Setting a best practice for determining the EGR rate in hydrogen internal combustion engines

Exhaust gas recirculation (EGR) is an effective way to reduce NOx-emissions and increase the efficiency of hydrogen fueled internal combustion engines. Knowledge of the exact amount of EGR is crucial to understand the effects of EGR. As the exhaust gas flow is pulsating and chemically aggressive, the flow rate is typically not measured directly and has to be derived from other quantities. For hydrocarbon fuels, the EGR rate is generally calculated from a molar CO2 balance, but for hydrogen engines this obviously cannot be used as there are no CO2 emissions, and consequently no standard practice has been established. This work considers three methods to calculate the amount of EGR in a hydrogen engine. The first one is based upon a volume balance in the mixing section of exhaust gases and fresh air. The second and third method uses a molar balance of O-2 and H2O respectively in this mixing section. The three methods are developed and tested for their accuracy with an error analysis. Additionally, the methods are applied to an experimental dataset gathered on a single cylinder hydrogen engine. Both the theoretical analysis and the experimental results confirm the method based on an O-2 molar balance as the most accurate one. The least practical method is the one based on an H2O balance as it requires additional relative humidity sensors and is less accurate than the others

The need for aquatic tracking networks: the permanent Belgian Acoustic Receiver Network

Aquatic biotelemetry techniques have proven to be valuable tools to generate knowledge on species behaviour, gather oceanographic data and help in assessing effects from anthropogenic disturbances. These data types support international policies and directives, needed for species and habitat conservation. As aquatic systems are highly interconnected and cross administrative borders, optimal data gathering should be organized on a large scale. This need triggered the development of regional, national and international aquatic animal tracking network initiatives around the globe. In Belgium, a national acoustic receiver network for fish tracking, called the Permanent Belgian Acoustic Receiver Network, was set up in 2014 with different research institutes collaborating. It is a permanent network with 160 acoustic receivers and since the start, over 800 animals from 16 different fish species have been tagged and generated more than 17 million detections so far. To handle all the (meta)data generated, a data management platform was built. The central database stores all the data and has an interactive web interface that allows the users to upload, manage and explore (meta)data. In addition, the database is linked to an R-shiny application to allow the user to visualize and download the detection data. The permanent tracking network is not only a collaborative platform for exchange of data, analysis tools, devices and knowledge. It also creates opportunities to perform feasibility studies and Ph.D. studies in a cost-efficient way. The Belgian tracking network is a first step towards a Pan-European aquatic tracking network

Design of protease-resistant myelin basic protein-derived peptides by cleavage site directed amino acid substitutions

Multiple Sclerosis (MS) is considered to be a T cell-mediated autoimmune disease. An attractive strategy to prevent activation of autoaggressive T cells in MS, is the use of altered peptide ligands (APL), which bind to major histocompatibility complex class II (MHC II) molecules. To be of clinical use, APL must be capable of resisting hostile environments including the proteolytic machinery of antigen presenting cells (APC). The current design of APL relies on cost- and labour-intensive strategies. To overcome these major drawbacks, we used a deductive approach which involved modifying proteolytic cleavage sites in APL. Cleavage site-directed amino acid substitution of the autoantigen myelin basic protein (MBP) resulted in lysosomal protease-resistant, high-affinity binding peptides. In addition, these peptides mitigated T cell activation in a similar fashion as conventional APL. The strategy outlined allows the development of protease-resistant APL and provides a universal design strategy to improve peptide-based immunotherapeutics

Network diffusion modeling predicts neurodegeneration in traumatic brain injury

Objective Traumatic brain injury (TBI) is a heterogeneous disease with multiple neurological deficits that evolve over time. It is also associated with an increased incidence of neurodegenerative diseases. Accordingly, clinicians need better tools to predict a patient’s long‐term prognosis. Methods Diffusion‐weighted and anatomical MRI data were collected from 17 adolescents (mean age = 15y8mo) with moderate‐to‐severe TBI and 19 healthy controls. Using a network diffusion model (NDM), we examined the effect of progressive deafferentation and gray matter thinning in young TBI patients. Moreover, using a novel automated inference method, we identified several injury epicenters in order to determine the neural degenerative patterns in each TBI patient. Results We were able to identify the subject‐specific patterns of degeneration in each patient. In particular, the hippocampus, temporal cortices, and striatum were frequently found to be the epicenters of degeneration across the TBI patients. Orthogonal transformation of the predicted degeneration, using principal component analysis, identified distinct spatial components in the temporal–hippocampal network and the cortico‐striatal network, confirming the vulnerability of these networks to injury. The NDM model, best predictive of the degeneration, was significantly correlated with time since injury, indicating that NDM can potentially capture the pathological progression in the chronic phase of TBI. Interpretation These findings suggest that network spread may help explain patterns of distant gray matter thinning, which would be consistent with Wallerian degeneration of the white matter connections (i.e., “diaschisis”) from diffuse axonal injuries and multifocal contusive injuries, and the neurodegenerative patterns of abnormal protein aggregation and transmission, which are hallmarks of brain changes in TBI. NDM approaches could provide highly subject‐specific biomarkers relevant for disease monitoring and personalized therapies in TBI

The vaginal microflora in relation to gingivitis

<p>Abstract</p> <p>Background</p> <p>Gingivitis has been linked to adverse pregnancy outcome (APO). Bacterial vaginosis (BV) has been associated with APO. We assessed if bacterial counts in BV is associated with gingivitis suggesting a systemic infectious susceptibilty.</p> <p>Methods</p> <p>Vaginal samples were collected from 180 women (mean age 29.4 years, SD ± 6.8, range: 18 to 46), and at least six months after delivery, and assessed by semi-quantitative DNA-DNA checkerboard hybridization assay (74 bacterial species). BV was defined by Gram stain (Nugent criteria). Gingivitis was defined as bleeding on probing at ≥ 20% of tooth sites.</p> <p>Results</p> <p>A Nugent score of 0–3 (normal vaginal microflora) was found in 83 women (46.1%), and a score of > 7 (BV) in 49 women (27.2%). Gingivitis was diagnosed in 114 women (63.3%). Women with a diagnosis of BV were more likely to have gingivitis (p = 0.01). Independent of gingival conditions, vaginal bacterial counts were higher (p < 0.001) for 38/74 species in BV+ in comparison to BV- women. Counts of four lactobacilli species were higher in BV- women (p < 0.001). Independent of BV diagnosis, women with gingivitis had higher counts of <it>Prevotella bivia </it>(p < 0.001), and <it>Prevotella disiens </it>(p < 0.001). <it>P. bivia, P. disiens, M. curtisii </it>and <it>M. mulieris </it>(all at the p < 0.01 level) were found at higher levels in the BV+/G+ group than in the BV+/G- group. The sum of bacterial load (74 species) was higher in the BV+/G+ group than in the BV+/G- group (p < 0.05). The highest odds ratio for the presence of bacteria in vaginal samples (> 1.0 × 10⁴cells) and a diagnosis of gingivitis was 3.9 for <it>P. bivia </it>(95% CI 1.5–5.7, p < 0.001) and 3.6 for <it>P. disiens </it>(95%CI: 1.8–7.5, p < 0.001), and a diagnosis of BV for <it>P. bivia </it>(odds ratio: 5.3, 95%CI: 2.6 to 10.4, p < 0.001) and <it>P. disiens </it>(odds ratio: 4.4, 95% CI: 2.2 to 8.8, p < 0.001).</p> <p>Conclusion</p> <p>Higher vaginal bacterial counts can be found in women with BV and gingivitis in comparison to women with BV but not gingivitis. <it>P. bivia </it>and <it>P. disiens </it>may be of specific significance in a relationship between vaginal and gingival infections.</p

PREDICT: a method for inferring novel drug indications with application to personalized medicine

The authors present a new method, PREDICT, for the large-scale prediction of drug indications, and demonstrate its use on both approved drugs and novel molecules. They also provide a proof-of-concept for its potential utility in predicting patient-specific medications