Degradation kinetics of biochar from pyrolysis and hydrothermal carbonization in temperate soils

Background and Aims

Estimates of biochar residence times in soils range over three orders of magnitude. We present the first direct comparison between the biodegradation of a char from hydrothermal carbonization (htcBC) and pyrolysis (pyrBC) with high temporal resolution.

Methods

Mineralization of the biochars and their shared Miscanthus feedstock in three soils was determined directly by the ¹³CO₂ efflux using a novel method incorporating wavelength scanned cavity ring-down spectroscopy. Biochar half-life (t_1/2) was estimated with three empirical models.

Results

(1) The htcBC was readily biodegradable, whereas pyrBC was more recalcitrant. (2) Cumulative degradation of both biochars increased with soil organic carbon and nitrogen content. (3) The corrected Akaike information criterion (AIC_C) showed an overall preference for the double exponential model (DEM) reflecting a labile and a recalcitrant C-pool, over the first-order degradation model (FODM) and a logarithmic model. (4) The DEM resulted in t_1/2 ranging from 19.7–44.5, 0.7–2.1 and 0.8–1.3 years for pyrBC, htcBC and feedstock, respectively.

Conclusion

The degradation was rather similar between feedstock and htcBC but one order of magnitude slower for pyrBC. The AIC_C preferred FODM in two cases, where the DEM parameters indicated no distinction between a labile and recalcitrant carbon pool.     

Structural insight into Parkinson's disease treatment from drug-inhibited DOPA decarboxylase.

DOPA decarboxylase (DDC) is responsible for the synthesis of the key neurotransmitters dopamine and serotonin via decarboxylation of L-3,4-dihydroxyphenylalanine (L-DOPA) and L-5-hydroxytryptophan, respectively. DDC has been implicated in a number of clinic disorders, including Parkinson's disease and hypertension. Peripheral inhibitors of DDC are currently used to treat these diseases. We present the crystal structures of ligand-free DDC and its complex with the anti-Parkinson drug carbiDOPA. The inhibitor is bound to the enzyme by forming a hydrazone linkage with the cofactor, and its catechol ring is deeply buried in the active site cleft. The structures provide the molecular basis for the development of new inhibitors of DDC with better pharmacological characteristics.     

CT-Guided Biopsy in Suspected Spondylodiscitis – The Association of Paravertebral Inflammation with Microbial Pathogen Detection

Objectives

To search for imaging characteristics distinguishing patients with successful from those with futile microbiological pathogen detection by CT-guided biopsy in suspected spondylodiscitis.

Methods

34 consecutive patients with suspected spondylodiscitis underwent CT-guided biopsy for pathogen detection. MR-images were assessed for inflammatory infiltration of disks, adjacent vertebrae, epidural and paravertebral space. CT-images were reviewed for arrosion of adjacent end plates and reduced disk height. Biopsy samples were sent for microbiological examination in 34/34 patients, and for additional histological analysis in 28/34 patients.

Results

Paravertebral infiltration was present in all 10/10 patients with positive microbiology and occurred in only 12/24 patients with negative microbiology, resulting in a sensitivity of 100% and a specificity of 50% for pathogen detection. Despite its limited sensitivities, epidural infiltration and paravertebral abscesses showed considerably higher specificities of 83.3% and 90.9%, respectively. Paravertebral infiltration was more extensive in patients with positive as compared to negative microbiology (p = 0.002). Even though sensitivities for pathogen detection were also high in case of vertebral and disk infiltration, or end plate arrosion, specificities remained below 10%.

Conclusions

Inflammatory infiltration of the paravertebral space indicated successful pathogen detection by CT-guided biopsy. Specificity was increased by the additional occurrence of epidural infiltration or paravertebral abscesses.     

The physiological role of GLP-1 in human: incretin, ileal brake or more?

The proglucagon-derived peptide glucagon-like peptide-1 (GLP-1) is an intestinal signal peptide postprandially released from the L cells of the lower gut. Exogenously administered the synthetic hormone exerts a glucose-dependent insulinotropic effect at the pancreatic beta-cells and lowers plasma glucagon by an inhibitory effect against the alpha-cells. It delays gastric emptying by relaxation of the gastric fundus, inhibition of antral contractility, and stimulation of both the tonic and phasic motility of the pyloric sphincter. Enhancement of insulin, suppression of glucagon, and inhibition of gastric emptying are the main determinants controlling glucose homeostasis with GLP-1. Human studies employing the specific GLP-1 receptor antagonist exendin(9-39) show that endogenously released GLP-1 likewise controls fasting plasma glucagon, stimulates insulin, and influences all the motoric mechanisms known to control gastric emptying. Therefore, GLP-1 is discussed as an incretin hormone and as an enterogastrone in man. Synthetic GLP-1 also suppresses gastric acid and pancreatic enzyme secretion. The inhibitory effects on upper gastrointestinal functions are at least partly mediated by vagal-cholinergic inhibition and may involve interactions with vagal afferent pathways and/or circumventricular regions within the CNS. GLP-1 is a candidate humoral mediator of the 'ileal brake' exerting inhibition of upper gastrointestinal function preventing malabsorption and postprandial metabolic disturbances. As human studies indicate a central action of GLP-1 in reduction of food intake, it is uncertain if this is a consequence of induction of satiety or of transduction of visceral aversive stress signals.     

Ceramide levels in blood plasma correlate with major depressive disorder severity and its neutralization abrogates depressive behavior in mice

Major depressive disorder (MDD) is a severe disease of unknown pathogenesis that will affect ∼10% of people during their lifetime. Therapy for MDD requires prolonged treatment and often fails, predicating a need for novel treatment strategies. Here, we report increased ceramide levels in the blood plasma of MDD patients and in murine stress-induced models of MDD. These blood plasma ceramide levels correlated with the severity of MDD in human patients and were independent of age, sex, or body mass index. In addition, intravenous injection of anti-ceramide antibodies or neutral ceramidase rapidly abrogated stress-induced MDD, and intravenous injection of blood plasma from mice with MDD induced depression-like behavior in untreated mice, which was abrogated by ex vivo preincubation of the plasma with anti-ceramide antibodies or ceramidase. Mechanistically, we demonstrate that ceramide accumulated in endothelial cells of the hippocampus of stressed mice, evidenced by the quantitative measurement of ceramide in purified hippocampus endothelial cells. We found ceramide inhibited the activity of phospholipase D (PLD) in endothelial cells in vitro and in the hippocampus in vivo and thereby decreased phosphatidic acid in the hippocampus. Finally, we show intravenous injection of PLD or phosphatidic acid abrogated MDD, indicating the significance of this pathway in MDD pathogenesis. Our data indicate that ceramide controls PLD activity and phosphatidic acid formation in hippocampal endothelial cells and thereby mediates MDD. We propose that neutralization of plasma ceramide could represent a rapid-acting targeted treatment for MDD.     

Using genetic algorithms to select most predictive protein features

Many important characteristics of proteins such as biochemical activity and subcellular localization present a challenge to machine-learning methods: it is often difficult to encode the appropriate input features at the residue level for the purpose of making a prediction for the entire protein. The problem is usually that the biophysics of the connection between a machine-learning method's input (sequence feature) and its output (observed phenomenon to be predicted) remains unknown; in other words, we may only know that a certain protein is an enzyme (output) without knowing which region may contain the active site residues (input). The goal then becomes to dissect a protein into a vast set of sequence-derived features and to correlate those features with the desired output. We introduce a framework that begins with a set of global sequence features and then vastly expands the feature space by generically encoding the coexistence of residue-based features. It is this combination of individual features, that is the step from the fractions of serine and buried (input space 20 + 2) to the fraction of buried serine (input space 20 * 2) that implicitly shifts the search space from global feature inputs to features that can capture very local evidence such as a the individual residues of a catalytic triad. The vast feature space created is explored by a genetic algorithm (GA) paired with neural networks and support vector machines. We find that the GA is critical for selecting combinations of features that are neither too general resulting in poor performance, nor too specific, leading to overtraining. The final framework manages to effectively sample a feature space that is far too large for exhaustive enumeration. We demonstrate the power of the concept by applying it to prediction of protein enzymatic activity.     

Expression and Purification of Biologically Active Rat Bone Morphogenetic Protein-4 produced as Inclusion Bodies in Recombinant <Emphasis Type="Italic">Escherichia coli</Emphasis>

Rat bone morphogenetic protein-4 (rBMP-4) cDNA was cloned from rat osteoblasts by RT-PCR and expressed in E. coli. Monomeric, dimeric and polymeric forms of recombinant rat BMP-4 (rrBMP-4) were obtained from inclusion bodies after solubilization with urea. The dimer was separated from the remaining polymer and host cell contaminants using size exclusion chromatography. Furthermore, purified rrBMP-4 was stabilized at low urea concentration (40 mm) and at pH 8.5 through the addition of bovine serum albumin. Both, rrBMP-4 dimer and polymer were biologically active as tested by the induction of alkaline phosphatase activity in MC3T3-E1 cells.     

Retinal ganglion cell loss is accompanied by antibody depositions and increased levels of microglia after immunization with retinal antigens

Background

Antibodies against retinal and optic nerve antigens are detectable in glaucoma patients. Recent studies using a model of experimental autoimmune glaucoma demonstrated that immunization with certain ocular antigens causes an immun-mediated retinal ganglion cell loss in rats.

Methodology/Principal Findings

Rats immunized with a retinal ganglion cell layer homogenate (RGA) had a reduced retinal ganglion cell density on retinal flatmounts (p = 0.007) and a lower number of Brn3⁺retinal ganglion cells (p = 0.0001) after six weeks. The autoreactive antibody development against retina and optic nerve was examined throughout the study. The levels of autoreactive antibodies continuously increased up to 6 weeks (retina: p = 0.004; optic nerve: p = 0.000003). Additionally, antibody deposits were detected in the retina (p = 0.02). After 6 weeks a reactive gliosis (GFAP density: RGA: 174.7±41.9; CO: 137.6±36.8, p = 0.0006; %GFAP⁺ area: RGA: 8.5±3.4; CO: 5.9±3.6, p = 0.006) as well as elevated level of Iba1⁺ microglia cells (p = 0.003) was observed in retinas of RGA animals.

Conclusions/Significance

Our findings suggest that these antibodies play a substantial role in mechanisms leading to retinal ganglion cell death. This seems to lead to glia cell activation as well as the invasion of microglia, which might be associated with debris clearance.     

Dynamics of glucose and insulin concentration connected to the <Emphasis Type="Italic">β</Emphasis>‐cell cycle: model development and analysis

Background

Diabetes mellitus is a group of metabolic diseases with increased blood glucose concentration as the main symptom. This can be caused by a relative or a total lack of insulin which is produced by the β‐cells in the pancreatic islets of Langerhans. Recent experimental results indicate the relevance of the β‐cell cycle for the development of diabetes mellitus.

Methods

This paper introduces a mathematical model that connects the dynamics of glucose and insulin concentration with the β‐cell cycle. The interplay of glucose, insulin, and β‐cell cycle is described with a system of ordinary differential equations. The model and its development will be presented as well as its mathematical analysis. The latter investigates the steady states of the model and their stability.

Results

Our model shows the connection of glucose and insulin concentrations to the β‐cell cycle. In this way the important role of glucose as regulator of the cell cycle and the capability of the β‐cell mass to adapt to metabolic demands can be presented. Simulations of the model correspond to the qualitative behavior of the glucose‐insulin regulatory system showed in biological experiments.

Conclusions

This work focusses on modeling the physiological situation of the glucose‐insulin regulatory system with a detailed consideration of the β‐cell cycle. Furthermore, the presented model allows the simulation of pathological scenarios. Modification of different parameters results in simulation of either type 1 or type 2 diabetes.