for applying the ADNEX model from the IOTA group to discriminate between different subtypes of adnexal tumors.

All gynecologists are faced with ovarian tumors on a regular basis, and the accurate preoperative diagnosis of these masses is important because appropriate management depends on the type of tumor. Recently, the International Ovarian Tumor Analysis (IOTA) consortium published the Assessment of Different NEoplasias in the adneXa (ADNEX) model, the first risk model that differentiates between benign and four types of malignant ovarian tumors: borderline, stage I cancer, stage II-IV cancer, and secondary metastatic cancer. This approach is novel compared to existing tools that only differentiate between benign and malignant tumors, and therefore questions may arise on how ADNEX can be used in clinical practice. In the present paper, we first provide an in-depth discussion about the predictors used in ADNEX and the ability for risk prediction with different tumor histologies. Furthermore, we formulate suggestions about the selection and interpretation of risk cut-offs for patient stratification and choice of appropriate clinical management. This is illustrated with a few example patients. We cannot propose a generally applicable algorithm with fixed cut-offs, because (as with any risk model) this depends on the specific clinical setting in which the model will be used. Nevertheless, this paper provides a guidance on how the ADNEX model may be adopted into clinical practice

High voltage implanted RESURF p-LDMOS using BICMOS technology

The hgh voltage DMOST based on BICMOS technology[l] are becoming more attractive because of its easy integration with bipolar and CMOS devices. Its process is required to be as compatible as possible with the BICMOS technology. This paper presents a complementary RESURF[2] p-LDMOS in whch the ni buried layer is used for the first time, as an effective substrate and the field implant is introduced to modify the drift charges. The implant conditions in t h ~ csa se, particularly the placements, will be studied

Three myths about risk thresholds for prediction models

Acknowledgments This work was developed as part of the international initiative of strengthening analytical thinking for observational studies (STRATOS). The objective of STRATOS is to provide accessible and accurate guidance in the design and analysis of observational studies (http://stratos-initiative.org/). Members of the STRATOS Topic Group ‘Evaluating diagnostic tests and prediction models’ are Gary Collins, Carl Moons, Ewout Steyerberg, Patrick Bossuyt, Petra Macaskill, David McLernon, Ben van Calster, and Andrew Vickers. Funding The study is supported by the Research Foundation-Flanders (FWO) project G0B4716N and Internal Funds KU Leuven (project C24/15/037). Laure Wynants is a post-doctoral fellow of the Research Foundation – Flanders (FWO). The funding bodies had no role in the design of the study, collection, analysis, interpretation of data, nor in writing the manuscript. Contributions LW and BVC conceived the original idea of the manuscript, to which ES, MVS and DML then contributed. DT acquired the data. LW analyzed the data, interpreted the results and wrote the first draft. All authors revised the work, approved the submitted version, and are accountable for the integrity and accuracy of the work.Peer reviewedPublisher PD

A blended preconception lifestyle programme for couples undergoing IVF:lessons learned from a multicentre randomized controlled trial

Study question: What is the effect of a blended preconception lifestyle programme on reproductive and lifestyle outcomes of couples going through their first 12 months of IVF as compared to an attention control condition?Summery answer:This randomized controlled trial (RCT) was stopped prematurely because of the coronavirus disease 2019 (Covid-19) pandemic but the available data did not suggest that a blended preconception lifestyle programme could meaningfully affect time to ongoing pregnancy or other reproductive and lifestyle outcomes.What is know already:Increasing evidence shows associations between a healthy lifestyle and IVF success rates. Lifestyle programmes provided through a mobile phone application have yet to be evaluated by RCTs in couples undergoing IVF.Study design, size, duration:A multicentre RCT (1:1) was carried out. The RCT started in January 2019 and was prematurely stopped because of the Covid-19 pandemic, leading to a reduced sample size (211 couples initiating IVF) and change in primary outcome (cumulative ongoing pregnancy to time to ongoing pregnancy).Participants/materials, setting, methods:Heterosexual couples initiating IVF in five fertility clinics were randomized between an attention control arm and an intervention arm for 12 months. The attention control arm received treatment information by mobile phone in addition to standard care. The intervention arm received the blended preconception lifestyle (PreLiFe)-programme in addition to standard care. The PreLiFe-programme included a mobile application, offering tailored advice and skills training on diet, physical activity and mindfulness, in combination with motivational interviewing over the telephone. The primary outcome was 'time to ongoing pregnancy'. Secondary reproductive outcomes included the Core Outcome Measures for Infertility Trials and IVF discontinuation. Changes in the following secondary lifestyle outcomes over 3 and 6 months were studied in both partners: diet quality, fruit intake, vegetable intake, total moderate to vigorous physical activity, sedentary behaviour, emotional distress, quality of life, BMI, and waist circumference. Finally, in the intervention arm, acceptability of the programme was evaluated and actual use of the mobile application part of the programme was tracked. Analysis was according to intention to treat.Main results and the role of chance:A total of 211 couples were randomized (105 control arm, 106 intervention arm). The hazard ratio of the intervention for time to ongoing pregnancy was 0.94 (95% CI 0.63 to 1.4). Little to no effect on other reproductive or lifestyle outcomes was identified. Although acceptability of the programme was good (6/10), considerable proportions of men (38%) and 9% of women did not actively use all the modules of the mobile application (diet, physical activity, or mindfulness).Limitations, reasons for caution:The findings of this RCT should be considered exploratory, as the Covid-19 pandemic limited its power and the actual use of the mobile application was low.Wider implications of the findings:This is the first multicentre RCT evaluating the effect of a blended preconception lifestyle programme for women and their partners undergoing IVF on both reproductive and lifestyle outcomes. This exploratory RCT highlights the need for further studies into optimal intervention characteristics and actual use of preconception lifestyle programmes, as well as RCTs evaluating effectiveness.Study fonding/competing intrest(s):Supported by the Research foundation Flanders (Belgium) (FWO-TBM; reference: T005417N). No competing interests to declare.Trial registration number:ClinicalTrials.gov Identifier: NCT03790449TRIAL REGISTRATION DATE 31 December 2018DATE OF FIRST PATIENT'S ENROLMENT 2 January 201

Minimum sample size for developing a multivariable prediction model using multinomial logistic regression.

AIMS: Multinomial logistic regression models allow one to predict the risk of a categorical outcome with > 2 categories. When developing such a model, researchers should ensure the number of participants (n) is appropriate relative to the number of events (Ek) and the number of predictor parameters (pk) for each category k. We propose three criteria to determine the minimum n required in light of existing criteria developed for binary outcomes. PROPOSED CRITERIA: The first criterion aims to minimise the model overfitting. The second aims to minimise the difference between the observed and adjusted R2 Nagelkerke. The third criterion aims to ensure the overall risk is estimated precisely. For criterion (i), we show the sample size must be based on the anticipated Cox-snell R2 of distinct 'one-to-one' logistic regression models corresponding to the sub-models of the multinomial logistic regression, rather than on the overall Cox-snell R2 of the multinomial logistic regression. EVALUATION OF CRITERIA: We tested the performance of the proposed criteria (i) through a simulation study and found that it resulted in the desired level of overfitting. Criterion (ii) and (iii) were natural extensions from previously proposed criteria for binary outcomes and did not require evaluation through simulation. SUMMARY: We illustrated how to implement the sample size criteria through a worked example considering the development of a multinomial risk prediction model for tumour type when presented with an ovarian mass. Code is provided for the simulation and worked example. We will embed our proposed criteria within the pmsampsize R library and Stata modules

Managing pregnancy of unknown location based on initial serum progesterone and serial serum hCG: development and validation of a two-step triage protocol.

A uniform rationalized management protocol for pregnancies of unknown location (PUL) is lacking. We developed a two-step triage protocol based on presenting serum progesterone (step 1) and hCG ratio two days later (step 2) to select PUL at high-risk of ectopic pregnancy (EP).Cohort study of 2753 PUL (301 EP), involving a secondary analysis of prospectively and consecutively collected PUL at two London-based university teaching hospitals. Using a chronological split we used 1449 PUL for development and 1304 for validation. We aimed to select PUL as low-risk with high confidence (high negative predictive value, NPV) while classifying most EP as high-risk (high sensitivity). The first triage step selects low-risk PUL at presentation using a serum progesterone threshold. The remaining PUL are triaged using a novel logistic regression risk model based on hCG ratio and initial serum progesterone (second step), defining low-risk as an estimated EP risk <5%.On validation, initial serum progesterone ≤2nmol/l (step 1) selected 16.1% PUL as low-risk. Second step classification with the risk model M6P selected an additional 46.0% of all PUL as low-risk. Overall, the two-step protocol classified 62.1% of PUL as low-risk, with an NPV of 98.6% and a sensitivity of 92.0%. When the risk model was used in isolation (i.e. without the first step), 60.5% of PUL were classified as low-risk with 99.1% NPV and 94.9% sensitivity.The two-step protocol can efficiently classify PUL into being at high or low risk of complications

Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study

Objectives To develop a risk prediction model to preoperatively discriminate between benign, borderline, stage I invasive, stage II-IV invasive, and secondary metastatic ovarian tumours. Design Observational diagnostic study using prospectively collected clinical and ultrasound data. Setting 24 ultrasound centres in 10 countries. Participants Women with an ovarian (including para-ovarian and tubal) mass and who underwent a standardised ultrasound examination before surgery. The model was developed on 3506 patients recruited between 1999 and 2007, temporally validated on 2403 patients recruited between 2009 and 2012, and then updated on all 5909 patients. Main outcome measures Histological classification and surgical staging of the mass. Results The Assessment of Different NEoplasias in the adneXa (ADNEX) model contains three clinical and six ultrasound predictors: age, serum CA-125 level, type of centre (oncology centres v other hospitals), maximum diameter of lesion, proportion of solid tissue, more than 10 cyst locules, number of papillary projections, acoustic shadows, and ascites. The area under the receiver operating characteristic curve (AUC) for the classic discrimination between benign and malignant tumours was 0.94 (0.93 to 0.95) on temporal validation. The AUC was 0.85 for benign versus borderline, 0.92 for benign versus stage I cancer, 0.99 for benign versus stage II-IV cancer, and 0.95 for benign versus secondary metastatic. AUCs between malignant subtypes varied between 0.71 and 0.95, with an AUC of 0.75 for borderline versus stage I cancer and 0.82 for stage II-IV versus secondary metastatic. Calibration curves showed that the estimated risks were accurate. Conclusions The ADNEX model discriminates well between benign and malignant tumours and offers fair to excellent discrimination between four types of ovarian malignancy. The use of ADNEX has the potential to improve triage and management decisions and so reduce morbidity and mortality associated with adnexal pathology