Bacterial Anaerobic Synthesis Gas (Syngas) and CO2+H2 Fermentation.

Anaerobic bacterial gas fermentation gains broad interest in various scientific, social, and industrial fields. This microbial process is carried out by a specific group of bacterial strains called acetogens. All these strains employ the Wood-Ljungdahl pathway but they belong to different taxonomic groups. Here we provide an overview of the metabolism of acetogens and naturally occurring products. Characteristics of 61 strains were summarized and selected acetogens described in detail. Acetobacterium woodii, Clostridium ljungdahlii, and Moorella thermoacetica serve as model organisms. Results of approaches such as genome-scale modeling, proteomics, and transcriptomics are discussed. Metabolic engineering of acetogens can be used to expand the product portfolio to platform chemicals and to study different aspects of cell physiology. Moreover, the fermentation of gases requires specific reactor configurations and the development of the respective technology, which can be used for an industrial application. Even though the overall process will have a positive effect on climate, since waste and greenhouse gases could be converted into commodity chemicals, some legislative barriers exist, which hamper successful exploitation of this technology.

Complete Genome Sequence of the Autotrophic Acetogen Clostridium formicaceticum DSM 92T Using Nanopore and Illumina Sequencing Data.

Here, we report the closed genome sequence of Clostridium formicaceticum, an Rnf- and cytochrome-containing autotrophic acetogen that is able to convert carbon monoxide to acetate using the Wood-Ljungdahl pathway. The genome consists of a circular chromosome (4.59 Mb).

Magnetic resonance imaging diffusion tensor tractography: evaluation of anatomic accuracy of different fiber tracking software packages.

BACKGROUND: Diffusion tensor imaging (DTI)-based tractography has become an integral part of preoperative diagnostic imaging in many neurosurgical centers, and other nonsurgical specialties depend increasingly on DTI tractography as a diagnostic tool. The aim of this study was to analyze the anatomic accuracy of visualized white matter fiber pathways using different, readily available DTI tractography software programs. METHODS: Magnetic resonance imaging scans of the head of 20 healthy volunteers were acquired using a Siemens Symphony TIM 1.5T scanner and a 12-channel head array coil. The standard settings of the scans in this study were 12 diffusion directions and 5-mm slices. The fornices were chosen as an anatomic structure for the comparative fiber tracking. Identical data sets were loaded into nine different fiber tracking packages that used different algorithms. The nine software packages and algorithms used were NeuroQLab (modified tensor deflection [TEND] algorithm), Sörensen DTI task card (modified streamline tracking technique algorithm), Siemens DTI module (modified fourth-order Runge-Kutta algorithm), six different software packages from Trackvis (interpolated streamline algorithm, modified FACT algorithm, second-order Runge-Kutta algorithm, Q-ball [FACT algorithm], tensorline algorithm, Q-ball [second-order Runge-Kutta algorithm]), DTI Query (modified streamline tracking technique algorithm), Medinria (modified TEND algorithm), Brainvoyager (modified TEND algorithm), DTI Studio modified FACT algorithm, and the BrainLab DTI module based on the modified Runge-Kutta algorithm. Three examiners (a neuroradiologist, a magnetic resonance imaging physicist, and a neurosurgeon) served as examiners. They were double-blinded with respect to the test subject and the fiber tracking software used in the presented images. Each examiner evaluated 301 images. The examiners were instructed to evaluate screenshots from the different programs based on two main criteria: (i) anatomic accuracy of the course of the displayed fibers and (ii) number of fibers displayed outside the anatomic boundaries. RESULTS: The mean overall grade for anatomic accuracy was 2.2 (range, 1.1-3.6) with a standard deviation (SD) of 0.9. The mean overall grade for incorrectly displayed fibers was 2.5 (range, 1.6-3.5) with a SD of 0.6. The mean grade of the overall program ranking was 2.3 with a SD of 0.6. The overall mean grade of the program ranked number one (NeuroQLab) was 1.7 (range, 1.5-2.8). The mean overall grade of the program ranked last (BrainLab iPlan Cranial 2.6 DTI Module) was 3.3 (range, 1.7-4). The difference between the mean grades of these two programs was statistically highly significant (P < 0.0001). There was no statistically significant difference between the programs ranked 1-3: NeuroQLab, Sörensen DTI Task Card, and Siemens DTI module. CONCLUSIONS: The results of this study show that there is a statistically significant difference in the anatomic accuracy of the tested DTI fiber tracking programs. Although incorrectly displayed fibers could lead to wrong conclusions in the neurosciences field, which relies heavily on this noninvasive imaging technique, incorrectly displayed fibers in neurosurgery could lead to surgical decisions potentially harmful for the patient if used without intraoperative cortical stimulation. DTI fiber tracking presents a valuable noninvasive preoperative imaging tool, which requires further validation after important standardization of the acquisition and processing techniques currently available.