sj-docx-1-nnr-10.1177_15459683231177604 – Supplemental material for Does a Cognitive Network Contribute to Motor Recovery After Ischemic Stroke?

Supplemental material, sj-docx-1-nnr-10.1177_15459683231177604 for Does a Cognitive Network Contribute to Motor Recovery After Ischemic Stroke? by Jungsoo Lee and Yun-Hee Kim in Neurorehabilitation and Neural Repair</p

Additional file 1 of Comparative study of young-old and old-old people using functional evaluation, gait characteristics, and cardiopulmonary metabolic energy consumption

Additional file 1: Supplementary Table S1. Kinematic joint angle differences between the young-old and old-old groups. Supplementary Table S2. Kinetic peak joint moment differences between the young-old and old-old groups. Supplementary Table S3. Kinetic peak joint power differences between the young-old and old-old groups. Supplementary Table S4. Self-selected treadmill walking speed and distance in the young-old and old-old groups. Supplementally Figure S1. Peak ground reaction force over a gait cycle did not differ significantly between the young-old and old-old groups. IC: Initial contact (0–2%), LR: Loading response (2–12%), MS: Mid-stance (12–31%), TS: Terminal stance (31–50%), PSw: Pre-swing (50–62%), ISw: Initial swing (62–73%), MSw: Mid-swing (73–87%), TSw: Terminal swing (87–100%)

Motor task-based differences in brain networks: Preliminary results

<p>This study examined characteristics of the brain networks related to upper limb grasp movements. EEG signal of 4 patients with chronic stroke were analyzed during different motor tasks. We compared the brain networks involved in the Active and Motor Imagery tasks by using the centrality and small-worldness (SW). There was a statistically significant difference between the centralities of two motor tasks in motor cortices of affected hemisphere in the high beta band (21 – 30 Hz). For SW, the Active task also decreased in the high beta band in contrast with the MI task. In this paper, we could support evidence that brain networks may different under the conditions of different motor tasks in both frequency and temporal domain.</p

sj-pdf-1-nnr-10.1177_15459683211070278 – Supplemental Material for Multimodal Imaging Biomarker-Based Model Using Stratification Strategies for Predicting Upper Extremity Motor Recovery in Severe Stroke Patients

Supplemental Material, sj-pdf-1-nnr-10.1177_15459683211070278 for Multimodal Imaging Biomarker-Based Model Using Stratification Strategies for Predicting Upper Extremity Motor Recovery in Severe Stroke Patients by Jungsoo Lee, Heegoo Kim, Jinuk Kim, Won Hyuk Chang, and Yun-Hee Kim in Neurorehabilitation and Neural Repair</p

Additional file 3: Figure S3. of EEG response varies with lesion location in patients with chronic stroke

Pearson’s correlation coefficients for the beta band power changes between the HCs and each of the three patient subgroups for each of the 28 channels during the MI task supination movement. Significant results of a pairwise statistical analysis on the differences in correlation coefficients are indicated (one-way ANOVA test, *p < 0.05; **p < 0.01). (JPG 640 kb

Preparation and welding of the pressure aluminium pipes

The project is an analysis of technology production of pressure pipes made of aluminum alloys. The basis is a literary study of TIG technology, aluminum heat-tretable and non-heat-treatable materials. The flange-material is EN AW 5083 and the pipe is made of EN AW 6005A. The design of the welding is compromise between the preparation, the cleaning of the welding edges and the weld metal backing strip. Weld was made in real production. Examined impacts are evaluated on the basis of destructive and non-destructive welding test methods. After heating process of weldment material exhibits better mechanical properties. Using the economical and technological evaluation were selected sutiable proces parameters. The result is a suitable weld of the pressure vessel. Further optimization is possible through automation and robotics

Additional file 5: Figure S5. of EEG response varies with lesion location in patients with chronic stroke

Twenty-eight channel topography of the beta band during active supination (left side) and grasping (right side) movements. The horizontal axis represents 2 s of the motor task with a 0.5-s window interval. The vertical axis represents the subject groups. The upper three rows represent each subgroup of patients according to their lesion location. The fourth row represents all patients and the last row represents healthy controls. (JPG 1727 kb

Flashlight into the Function of Unannotated C11orf52 using Affinity Purification Mass Spectrometry

For an enhanced understanding of the biological mechanisms of human disease, it is essential to investigate protein functions. In a previous study, we developed a prediction method of gene ontology (GO) terms by the I-TASSER/COFACTOR result, and we applied this to uPE1 in chromosome 11. Here, to validate the bioinformatics prediction of C11orf52, we utilized affinity purification and mass spectrometry to identify interacting partners of C11orf52. Using immunoprecipitation methods with three different peptide tags (Myc, Flag, and 2B8) in HEK 293T cell lines, we identified 79 candidate proteins that are expected to interact with C11orf52. The results of a pathway analysis of the GO and STRING database with candidate proteins showed that C11orf52 could be related to signaling receptor binding, cell–cell adhesion, and ribosome biogenesis. Then, we selected three partner candidates of DSG1, JUP, and PTPN11 for verification of the interaction with C11orf52 and confirmed them by colocalization at the cell–cell junctions by coimmunofluorescence experiments. On the basis of this study, we expect that C11orf52 is related to the Wnt signaling pathway via DSG1 from the protein–protein interactions, given the results of a comprehensive analysis of the bioinformatic predictions. The data set is available at the ProteomeXchange consortium via PRIDE repository (PXD026986)

Flashlight into the Function of Unannotated C11orf52 using Affinity Purification Mass Spectrometry

For an enhanced understanding of the biological mechanisms of human disease, it is essential to investigate protein functions. In a previous study, we developed a prediction method of gene ontology (GO) terms by the I-TASSER/COFACTOR result, and we applied this to uPE1 in chromosome 11. Here, to validate the bioinformatics prediction of C11orf52, we utilized affinity purification and mass spectrometry to identify interacting partners of C11orf52. Using immunoprecipitation methods with three different peptide tags (Myc, Flag, and 2B8) in HEK 293T cell lines, we identified 79 candidate proteins that are expected to interact with C11orf52. The results of a pathway analysis of the GO and STRING database with candidate proteins showed that C11orf52 could be related to signaling receptor binding, cell–cell adhesion, and ribosome biogenesis. Then, we selected three partner candidates of DSG1, JUP, and PTPN11 for verification of the interaction with C11orf52 and confirmed them by colocalization at the cell–cell junctions by coimmunofluorescence experiments. On the basis of this study, we expect that C11orf52 is related to the Wnt signaling pathway via DSG1 from the protein–protein interactions, given the results of a comprehensive analysis of the bioinformatic predictions. The data set is available at the ProteomeXchange consortium via PRIDE repository (PXD026986)