Comparison of Different Strategies for Selection/Adaptation of Mixed Microbial Cultures Able to Ferment Crude Glycerol Derived from Second-Generation Biodiesel

Objective of this study was the selection and adaptation of mixed microbial cultures (MMCs), able to ferment crude glycerol generated from animal fat-based biodiesel and produce building-blocks and green chemicals. Various adaptation strategies have been investigated for the enrichment of suitable and stable MMC, trying to overcome inhibition problems and enhance substrate degradation efficiency, as well as generation of soluble fermentation products. Repeated transfers in small batches and fed-batch conditions have been applied, comparing the use of different inoculum, growth media, and Kinetic Control. The adaptation of activated sludge inoculum was performed successfully and continued unhindered for several months. The best results showed a substrate degradation efficiency of almost 100% (about 10 g/L glycerol in 21 h) and different dominant metabolic products were obtained, depending on the selection strategy (mainly 1,3-propanediol, ethanol, or butyrate). On the other hand, anaerobic sludge exhibited inactivation after a few transfers. To circumvent this problem, fed-batch mode was used as an alternative adaptation strategy, which led to effective substrate degradation and high 1,3-propanediol and butyrate production. Changes in microbial composition were monitored by means of Next Generation Sequencing, revealing a dominance of glycerol consuming species, such as Clostridium, Klebsiella, and Escherichia

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Background Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson’s Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. Results The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson’s disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Conclusions Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context

Investigating locally relevant risk factors for Campylobacter infection in Australia: Protocol for a case-control study and genomic analysis

Introduction The CampySource project aims to identify risk factors for human Campylobacter infection in Australia. We will investigate locally relevant risk factors and those significant in international studies in a case-control study. Case isolates and contemporaneous isolates from food and animal sources will be sequenced to conduct source attribution modelling, and findings will be combined with the case-control study in a source-assigned analysis. Methods and analysis The case-control study will include 1200 participants (600 cases and 600 controls) across three regions in Australia. Cases will be recruited from campylobacteriosis notifications to health departments. Only those with a pure and viable Campylobacter isolate will be eligible for selection to allow for whole genome sequencing of isolates. Controls will be recruited from notified cases of influenza, frequency matched by sex, age group and geographical area of residence. All participants will be interviewed by trained telephone interviewers using a piloted questionnaire. We will collect Campylobacter isolates from retail meats and companion animals (specifically dogs), and all food, animal and human isolates will undergo whole genome sequencing. We will use sequence data to estimate the proportion of human infections that can be attributed to animal and food reservoirs (source attribution modelling), and to identify spatial clusters and temporal trends. Source-assigned analysis of the case-control study data will also be conducted where cases are grouped according to attributed sources. Ethics and dissemination Human and animal ethics have been approved. Genomic data will be published in online archives accompanied by basic metadata. We anticipate several publications to come from this study

No difference in striatal dopamine transporter availability between active smokers, ex-smokers and non-smokers using [123I]FP-CIT (DaTSCAN) and SPECT

Background: Mesolimbic and nigrostriatal dopaminergic pathways play important roles in both the rewarding and conditioning effects of drugs. The dopamine transporter (DAT) is of central importance in regulating dopaminergic neurotransmission and in particular in activating the striatal D2-like receptors. Molecular imaging studies of the relationship between DAT availability/dopamine synthesis capacity and active cigarette smoking have shown conflicting results. Through the collaboration between 13 SPECT centres located in 10 different European countries, a database of FP-CIT-binding in healthy controls was established. We used the database to test the hypothesis that striatal DAT availability is changed in active smokers compared to non-smokers and ex-smokers. Methods: A total of 129 healthy volunteers were included. Subjects were divided into three categories according to past and present tobacco smoking: (1) non-smokers (n = 64), (2) ex-smokers (n = 39) and (3) active smokers (n = 26). For imaging of the DAT availability, we used [123I]FP-CIT (DaTSCAN) and single photon emission computed tomography (SPECT). Data were collected in collaboration between 13 SPECT centres located in 10 different European countries. The striatal measure of DAT availability was analyzed in a multiple regression model with age, SPECT centre and smoking as predictor. Results: There was no statistically significant difference in DAT availability between the groups of active smokers, ex-smokers and non-smokers (p = 0.34). Further, we could not demonstrate a significant association between striatal DAT and the number of cigarettes per day or total lifetime cigarette packages in smokers and ex-smokers. Conclusion: Our results do not support the hypothesis that large differences in striatal DAT availability are present in smokers compared to ex-smokers and healthy volunteers with no history of smoking

Reduction in camera-specific variability in [123I]FP-CIT SPECT outcome measures by image reconstruction optimized for multisite settings: impact on age-dependence of the specific binding ratio in the ENC-DAT database of healthy controls

Purpose Quantitative estimates of dopamine transporter availability, determined with [123I]FP-CIT SPECT, depend on the SPECT equipment, including both hardware and (reconstruction) software, which limits their use in multicentre research and clinical routine. This study tested a dedicated reconstruction algorithm for its ability to reduce camera-specific intersubject variability in [123I]FP-CIT SPECT. The secondary aim was to evaluate binding in whole brain (excluding striatum) as a reference for quantitative analysis. Methods Of 73 healthy subjects from the European Normal Control Database of [123I]FP-CIT recruited at six centres, 70 aged between 20 and 82 years were included. SPECT images were reconstructed using the QSPECT software package which provides fully automated detection of the outer contour of the head, camera-specific correction for scatter and septal penetration by transmission-dependent convolution subtraction, iterative OSEMreconstruction including attenuation correction, and camera-specific Bto kBq/ml^ calibration. LINK and HERMES reconstruction were used for head-to-head comparison. The specific striatal [123I]FP-CIT binding ratio (SBR) was computed using the Southampton method with binding in the whole brain, occipital cortex or cerebellum as the reference. The correlation between SBR and age was used as the primary quality measure. Results The fraction of SBR variability explained by age was highest (1) with QSPECT, independently of the reference region, and (2) with whole brain as the reference, independently of the reconstruction algorithm. Conclusion QSPECT reconstruction appears to be useful for reduction of camera-specific intersubject variability of [123I]FP-CIT SPECT in multisite and single-site multicamera settings. Whole brain excluding striatal binding as the reference provides more stable quantitative estimates than occipital or cerebellar binding

Early-stage [123I]beta-CIT SPECT and long-term clinical follow-up in patients with an initial diagnosis of Parkinson's disease

beta-CIT binding in both caudate nuclei was lower than in the group of patients with IPD. In addition, putamen to caudate binding ratios were higher in the group of APS patients. In spite of these differences, individual binding values showed considerable overlap between the groups. CONCLUSION: [(123)I]beta-CIT SPECT scanning in early-stage, untreated parkinsonian patients revealed a relative sparing of the caudate nucleus in patients with IPD as compared to patients later (re)diagnosed with APS. Nevertheless, the pattern of striatal involvement appears to have little predictive value for a later re-diagnosis of APS in individual case