A Novel Apoptosis Correlated Molecule: Expression and Characterization of Protein Latcripin-1 from Lentinula edodes C91–3

An apoptosis correlated molecule—protein Latcripin-1 of Lentinula edodes C91–3—was expressed and characterized in Pichia pastoris GS115. The total RNA was obtained from Lentinula edodes C91–3. According to the transcriptome, the full-length gene of Latcripin-1 was isolated with 3′-Full Rapid Amplification of cDNA Ends (RACE) and 5′-Full RACE methods. The full-length gene was inserted into the secretory expression vector pPIC9K. The protein Latcripin-1 was expressed in Pichia pastoris GS115 and analyzed by Sodium Dodecylsulfonate Polyacrylate Gel Electrophoresis (SDS-PAGE) and Western blot. The Western blot showed that the protein was expressed successfully. The biological function of protein Latcripin-1 on A549 cells was studied with flow cytometry and the 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyl-tetrazolium Bromide (MTT) method. The toxic effect of protein Latcripin-1 was detected with the MTT method by co-culturing the characterized protein with chick embryo fibroblasts. The MTT assay results showed that there was a great difference between protein Latcripin-1 groups and the control group (p < 0.05). There was no toxic effect of the characterized protein on chick embryo fibroblasts. The flow cytometry showed that there was a significant difference between the protein groups of interest and the control group according to apoptosis function (p < 0.05). At the same time, cell ultrastructure observed by transmission electron microscopy supported the results of flow cytometry. The work demonstrates that protein Latcripin-1 can induce apoptosis of human lung cancer cells A549 and brings new insights into and advantages to finding anti-tumor proteins

Geodesic distance on optimally regularized functional connectomes uncovers individual fingerprints

Background: Functional connectomes (FCs), have been shown to provide a reproducible individual fingerprint, which has opened the possibility of personalized medicine for neuro/psychiatric disorders. Thus, developing accurate ways to compare FCs is essential to establish associations with behavior and/or cognition at the individual-level. Methods: Canonically, FCs are compared using Pearson's correlation coefficient of the entire functional connectivity profiles. Recently, it has been proposed that the use of geodesic distance is a more accurate way of comparing functional connectomes, one which reflects the underlying non-Euclidean geometry of the data. Computing geodesic distance requires FCs to be positive-definite and hence invertible matrices. As this requirement depends on the fMRI scanning length and the parcellation used, it is not always attainable and sometimes a regularization procedure is required. Results: In the present work, we show that regularization is not only an algebraic operation for making FCs invertible, but also that an optimal magnitude of regularization leads to systematically higher fingerprints. We also show evidence that optimal regularization is dataset-dependent, and varies as a function of condition, parcellation, scanning length, and the number of frames used to compute the FCs. Discussion: We demonstrate that a universally fixed regularization does not fully uncover the potential of geodesic distance on individual fingerprinting, and indeed could severely diminish it. Thus, an optimal regularization must be estimated on each dataset to uncover the most differentiable across-subject and reproducible within-subject geodesic distances between FCs. The resulting pairwise geodesic distances at the optimal regularization level constitute a very reliable quantification of differences between subjects.Comment: 39 pages, 7 figures, 4 table

GEFF: Graph Embedding for Functional Fingerprinting

It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate is high when using resting-state FCs, other tasks show moderate to low values. Furthermore, identification rate is task-dependent, and is low when distinct cognitive states, as captured by different fMRI tasks, are compared. Here we propose an embedding framework, GEFF (Graph Embedding for Functional Fingerprinting), based on group-level decomposition of FCs into eigenvectors. GEFF creates an eigenspace representation of a group of subjects using one or more task FCs (Learning Stage). In the Identification Stage, we compare new instances of FCs from the Learning subjects within this eigenspace (validation dataset). The validation dataset contains FCs either from the same tasks as the Learning dataset or from the remaining tasks that were not included in Learning. Assessment of validation FCs within the eigenspace results in significantly increased subject-identification rates for all fMRI tasks tested and potentially task-independent fingerprinting process. It is noteworthy that combining resting-state with one fMRI task for GEFF Learning Stage covers most of the cognitive space for subject identification. In addition to subject-identification, GEFF was also used for identification of cognitive states, i.e. to identify the task associated to a given FC, regardless of the subject being already in the Learning dataset or not (subject-independent task-identification). In addition, we also show that eigenvectors from the Learning Stage can be characterized as task-dominant, subject dominant or neither, providing a deeper insight into the extent of variance in functional connectivity across individuals and cognitive states.Comment: 30 pages; 6 figures; 5 supplementary figure

GEFF: Graph Embedding for Functional Fingerprinting

It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate is high when using resting-state FCs, other tasks show moderate to low values. Furthermore, identification rate is task-dependent, and is low when distinct cognitive states, as captured by different fMRI tasks, are compared. Here we propose an embedding framework, GEFF (Graph Embedding for Functional Fingerprinting), based on group-level decomposition of FCs into eigenvectors. GEFF creates an eigenspace representation of a group of subjects using one or more task FCs (Learning Stage). In the Identification Stage, we compare new instances of FCs from the Learning subjects within this eigenspace (validation dataset). The validation dataset contains FCs either from the same tasks as the Learning dataset or from the remaining tasks that were not included in Learning. Assessment of validation FCs within the eigenspace results in significantly increased subject-identification rates for all fMRI tasks tested and potentially task-independent fingerprinting process. It is noteworthy that combining resting-state with one fMRI task for GEFF Learning Stage covers most of the cognitive space for subject identification. Thus, while designing an experiment, one could choose a task fMRI to ask a specific question and combine it with resting-state fMRI to extract maximum subject differentiability using GEFF. In addition to subject-identification, GEFF was also used for identification of cognitive states, i.e. to identify the task associated to a given FC, regardless of the subject being already in the Learning dataset or not (subject-independent task-identification). In addition, we also show that eigenvectors from the Learning Stage can be characterized as task- and subject-dominant, subject-dominant or neither, using two-way ANOVA of their corresponding loadings, providing a deeper insight into the extent of variance in functional connectivity across individuals and cognitive states

Tangent functional connectomes uncover more unique phenotypic traits

Functional connectomes (FCs) contain pairwise estimations of functional couplings based on pairs of brain regions activity. FCs are commonly represented as correlation matrices that are symmetric positive definite (SPD) lying on or inside the SPD manifold. Since the geometry on the SPD manifold is non-Euclidean, the inter-related entries of FCs undermine the use of Euclidean-based distances. By projecting FCs into a tangent space, we can obtain tangent functional connectomes (tangent-FCs). Tangent-FCs have shown a higher predictive power of behavior and cognition, but no studies have evaluated the effect of such projections with respect to fingerprinting. We hypothesize that tangent-FCs have a higher fingerprint than regular FCs. Fingerprinting was measured by identification rates (ID rates) on test-retest FCs as well as on monozygotic and dizygotic twins. Our results showed that identification rates are systematically higher when using tangent-FCs. Specifically, we found: (i) Riemann and log-Euclidean matrix references systematically led to higher ID rates. (ii) In tangent-FCs, Main-diagonal regularization prior to tangent space projection was critical for ID rate when using Euclidean distance, whereas barely affected ID rates when using correlation distance. (iii) ID rates were dependent on condition and fMRI scan length. (iv) Parcellation granularity was key for ID rates in FCs, as well as in tangent-FCs with fixed regularization, whereas optimal regularization of tangent-FCs mostly removed this effect. (v) Correlation distance in tangent-FCs outperformed any other configuration of distance on FCs or on tangent-FCs across the fingerprint gradient (here sampled by assessing test-retest, Monozygotic and Dizygotic twins). (vi)ID rates tended to be higher in task scans compared to resting-state scans when accounting for fMRI scan length.Comment: 29 pages, 10 figures, 2 table

Biodegradable Polymer-Coated, Gelatin Hydrogel/Bioceramics Ternary Composites for Antitubercular Drug Delivery and Tissue Regeneration

A simple and effective strategy for the treatment of osteoarticular tuberculosis is proposed through combining tissue engineering approach with anti-tuberculosis drug therapy. A series of tricalcium phosphate bioceramics (TPB) composites, coated by degradable polymer outside and loaded with rifampicin (RFP)-containing gelatin hydrogel inside, were thus fabricated and successfully applied to deliver antitubercular drug RFP into osseous lesion and concomitantly to induce tissue regeneration. RFP-loaded gelatin hydrogel/TPB composites could be readily prepared by filling RFP-containing gelatin solution into TPB and then in situ crosslinking of gelatin with calcium ions. Depending on the concentrations of RFP, the loading efficiency of RFP in the composites varied in the range from approximately 2% to 5%. Moreover, the surface of these binary composites could be further coated by a biodegradable polymer, yielding biodegradable polymer-coated, RFP-containing gelatin hydrogel/TPB ternary composites. It was shown that in vitro release of RFP from the ternary composites could be effectively sustained for a long period of time. Besides, these composites revealed good biocompatibility towards the survival of MC-3T3 cells in vitro and could be used for tissue regeneration in vivo in a rabbit model. The results indicate that TPB ternary composites have great potential for the treatment of osteoarticular tuberculosis

Modeling of the dynamic behaviors of heat transfer during the construction of roadway using moving mesh

Heat hazard is major challenge for the safe and efficient exploitation of deep resources. Understanding the heat transfer behaviors in roadway is the premise of temperature prediction and ventilation design. A fully coupled model incorporated with a moving mesh method was developed, which considers the convective heat transfer between surrounding rock and airflow, unsteady-state heat transfer in rock, and non-isothermal flow in roadway. The characteristics of thermal performance and its evolution law in an excavating roadway were obtained. The numerical model was validated against previous experimental data with a deviation of less than 3%. Analysis of the airflow and temperature field revealed the characteristic of convection heat transfer in the wall and local heat accumulation in roadway. The air temperature in roadway is associated with the airflow characteristics, and a local high temperature zone is presented in the vortex zone. By comparing the heat flux of the surrounding rock and excavation condition, it is found that the heat released from the working face poses a crucial effect on the airflow temperature in roadway. The present study provided a robust theoretical basis for improving cooling efficiency and thermal comfort in roadway construction