The impact of biogas production on the organic carbon input to the soil of Dutch dairy farms:A substance flow analysis

The use of Dutch dairy manure for biogas production is expected to increase from 10% in 2020 to 60% in 2030. Traditionally, manure is returned to fields as a source of nutrients and organic carbon. Since a share of manure carbon is converted into biogas, this practice impacts the organic carbon input to soil (OCIS) of the dairy farms. The magnitude of the impact depends on the magnitude of the other sources of organic carbon. This impact is not considered by current advocates for large-scale use of dairy manure for biogas while understanding it is essential because of the risk of decreasing carbon soil input. Therefore, a study of carbon flows of dairy farms that eventually contribute to the OCIS is required. In this paper, we use substance flow analysis to quantify the carbon flows on different Dutch dairy farms and investigate the impact of using manure for biogas production to their OCIS (kgC/year/ha). The farms differ in farming practices such as whether cows are grazed outside or not. The results show that about 40% of OCIS of a Dutch dairy farm comes from manure and the rest comes from its crop production. The organic carbon from manure to the soil is also limited by the need to export manure due to the Dutch nutrient regulations. The overall reduction in OCIS caused by biogas production is 10%–20%. The impact is largest in farms with no grazing. These findings provide insights into the possible trade-offs of using manure for biogas production

Quantum Chemistry–Machine Learning Approach for Predicting Properties of Lewis Acid–Lewis Base Adducts

Synthetic design allowing predictive control of charge transfer and other optoelectronic properties of Lewis acid adducts remains elusive. This challenge must be addressed through complementary methods combining experimental with computational insights from first principles. Ab initio calculations for optoelectronic properties can be computationally expensive and less straightforward than those sufficient for simple ground-state properties, especially for adducts of large conjugated molecules and Lewis acids. In this contribution, we show that machine learning (ML) can accurately predict density functional theory (DFT)-calculated charge transfer and even properties associated with excited states of adducts from readily obtained molecular descriptors. Seven ML models, built from a dataset of over 1000 adducts, show exceptional performance in predicting charge transfer and other optoelectronic properties with a Pearson correlation coefficient of up to 0.99. More importantly, the influence of each molecular descriptor on predicted properties can be quantitatively evaluated from ML models. This contributes to the optimization of a priori design of Lewis adducts for future applications, especially in organic electronics

Lower bound element and submodel for modelling of joints between precast concrete panels

In practice, precast concrete structures are designed using either analytical methods or linear finite element tools, and the in-situ cast joints between the precast panels are assessed using conservative empirical design formulas. This often leads to a suboptimal design, and local mechanisms inside the joint are not taken into account. This paper presents an equilibrium element representing in-situ cast joints and an advanced submodel yield criterion is developed. The element and submodel are verified by compar- ison to a detailed numerical model as well as experimental results. The computational time and problem size of the joint element and detailed model will be discussed

Global, regional, and national burden of chronic kidney disease, 1990–2017 : a systematic analysis for the Global Burden of Disease Study 2017

Background Health system planning requires careful assessment of chronic kidney disease (CKD) epidemiology, but data for morbidity and mortality of this disease are scarce or non-existent in many countries. We estimated the global, regional, and national burden of CKD, as well as the burden of cardiovascular disease and gout attributable to impaired kidney function, for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017. We use the term CKD to refer to the morbidity and mortality that can be directly attributed to all stages of CKD, and we use the term impaired kidney function to refer to the additional risk of CKD from cardiovascular disease and gout. Methods The main data sources we used were published literature, vital registration systems, end-stage kidney disease registries, and household surveys. Estimates of CKD burden were produced using a Cause of Death Ensemble model and a Bayesian meta-regression analytical tool, and included incidence, prevalence, years lived with disability, mortality, years of life lost, and disability-adjusted life-years (DALYs). A comparative risk assessment approach was used to estimate the proportion of cardiovascular diseases and gout burden attributable to impaired kidney function. Findings Globally, in 2017, 1·2 million (95% uncertainty interval [UI] 1·2 to 1·3) people died from CKD. The global all-age mortality rate from CKD increased 41·5% (95% UI 35·2 to 46·5) between 1990 and 2017, although there was no significant change in the age-standardised mortality rate (2·8%, −1·5 to 6·3). In 2017, 697·5 million (95% UI 649·2 to 752·0) cases of all-stage CKD were recorded, for a global prevalence of 9·1% (8·5 to 9·8). The global all-age prevalence of CKD increased 29·3% (95% UI 26·4 to 32·6) since 1990, whereas the age-standardised prevalence remained stable (1·2%, −1·1 to 3·5). CKD resulted in 35·8 million (95% UI 33·7 to 38·0) DALYs in 2017, with diabetic nephropathy accounting for almost a third of DALYs. Most of the burden of CKD was concentrated in the three lowest quintiles of Socio-demographic Index (SDI). In several regions, particularly Oceania, sub-Saharan Africa, and Latin America, the burden of CKD was much higher than expected for the level of development, whereas the disease burden in western, eastern, and central sub-Saharan Africa, east Asia, south Asia, central and eastern Europe, Australasia, and western Europe was lower than expected. 1·4 million (95% UI 1·2 to 1·6) cardiovascular disease-related deaths and 25·3 million (22·2 to 28·9) cardiovascular disease DALYs were attributable to impaired kidney function. Interpretation Kidney disease has a major effect on global health, both as a direct cause of global morbidity and mortality and as an important risk factor for cardiovascular disease. CKD is largely preventable and treatable and deserves greater attention in global health policy decision making, particularly in locations with low and middle SDI

Tracking modifications to implementation strategies: a case study from SNaP - a hybrid type III randomized controlled trial to scale up integrated systems navigation and psychosocial counseling for PWID with HIV in Vietnam

Introduction Evaluation of implementation strategies is core to implementation trials, but implementation strategies often deviate from the original plan to adjust to the real-world conditions. The optimal approach to track modifications to implementation strategies is unclear, especially in low-resource settings. Using data from an implementation trial for people who inject drugs (PWID) with HIV in Vietnam, we describe the tracking of implementation strategy modifications and present findings of this process. Methods SNaP (Systems Navigation and Psychosocial Counseling) is a hybrid type-III effectiveness-implementation randomized controlled trial aiming to scale up the evidence-based intervention, integrated systems navigation and psychosocial counseling, for PWID with HIV in Vietnam. Forty-two HIV testing sites were randomized 1:1 to a standard or tailored arm. While the standard arm (SA) received a uniform package of strategies, implementation strategies for the tailored arm (TA) were tailored to address specific needs of each site. The central research team also met monthly with the TA to document how their tailored strategies were implemented over time. Five components were involved in the tracking process: describing the planned strategies; tracking strategy use; monitoring barriers and solutions; describing modifications; and identifying and describing any additional strategies. Results Our approach allowed us to closely track the modifications to implementation strategies in the tailored arms every month. TA sites originally identified 27 implementation strategies prior to implementation. During implementation, five strategies were dropped by four sites and two new strategies were added to twelve sites. Modifications of five strategies occurred at four sites to accommodate their changing needs and resources. Difficulties related to the COVID-19 pandemic, low number of participants recruited, high workload at the clinic, lack of resources for HIV testing and high staff turnover were among barriers of implementing the strategies. A few challenges to tracking modifications were noted, including the considerable amount of time and efforts needed as well as the lack of motivation from site staff to track and keep written documentations of modifications. Conclusions We demonstrated the feasibility of a systematic approach to tracking implementation strategies for a large-scale implementation trial in a low-resource setting. This process could be further enhanced and replicated in similar settings to balance the rigor and feasibility of implementation strategy tracking. Our findings can serve as additional guidelines for future researchers planning to report and track modifications to implementation strategies in large, complex trials. Trial registration: clinicaltrials.gov ID: NCT03952520 (first posted 2019-05-16)

Proteomics Profiling of Research Models for Studying Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival rate of 10-15% due to late-stage diagnosis and limited efficacy of existing treatments. This study utilized proteomics-based systems modelling to generate multimodal datasets from various research models, including PDAC cells, spheroids, organoids, and tissues derived from murine and human samples. Identical mass spectrometry-based proteomics was applied across the different models. The preparation and validation of the research models and the proteomics were described in detail. The assembly datasets we present here contribute to the data collection on PDAC, which will be useful for systems modelling, data mining, knowledge discovery in databases, and bioinformatics of individual models. Further data analysis may lead to the generation of research hypotheses, predictions of targets for diagnosis and treatment, and relationships between data variables

Enablers and barriers to implementing effective disaster risk management according to good governance principles: Lessons from Central Vietnam

Despite the increasing frequency and intensity of natural hazard-induced disasters, global disaster risk governance predominantly focuses on theoretical frameworks and broad policies, with a noticeable gap in the effective local implementation of strategies grounded in good governance principles. This research aims to address this gap by evaluating the alignment of local disaster risk management policies with key good governance principles including: accountability, collaboration, transparency, information sharing, decentralization and autonomy, responsiveness and flexibility. Using Thua Thien Hue province in Central Vietnam, a region highly vulnerable to natural hazards, as a case study, this research combines legal document analysis and expert interviews to assess both enablers and barriers in disaster risk management. The findings identify several enablers, including clear legal frameworks, public transparency in resource allocation, active multi-stakeholder collaboration, and localized governance approaches that empower community involvement. However, persistent barriers include accountability gaps due to the lack of enforceable sanctions and incentives for proactive disaster prevention. Collaborative efforts remain predominantly government-led, with limited engagement from the private sector. Challenges in information sharing arise from insufficient dissemination of risk maps and hazard assessments at the community level. Decentralization and autonomy efforts struggle with personnel shortages and inadequate training. Responsiveness and flexibility suffer from the failure to adequately integrate vulnerability scenarios into legal frameworks. These findings highlight the importance of addressing barriers while leveraging existing enablers to strengthen governance frameworks in hazard-prone regions, providing valuable lessons that can be adapted to other disaster-prone areas globally

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations