Electrochemical cell

The present invention pertains to an electrochemical cell (1) comprising: - an anolyte space with an anode (11); - a catholyte space with a cathode (12); and - a separator disc (2) separating the anolyte space from the catholyte space; wherein the anolyte space and/or the catholyte space comprise at least one wall (2) which is rotatable about an axis of rotation crossing the separator disc, wherein the electrochemical cell comprises at least one stator (6, 7) in the anolyte space and/or the catholyte space

Development of soil and terrain digital database for major food-growing regions of India for resource planning

Soil information system in SOTER (soil and terrain digital database) framework is developed for the Indo-Gangetic Plains (IGP) and black soil regions (BSR) of India with the help of information from 842 georeferenced soil profiles including morphological, physical and chemical properties of soils in addition to the site characteristics and climatic information. The database has information from 82 climatic stations that can be linked with the other datasets. The information from this organized database can be easily retrieved for use and is compatible with the global database. The database can be updated with recent and relevant data as and when they are available. The database has many applications such as inputs for refinement of agro-ecological regions and sub-regions, studies on carbon sequestration, land evaluation and land (crop) planning, soil erosion, soil quality, carbon and crop modelling and other climate change related research. This warehouse of information in a structured framework can be used as a data bank for posterity

Fertilizer and Soil Health in Africa The Role of Fertilizer in Building Soil Health to Sustain Farming and Address Climate Change

Summary Soil health is commonly defined as the ability to generate sufficient crop yields while maintaining the future productive capacity of soils and the ecosystem services soils regulate and deliver. However, less consensus exists on indicators to assess soil health and its changes over time and space, although soil organic carbon (SOC) is generally acknowledged as a key indicator. In the context of this paper, soil health status is equated with SOC status. Current SOC conditions are influenced by soil properties and climate. Under smallholder farming conditions, SOC is variable and affected by past crop and soil management practices, which are influenced by farmer typology. Although SOC content under cropland is a maximum of 60-70% of that under natural vegetation, there is substantial scope to increase it in smallholder farming conditions. A conceptual framework relating to fertilizer, crop productivity, and soil health is presented here. While fertilizer application commonly results in a substantial increase in crop yield at various scales, a key indicator of fertilizer use, agronomic efficiency (AE), is often observed to be lower than relatively easily achievable values under well-managed conditions, caused by a diversity of factors. Low AE values do not necessarily result in greater greenhouse gas (GHG) emissions because of the low fertilizer application rates in sub-Saharan Africa (SSA), though increases in GHG emissions are likely with increases in fertilizer use. Crop response to organic inputs is substantially lower although organic inputs increase SOC content, which usually results in greater AE values relative to sole application of fertilizer. Increases in crop productivity are associated with increases in SOC, though the relationship is weak and efforts besides fertilizer application itself are required. That said, N(PK) fertilizer has had a positive effect on SOC in most parts of the world except SSA, an observation corroborated by an analysis of past and ongoing long-term experiments, likely related to the low and erratic use of fertilizer in the region. While fertilizer use can be an entry point to increasing soil health, this will not likely happen on degraded soils where responses to fertilizer are limited. In such cases, investments to rehabilitate degraded soils should come first. Several approaches can be followed to determine best fertilizer recommendations, while recognizing nutrients needs by crops and soil-specific properties. Site-specificity commonly requires an assessment of the soil fertility status of a particular field, and analytical tools now allow for the development of locally relevant recommendations at scale with some early successes. While organic inputs do positively impact SOC, attractive options to increase organic inputs in smallholder farming systems are limited and mostly related to in-situ production, with an important emphasis on multi-purpose legumes. Climate adaptation is facilitated by healthy Fertilizer and Soil Health in Africa 2 soils and requires fertilizer to be combined with other crop, soil, and water management practices (Wortmann and Stewart, 2021). While low yields are linked to the ecological yield gap, whereby the potential productivity of crops is set by biological factors, input and output prices determine the economic yield gap, which is usually quite lower than the former because of unfavourable ratio of fertilizer prices to crop product prices. Even though profitability is a key driver of impact, many other factors affect the adoption of appropriate fertilizer and soil health recommendations, including farmers’ production objectives, resource endowment, land tenure, and access to markets. A main bottleneck in engaging smallholder farmers in soil health-restoring practices is the relatively large amount of time such practices take to deliver benefits that are visible to farmers. In the absence of incentive programs, farmers require short-term benefits, generated within their farming systems. Furthermore, associated advice on complementary practices to fertilizer use increases the complexity of information to be conveyed to farmers. Scaling models have moved toward the delivery of bundled services, often digitally enabled, to address challenges with communicating complex information and the necessary complementary crop and soil management practices. Targeted policy interventions can support the delivery of broad digitally enabled fertilizer management recommendations and the creation of conditions that enable smallholder farmers to implement these recommendations at scale. A number of recommendations have been generated from the scientific information, covered under the following headings: (1) key elements of a Fertilizer and Soil Health Action Plan; (2) development of quantitative indicators and targets of soil health; (3) addressing climate change requires choices; (4) incentivizing farmers; (5) soil health investments, which require localized actions (think global, act local); and (6) not only fertilizers, but also auxiliary interventions, as defined by the Integrated Soil Fertility Management (ISFM) approach. Action is needed today to reverse the downward spiral of low and inefficient fertilizer use, resulting in low yields and declining soil health

Differential impact of impaired fasting glucose versus impaired glucose tolerance on cardiometabolic risk factors in multi-ethnic overweight/obese children

We aimed to investigate the prevalence of impaired fasting glucose (IFG) and impaired glucose tolerance (IGT), and their associations with cardiometabolic risk factors, according to ethnicity in a large obese paediatric cohort. A 75-g oral glucose tolerance test was performed in 1,007 overweight/obese Dutch children of multi-ethnic origin, referred to the obesity outpatient clinics of two Dutch hospitals in Amsterdam (mean age, 11.4 ± 3.2 years; 50.7% boys). Anthropometric parameters and blood samples were collected, and cardiometabolic risk factors were assessed. The cohort consisted of Dutch native (26.0%), Turkish (23.7%), Moroccan (18.8%) and children of ‘other’ (31.5%) ethnicity. The prevalence of IFG was significantly higher in Moroccan and Turkish children as compared to Dutch native children (25.4% and 19.7% vs. 11.8%, respectively, P < 0.05). IGT was most frequently present in Turkish and Dutch native children, relative to Moroccan children (6.3% and 5.3% vs. 1.6%, P < 0.05). Besides pubertal status and ethnicity, components of ‘metabolic syndrome’ (MetS) which were associated with IGT, independent of hyperinsulinaemia, were hypertension [odds ratio (OR), 2.3; 95% CI, 1.1–4.9] while a trend was seen for high triglycerides (OR, 2.0; 95% CI, 0.9–4.3). When analyzing components of MetS which were associated with IFG, only low high-density lipoprotein cholesterol was significantly associated (OR, 1.7; 95% CI, 1.2–2.5) independent of hyperinsulinaemia. In conclusion, in a Dutch multi-ethnic cohort of overweight/obese children, a high prevalence of IFG was found against a low prevalence of IGT, which differed in their associations with cardiometabolic risk factors

Prevalence of diabetes mellitus and the performance of a risk score among Hindustani Surinamese, African Surinamese and ethnic Dutch: a cross-sectional population-based study

<p>Abstract</p> <p>Background</p> <p>While the prevalence of type 2 diabetes mellitus (DM) is high, tailored risk scores for screening among South Asian and African origin populations are lacking. The aim of this study was, first, to compare the prevalence of (known and newly detected) DM among Hindustani Surinamese, African Surinamese and ethnic Dutch (Dutch). Second, to develop a new risk score for DM. Third, to evaluate the performance of the risk score and to compare it to criteria derived from current guidelines.</p> <p>Methods</p> <p>We conducted a cross-sectional population based study among 336 Hindustani Surinamese, 593 African Surinamese and 486 Dutch, aged 35–60 years, in Amsterdam. Logistic regressing analyses were used to derive a risk score based on non-invasively determined characteristics. The diagnostic accuracy was assessed by the area under the Receiver-Operator Characteristic curve (AUC).</p> <p>Results</p> <p>Hindustani Surinamese had the highest prevalence of DM, followed by African Surinamese and Dutch: 16.7, 8.1, 4.2% (age 35–44) and 35.0, 19.0, 8.2% (age 45–60), respectively. The risk score included ethnicity, body mass index, waist circumference, resting heart rate, first-degree relative with DM, hypertension and history of cardiovascular disease. Selection based on age alone showed the lowest AUC: between 0.57–0.62. The AUC of our score (0.74–0.80) was higher than that of criteria from guidelines based solely on age and BMI and as high as criteria that required invasive specimen collection.</p> <p>Conclusion</p> <p>In Hindustani Surinamese and African Surinamese populations, screening for DM should not be limited to those over 45 years, as is advocated in several guidelines. If selective screening is indicated, our ethnicity based risk score performs well as a screening test for DM among these groups, particularly compared to the criteria based on age and/or body mass index derived from current guidelines.</p

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

<p>Abstract</p> <p>Background</p> <p>The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.</p> <p>Methods</p> <p>We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.</p> <p>Results</p> <p>Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).</p> <p>Conclusions</p> <p>We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.</p