Neuronal networks underlying ictal and subclinical discharges in childhood absence epilepsy.

Childhood absence epilepsy (CAE), involves 3 Hz generalized spikes and waves discharges (GSWDs) on the electroencephalogram (EEG), associated with ictal discharges (seizures) with clinical symptoms and impairment of consciousness and subclinical discharges without any objective clinical symptoms or impairment of consciousness. This study aims to comparatively characterize neuronal networks underlying absence seizures and subclinical discharges, using source localization and functional connectivity (FC), to better understand the pathophysiological mechanism of these discharges. Routine EEG data from 12 CAE patients, consisting of 45 ictal and 42 subclinical discharges were selected. Source localization was performed using the exact low-resolution electromagnetic tomography (eLORETA) algorithm, followed by FC based on the imaginary part of coherency. FC based on the thalamus as the seed of interest showed significant differences between ictal and subclinical GSWDs (p < 0.05). For delta (1-3 Hz) and alpha bands (8-12 Hz), the thalamus displayed stronger connectivity towards other brain regions for ictal GSWDs as compared to subclinical GSWDs. For delta band, the thalamus was strongly connected to the posterior cingulate cortex (PCC), precuneus, angular gyrus, supramarginal gyrus, parietal superior, and occipital mid-region for ictal GSWDs. The strong connections of the thalamus with other brain regions that are important for consciousness, and with components of the default mode network (DMN) suggest the severe impairment of consciousness in ictal GSWDs. However, for subclinical discharges, weaker connectivity between the thalamus and these brain regions may suggest the prevention of impairment of consciousness. This may benefit future therapeutic targets and improve the management of CAE patients.

Expression of CD44 and CD133 stem cell markers in squamous cell carcinoma of esophagus.

CONTEXT: Role of cancer stem cells in the esophageal carcinogenesis is not clear. AIM: To assess the expression of CD44 and CD133 cancer stem cell markers in esophageal squamous cell carcinoma (ESCC) and its predisposing lesions by immunohistochemistry. SETTING AND DESIGN: Prospective study as a part of an intramural research project. MATERIALS AND METHODS: Tissues samples were obtained with endoscopic biopsy and from surgically resected esophageal specimens. Fifty cases each of histopathologically diagnosed cases of esophageal squamous cell carcinoma and its predisposing lesions (mild, moderate, and severe dysplasia and esophagitis) were evaluated for stem cell marker CD44 and C133 by immunohistochemistry using a scoring system. STATISTICAL ANALYSIS: Chi-square test, analysis of variance (ANOVA), post-hoc tests (Tukey-HSD) were used as appropriate for data analysis. Two sided P < 0.05 was considered as significant. RESULTS: CD44 expression was significantly higher in ESCC as compared to dysplasia and esophagitis (mean IS 7.92 ± 1.45 vs. 6.34 ± 0.80 vs 5.15 ± 0.86 respectively, P < 0.001). CD133 expression was also significantly higher in ESCC as compared to dysplasia (mean IS 6.82 ± 1.57 vs. 1.00 ± 0.00 respectively, P < 0.001) while esophagitis showed no expression. CD44 and CD133 expressions were significantly higher in poorly differentiated ESCC than moderately differentiated and well differentiated lesions (CD44 mean IS 6.94 ± 1.44 vs 8.17 ± 1.38 vs. 8.63 ± 1.02 respectively, P < 0.001 and CD 133 mean IRS 5.63 ± 0.81 vs 6.00 ± 00 vs. 9.0 ± 00 respectively, P < 0.001). CONCLUSION: Significantly higher expression of CD44 and CD133 cancer stem cell markers in ESCC as compared to its predisposing lesions (esophagitis and dysplasia) suggests its role in esophageal carcinogenesis.

Differentiating ictal/subclinical spikes and waves in childhood absence epilepsy by spectral and network analyses: A pilot study.

OBJECTIVE: Childhood absence epilepsy (CAE) is a disease with distinct seizure semiology and electroencephalographic (EEG) features. Differentiating ictal and subclinical generalized spikes and waves discharges (GSWDs) in the EEG is challenging, since they appear to be identical upon visual inspection. Here, spectral and functional connectivity (FC) analyses were applied to routine EEG data of CAE patients, to differentiate ictal and subclinical GSWDs. METHODS: Twelve CAE patients with both ictal and subclinical GSWDs were retrospectively selected for this study. The selected EEG epochs were subjected to frequency analysis in the range of 1-30 Hz. Further, FC analysis based on the imaginary part of coherency was used to determine sensor level networks. RESULTS: Delta, alpha and beta band frequencies during ictal GSWDs showed significantly higher power compared to subclinical GSWDs. FC showed significant network differences for all frequency bands, demonstrating weaker connectivity between channels during ictal GSWDs. CONCLUSION: Using spectral and FC analyses significant differences between ictal and subclinical GSWDs in CAE patients were detected, suggesting that these features could be used for machine learning classification purposes to improve EEG monitoring. SIGNIFICANCE: Identifying differences between ictal and subclinical GSWDs using routine EEG, may improve understanding of this syndrome and the management of patients with CAE.

An improved filtering algorithm for big read datasets and its application to single-cell assembly.

BACKGROUND: For single-cell or metagenomic sequencing projects, it is necessary to sequence with a very high mean coverage in order to make sure that all parts of the sample DNA get covered by the reads produced. This leads to huge datasets with lots of redundant data. A filtering of this data prior to assembly is advisable. Brown et al. (2012) presented the algorithm Diginorm for this purpose, which filters reads based on the abundance of their k-mers. METHODS: We present Bignorm, a faster and quality-conscious read filtering algorithm. An important new algorithmic feature is the use of phred quality scores together with a detailed analysis of the k-mer counts to decide which reads to keep. RESULTS: We qualify and recommend parameters for our new read filtering algorithm. Guided by these parameters, we remove in terms of median 97.15% of the reads while keeping the mean phred score of the filtered dataset high. Using the SDAdes assembler, we produce assemblies of high quality from these filtered datasets in a fraction of the time needed for an assembly from the datasets filtered with Diginorm. CONCLUSIONS: We conclude that read filtering is a practical and efficient method for reducing read data and for speeding up the assembly process. This applies not only for single cell assembly, as shown in this paper, but also to other projects with high mean coverage datasets like metagenomic sequencing projects. Our Bignorm algorithm allows assemblies of competitive quality in comparison to Diginorm, while being much faster. Bignorm is available for download at https://git.informatik.uni-kiel.de/axw/Bignorm .

Three-dimensional reconstruction of seed implants by randomized rounding and visual evaluation.

The development of efficient 3D seed reconstruction algorithms is an ongoing and vivid research topic. Since the 1980s many publications about seed assignment were published. In this paper a novel mathematical approach is described to solve the 3D assignment problem for the reconstruction of seeds with radiographs: we present a fast linear programming approach together with afterwards applying the so-called randomized rounding scheme to compute good (possibly partial) assignments. We apply a visualization software that allows user interaction to check the solution given by the algorithm and to augment partial assignments. The second step is justified as the randomized algorithm already returns optimal solutions is many cases, and in cases with partial assignments it fails to match only a very small number of seed images. Our algorithm transfers ideas from recent breakthrough research work on the design of efficient randomized algorithms in discrete optimization and computer science to the seed reconstruction problem.

Structural basis for the carbohydrate specificities of artocarpin: variation in the length of a loop as a strategy for generating ligand specificity.

Artocarpin, a tetrameric lectin of molecular mass 65 kDa, is one of the two lectins extracted from the seeds of jackfruit. The structures of the complexes of artocarpin with mannotriose and mannopentose reported here, together with the structures of artocarpin and its complex with Me-alpha-mannose reported earlier, show that the lectin possesses a deep-seated binding site formed by three loops. The binding site can be considered as composed of two subsites; the primary site and the secondary site. Interactions at the primary site composed of two of the loops involve mainly hydrogen bonds, while those at the secondary site comprising the third loop are primarily van der Waals in nature. Mannotriose in its complex with the lectin interacts through all the three mannopyranosyl residues; mannopentose interacts with the protein using at least three of the five mannose residues. The complexes provide a structural explanation for the carbohydrate specificities of artocarpin. A detailed comparison with the sugar complexes of heltuba, the only other mannose-specific jacalin-like lectin with known three-dimensional structure in sugar-bound form, establishes the role of the sugar-binding loop constituting the secondary site, in conferring different specificities at the oligosaccharide level. This loop is four residues longer in artocarpin than in heltuba, providing an instance where variation in loop length is used as a strategy for generating carbohydrate specificity.