Selective leaching of copper and zinc from primary ores and secondary mineral residues using biogenic ammonia

With the number of easily accessible ores depleting, alternate primary and secondary sources are required to meet the increasing demand of economically important metals. Whilst highly abundant, these materials are of lower grade with respect to traditional ores, thus highly selective and sustainable metal extraction technologies are needed to reduce processing costs. Here, we investigated the metal leaching potential of biogenic ammonia produced by a ureolytic strain of Lysinibacillus sphaericus on eight primary and secondary materials, comprised of mining and metallurgical residues, sludges and automotive shredder residues (ASR). For the majority of materials, moderate to high yields (30–70%) and very high selectivity (>97% against iron) of copper and zinc were obtained with 1 mol L−1 total ammonia. Optimal leaching was achieved and further refined for the ASR in a two-step indirect leaching system with biogenic ammonia. Copper leaching was the result of local corrosion and differences in leaching against the synthetic (NH4)2CO3 control could be accounted for by pH shifts from microbial metabolism, subsequently altering free NH3 required for coordination. These results provide important findings for future sustainable metal recovery technologies from secondary materials.This work was conducted under the financial support of the Strategic Initiative Materials in Flanders (SIM) (SBO-SMART: Sustainable Metal Extraction from Tailings, grant no. HBC.2016.0456) and the European Union’s Horizon 2020 research and innovation programme, Metal Re-covery from Low-Grade Ores and Wastes Plus (METGROW+, grant no. 690088) . FV acknowledges support by the Flemish Agency for Inno-vation and Entrepreneurship (Vlaio) via a Baekeland PhD fellowship (HBC.2017.0224) and by the Research & Development Umicore Group. We would like to thank Pieter Ostermeyer and Karel Folens for assis-tance with thermodynamic modelling and CMET and ECOCHEM group members and SMART/METGROW+partners for valuable discussions throughout the projec

Biomarker-based asthma phenotypes of corticosteroid response

BackgroundAsthma is a heterogeneous disease with different phenotypes. Inhaled corticosteroid (ICS) therapy is a mainstay of treatment for asthma, but the clinical response to ICSs is variable.ObjectiveWe hypothesized that a panel of inflammatory biomarkers (ie, fraction of exhaled nitric oxide [Feno], sputum eosinophil count, and urinary bromotyrosine [BrTyr] level) might predict steroid responsiveness.MethodsThe original study from which this analysis originates comprised 2 phases: a steroid-naive phase 1 and a 28-day trial of ICSs (phase 2) during which Feno values, sputum eosinophil counts, and urinary BrTyr levels were measured. The response to ICSs was based on clinical improvements, including a 12% or greater increase in FEV1, a 0.5-point or greater decrease in Asthma Control Questionnaire score, and 2 doubling dose or greater increase in provocative concentration of adenosine 5′-monophosphate causing a 20% decrease in FEV1 (PC20AMP). Healthy control subjects were also evaluated in this study for comparison of biomarkers with those seen in asthmatic patients.ResultsAsthmatic patients had higher than normal Feno values, sputum eosinophil counts, and urinary BrTyr levels during the steroid-naive phase and after ICS therapy. After 28-day trial of ICSs, Feno values decreased in 82% of asthmatic patients, sputum eosinophil counts decreased in 60%, and urinary BrTyr levels decreased in 58%. Each of the biomarkers at the steroid-naive phase had utility for predicting steroid responsiveness, but the combination of high Feno values and high urinary BrTyr levels had the best power (13.3-fold, P < .01) to predict a favorable response to ICS therapy. However, the magnitude of the decrease in biomarker levels was unrelated to the magnitude of clinical response to ICS therapy.ConclusionA noninvasive panel of biomarkers in steroid-naive asthmatic patients predicts clinical responsiveness to ICS therapy

Laparoscopy in management of appendicitis in high-, middle-, and low-income countries: a multicenter, prospective, cohort study.

BACKGROUND: Appendicitis is the most common abdominal surgical emergency worldwide. Differences between high- and low-income settings in the availability of laparoscopic appendectomy, alternative management choices, and outcomes are poorly described. The aim was to identify variation in surgical management and outcomes of appendicitis within low-, middle-, and high-Human Development Index (HDI) countries worldwide. METHODS: This is a multicenter, international prospective cohort study. Consecutive sampling of patients undergoing emergency appendectomy over 6 months was conducted. Follow-up lasted 30 days. RESULTS: 4546 patients from 52 countries underwent appendectomy (2499 high-, 1540 middle-, and 507 low-HDI groups). Surgical site infection (SSI) rates were higher in low-HDI (OR 2.57, 95% CI 1.33-4.99, p = 0.005) but not middle-HDI countries (OR 1.38, 95% CI 0.76-2.52, p = 0.291), compared with high-HDI countries after adjustment. A laparoscopic approach was common in high-HDI countries (1693/2499, 67.7%), but infrequent in low-HDI (41/507, 8.1%) and middle-HDI (132/1540, 8.6%) groups. After accounting for case-mix, laparoscopy was still associated with fewer overall complications (OR 0.55, 95% CI 0.42-0.71, p < 0.001) and SSIs (OR 0.22, 95% CI 0.14-0.33, p < 0.001). In propensity-score matched groups within low-/middle-HDI countries, laparoscopy was still associated with fewer overall complications (OR 0.23 95% CI 0.11-0.44) and SSI (OR 0.21 95% CI 0.09-0.45). CONCLUSION: A laparoscopic approach is associated with better outcomes and availability appears to differ by country HDI. Despite the profound clinical, operational, and financial barriers to its widespread introduction, laparoscopy could significantly improve outcomes for patients in low-resource environments. TRIAL REGISTRATION: NCT02179112

Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states

Accurate diagnosis of mild cognitive impairment (MCI) before conversion to Alzheimer’s disease (AD) is invaluable for patient treatment. Many works showed that MCI and AD affect functional and structural connections between brain regions as well as the shape of cortical regions. However, ‘shape connections’ between brain regions are rarely investigated -e.g., how morphological attributes such as cortical thickness and sulcal depth of a specific brain region change in relation to morphological attributes in other regions. To fill this gap, we unprecedentedly design morphological brain multiplexes for late MCI/AD classification. Specifically, we use structural T1-w MRI to define morphological brain networks, each quantifying similarity in morphology between different cortical regions for a specific cortical attribute. Then, we define a brain multiplex where each intra-layer represents the morphological connectivity network of a specific cortical attribute, and each inter-layer encodes the similarity between two consecutive intra-layers. A significant performance gain is achieved when using the multiplex architecture in comparison to other conventional network analysis architectures. We also leverage this architecture to discover morphological connectional biomarkers fingerprinting the difference between late MCI and AD stages, which included the right entorhinal cortex and right caudal middle frontal gyrus

Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1–3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature

Biodiversity Conservation and Management

First paragraph: Since the first edition of the State of the Forest (SOF), the state and conservation of biodiversity have been a continuing concern. Every subsequent edition has reviewed the threats to the fauna and flora of the subregion. In 2010, the subject was presented in a chapter entitled "Biodiversity in the forests of Central Africa: panorama of knowledge, principal challenges and conservation measures" (Billand, 2012). By devoting a new chapter to this subject, the SOF 2013 reaffirms the importance of biodiversity and the protection of species for the sustainable development of the forests of Central Africa

Context matters-using an agent-based model to investigate the influence of market context on the supply of local biomass for anaerobic digestion

Biogas plant managers often face difficulties in obtaining feedstock at stable and affordable prices. The context in which the biogas plant manager needs to purchase the feedstock could be important when the biomass is also used in valorization trajectories besides anaerobic digestion. Using a combination of qualitative research and agent-based modelling, we investigated the effect of market context on the purchase of local biomass for anaerobic digestion. This paper details the institutional arrangements of our case study, the silage maize market in Flanders and the results of a scenario analysis, simulating nine different market contexts. Silage maize is an interesting case study, as it is both used for feed by farmers and as an input in biogas plants. The results show that mainly the time of entry into the market explains the difficulties in obtaining a stable supply of silage maize to biogas plants. Furthermore, we found a silage maize price increase for farmers in competition with a biogas plant, especially in case of a silage maize deficit in the market. The different institutional arrangements used have no significant effect. Our findings may guide biogas plant managers in assessing and reducing the consequences of the establishment of a biogas plant, competing for local biomass resources

Conservation et gestion de la biodiversité

First paragraph: Since the first edition of the State of the Forest (SOF), the state and conservation of biodiversity have been a continuing concern. Every subsequent edition has reviewed the threats to the fauna and flora of the subregion. In 2010, the subject was presented in a chapter entitled "Biodiversity in the forests of Central Africa: panorama of knowledge, principal challenges and conservation measures" (Billand, 2012). By devoting a new chapter to this subject, the SOF 2013 reaffirms the importance of biodiversity and the protection of species for the sustainable development of the forests of Central Africa