A robust transformer-based pipeline of 3D cell alignment, denoise and instance segmentation on electron microscopy sequence images

Germline cells are critical for transmitting genetic information to subsequent generations in biological organisms. While their differentiation from somatic cells during embryonic development is well-documented in most animals, the regulatory mechanisms initiating plant germline cells are not well understood. To thoroughly investigate the complex morphological transformations of their ultrastructure over developmental time, nanoscale 3D reconstruction of entire plant tissues is necessary, achievable exclusively through electron microscopy imaging. This paper presents a full-process framework designed for reconstructing large-volume plant tissue from serial electron microscopy images. The framework ensures end-to-end direct output of reconstruction results, including topological networks and morphological analysis. The proposed 3D cell alignment, denoise, and instance segmentation pipeline (3DCADS) leverages deep learning to provide a cell instance segmentation workflow for electron microscopy image series, ensuring accurate and robust 3D cell reconstructions with high computational efficiency. The pipeline involves five stages: the registration of electron microscopy serial images; image enhancement and denoising; semantic segmentation using a Transformer-based neural network; instance segmentation through a supervoxel-based clustering algorithm; and an automated analysis and statistical assessment of the reconstruction results, with the mapping of topological connections. The 3DCADS model's precision was validated on a plant tissue ground-truth dataset, outperforming traditional baseline models and deep learning baselines in overall accuracy. The framework was applied to the reconstruction of early meiosis stages in the anthers of Arabidopsis thaliana, resulting in a topological connectivity network and analysis of morphological parameters and characteristics of cell distribution. The experiment underscores the 3DCADS model's potential for biological tissue identification and its significance in quantitative analysis of plant cell development, crucial for examining samples across different genetic phenotypes and mutations in plant development. Additionally, the paper discusses the regulatory mechanisms of Arabidopsis thaliana's germline cells and the development of stamen cells before meiosis, offering new insights into the transition from somatic to germline cell fate in plants.

EnsemBic: An effective ensemble of biclustering to identify potential biomarkers of esophageal squamous cell carcinoma

The development of functionally enriched and biologically competent biclustering algorithm is essential for extracting hidden information from massive biological datasets. This paper presents a novel biclustering ensemble called EnsemBic based on p-value, which calculates the functional similarity of genetic associations. To validate the effectiveness and robustness of EnsemBic, we apply three well-known biclustering techniques, viz. Laplace Prior, iBBiG, and xMotif to implement EnsemBic and have been compared using different leading parameters. It is observed that the EnsemBic outperforms its competing algorithms in several prominent functional and biological measures. Next, the biclusters obtained from EnsemBic are used to identify potential biomarkers of Esophageal Squamous Cell Carcinoma (ESCC) by exploring topological and biological relevance with reference to the elite genes, attained from genecards. Finally, we discover that the genes F2RL3, APPL1, CALM1, IFNGR1, LPAR1, ANGPT2, ARPC2, CGN, CLDN7, ATP6V1C2, CEACAM1, FTL, PLAU,PSMB4, and EPHB2 carry both the topological and biological significance of previously established ESCC elite genes. Therefore, we declare the aforementioned genes as potential biomarkers of ESCC.

Development and Validation of the Diagnostic Model of 7 Gene in Endometriosis

AIMS

To explore the diagnostic biomarkers for diagnosing endometriosis.

BACKGROUND

Endometriosis is a benign, progressive, estrogen-dependent gynecological disorder that has highly variant prevalence. Therefore, it is essential to develop reliable diagnostic biomarkers for endometriosis diagnosis.

OBJECTIVE

To explore the diagnostic biomarkers for endometriosis diagnosis.

METHOD

Based on transcriptome data from GSE145701, we identified potential therapeutic targets through the intersection of endometriosis-related genes from weighted gene correlation network analysis (WGCNA) and differential expression analysis. Aprotein-protein interaction (PPI) was constructed. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were employed for functional enrichment analysis. The intersection of hub genes from topological analysis and module genes from module-based network analysis were selected as core targets, which were used for diagnostic model construction. Its robustness was validated using GSE7305 and GSE134056. Associations of core targets with immune characteristics and pathways were further evaluated. Molecular docking was employed to evaluate the docking affinity between core targets and drugs. Additionally, western blot and quantitative real-time polymerase chain reaction were also carried out to validate molecular docking results.

RESULT

A diagnostic model was constructed using 7 core targets, which had a high diagnostic ability for endometriosis. CTSK was positively correlated with immune scores, while CDH2 was negatively correlated with immune scores. CTSK, HGF, and EPCAM were positively correlated with energy metabolism and inflammation-related pathways, while RUNX2, FN1, NCAM1, and CDH2 were positively correlated with epithelial-to-mesenchymal transition (EMT) and unfolded protein response (UPR). Moreover, FN1 had good docking affinity with Elagolix, Esmya, and Proellex. NCAM1 might be a promising target modulated by Elagolix. in vitro experiment revealed that the expression of FN1 in human normal endometrial cell lines (hEEC) gradually decreased with the increase of Esmya concentration, indicating that FN1 was a target for Esmya.

CONCLUSION

These results may facilitate the in-depth understanding of the development of endometriosis, and guide early diagnostic as well as clinical treatments for patients with endometriosis.

Activation network improves spatiotemporal modelling of human brain communication processes

Dynamic functional networks (DFN) have considerably advanced modelling of the brain communication processes. The prevailing implementation capitalizes on the system and network-level correlations between time series. However, this approach does not account for the continuous impact of non-dynamic dependencies within the statistical correlation, resulting in relatively stable connectivity patterns of DFN over time with limited sensitivity for communication dynamic between brain regions. Here, we propose an activation network framework based on the activity of functional connectivity (AFC) to extract new types of connectivity patterns during brain communication process. The AFC captures potential time-specific fluctuations associated with the brain communication processes by eliminating the non-dynamic dependency of the statistical correlation. In a simulation study, the positive correlation (r=0.966,p<0.001) between the extracted dynamic dependencies and the simulated "ground truth" validates the method's dynamic detection capability. Applying to autism spectrum disorders (ASD) and COVID-19 datasets, the proposed activation network extracts richer topological reorganization information, which is largely invisible to the DFN. Detailed, the activation network exhibits significant inter-regional connections between function-specific subnetworks and reconfigures more efficiently in the temporal dimension. Furthermore, the DFN fails to distinguish between patients and healthy controls. However, the proposed method reveals a significant decrease (p<0.05) in brain information processing abilities in patients. Finally, combining two types of networks successfully classifies ASD (83.636 % ± 11.969 %,mean±std) and COVID-19 (67.333 % ± 5.398 %). These findings suggest the proposed method could be a potential analytic framework for elucidating the neural mechanism of brain dynamics.

Topological analysis and reactivity study of monomeric and dimeric forms of 2-methyl-4(1H)-quinolone: a computational study

CONTEXT

Quinolone derivatives have gathered major attention largely due to their wonderful biological activities. Quinolones are a class of molecules that are derived from quinolines and also extracted from natural sources. Most of these quinolones have significant medicinal properties ranging from antiallergenic and anticancer to antimicrobial activities. Some bacteria produce several 2-alkyl-4(1H)-quinolones. In past years, a variety of methods have been reported for the synthesis of quinolone derivatives. In this present work, structural, wave functional, and electronic properties of monomeric and dimeric forms of 2-methyl-4(1H)-quinolone are investigated. From the calculated binding energies, it was found that the formation of dimers is thermodynamically favorable. The analysis of reactivity parameters confirms that the keto form is more reactive than the enol form and keto-keto dimer is more reactive than compared to all monomeric and dimeric forms of our studied compound.

METHODS

Geometry optimizations of monomers and dimers of studied molecules were carried out using the B3LYP-D3(BJ)/ma-def2-TZVPP level of theory. The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies were calculated using the B3LYP/def2-TZVP level of theory. All DFT calculations were done with the ORCA 5.0.3 program. The reactivity parameters such as ionization potential, electron affinity, global hardness, global softness, electronegativity, chemical potential, and electrophilicity index were calculated. The nature of intermolecular interactions within the dimers was studied using topological analysis such as atoms in molecule (AIM) and reduced density gradient (RDG) surface analyses. To visualize the electron delocalization in the dimer electron localization function (ELF) and localized orbital locator (LOL) studies were also performed. The analyses such as AIM, RDG, ELF, and LOL were carried out by the multifunctional wavefunction analysis program Multiwfn 3.8.

Conformation and vibrational spectroscopic analysis of 2,6-bis(4-fluorophenyl)-3,3-dimethylpiperidin-4-one (BFDP) by DFT method: A potent anti-Parkinson's, anti-lung cancer, and anti-human infectious agent

The potential of 2,6-bis(4-fluorophenyl)-3,3-dimethylpiperidin-4-one (BFDP) as an anti-Parkinson's, anti-lung cancer, and anti-human infectious agent was extensively assessed in the current study. To accomplish this, the compound BFDP was synthesised and analysed using several spectroscopic approaches, such as NMR, mass and FT-IR spectral studies. The computational calculations for the molecule were carried out using density functional theory (DFT) at the B3LYP/6-311G++ (d,p) level of theory. A X-ray diffraction (XRD) study allows us to analyse the crystalline structure of our BFDP molecule. Intermolecular interactions were assessed using 3D Hirshfeld surfaces (3D-HS) and 2D fingerprint plots. AIM and NCI-RDG were done using quantum calculations and the DFT technique, and topological ELF and LOL, as well as vibrational parameters, have been obtained. The thermodynamic and thermal properties of the BFDP compound were determined. To investigate the pharmacokinetic characteristics of BFDP, a molecular docking study and an in silico ADMET study were done.

Tau-PET abnormality as a biomarker for Alzheimer's disease staging and early detection: a topological perspective

Alzheimer's disease can be detected early through biomarkers such as tau positron emission tomography (PET) imaging, which shows abnormal protein accumulations in the brain. The standardized uptake value ratio (SUVR) is often used to quantify tau-PET imaging, but topological information from multiple brain regions is also linked to tau pathology. Here a new method was developed to investigate the correlations between brain regions using subject-level tau networks. Participants with cognitive normal (74), early mild cognitive impairment (35), late mild cognitive impairment (32), and Alzheimer's disease (40) were included. The abnormality network from each scan was constructed to extract topological features, and 7 functional clusters were further analyzed for connectivity strengths. Results showed that the proposed method performed better than conventional SUVR measures for disease staging and prodromal sign detection. For example, when to differ healthy subjects with and without amyloid deposition, topological biomarker is significant with P < 0.01, SUVR is not with P > 0.05. Functionally significant clusters, i.e. medial temporal lobe, default mode network, and visual-related regions, were identified as critical hubs vulnerable to early disease conversion before mild cognitive impairment. These findings were replicated in an independent data cohort, demonstrating the potential to monitor the early sign and progression of Alzheimer's disease from a topological perspective for individual.

A perception into binary and ternary copper (II) complexes: synthesis, characterization, DFT modeling, antimicrobial activity, protein binding screen, and amino acid interaction

Ensuring healthy lives and promoting well-being for all at all ages is the third goal of the sustainable development plan, so it was necessary to identify the most important problems that threaten health in our world. The World Health Organization declared that antibiotic resistance is one of the uppermost global public health threats facing humanity and searching for new antibiotics is slow. This problem can be approached by improving available drugs to combat various bacterial threats. To circumvent bacterial resistance, three copper(II) complexes based on the pefloxacin drug were prepared and characterized using analytical, spectroscopic, and thermal techniques. The resulting data suggested the formation of one octahedral binary and two distorted square pyramidal ternary complexes. Fluorescence spectra results revealed the formation of a turn-on fluorophore for amino acid detection. Computational calculations investigated quantum and reactivity parameters. Molecular electrostatic potential profiles and noncovalent bond interaction-reduced density gradient analysis indicated the active sites on the complex surface. The complexes were subjected to six microbial species, where the octahedral binary complex provoked its antimicrobial potency in comparison with ternary complexes. The enhanced antimicrobial activity against gram-negative bacterium E-coli compared to gentamicin was exhibited by the three complexes. Docking simulation was performed based on the crystal structure of E. coli and S. pneumoniae receptors using 5I2D and 6O15 codes. The binary complex exhibited a potent fitness score with 5I2D (TBE = - 107 kcal/mol) while ternary complexes displayed the highest docked score of fitness with 6O15.

Structural, Theoretical Investigations, Hirshfeld Surface Analysis, and Cytotoxicity Profile of a Neocuproine-Co(II)-Based Discrete Homodinuclear Complex

In this work, we aimed to synthesize a new cobalt(II) complex, namely [Co₂(μ-HIPA)(NC)₂(H₂O)₃(NO₃)]·(NO₃)(C₂H₅OH)(1) (where H₃IPA = 5-hydroxy isophthalic acid and NC = 2,9-dimethyl-1,10-phenanthroline or neocuproine), as a promising chemotherapeutic agent. The diffraction (single crystal-XRD and powder-XRD), spectroscopic (FTIR and UV-visible), molar conductance, and thermal techniques were used to characterize complex 1. Single-crystal X-ray diffraction analysis reveals that Co(II) exists in an octahedral geometry, with the ligation of four oxygen atoms, and two nitrogen atoms. Topological analysis of complex 1 reveals 2,6C6 topological type as an underlying net. The plausible intermolecular interactions within complex 1 that control the crystal packing were analyzed by Hirshfeld surface analysis. In vitro cytotoxicity of complex 1 was evaluated against acute myeloid leukemia (THP-1), colorectal (SW480), and prostate (PC-3) cancer cell lines by utilizing an MTT assay. The result shows that complex 1 can inhibit the growth of cancer cells (THP-1, SW480, and PC-3) at lower inhibitory concentration (IC₅₀) values of > 100, 43.6, and 95.1 µM respectively. The morphological changes induced by complex 1 on THP-1 and SW480 cancer cell lines were carried out with acridine orange/ethidium bromide staining methods. Additionally, comprehensive molecular docking studies were performed to understand the potential binding interactions of complex 1 with different bio-macromolecules.

Aberrant dynamic Functional-Structural connectivity coupling of Large-scale brain networks in poststroke motor dysfunction

BACKGROUND AND PURPOSE

Stroke may lead to widespread functional and structural reorganization in the brain. Several studies have reported a potential correlation between functional network changes and structural network changes after stroke. However, it is unclear how functional-structural relationships change dynamically over the course of one resting-state fMRI scan in patients following a stroke; furthermore, we know little about their relationships with the severity of motor dysfunction. Therefore, this study aimed to investigate dynamic functional and structural connectivity (FC-SC) coupling and its relationship with motor function in subcortical stroke from the perspective of network dynamics.

METHODS

Resting-state functional magnetic resonance imaging and diffusion tensor imaging were obtained from 39 S patients (19 severe and 20 moderate) and 22 healthy controls (HCs). Brain structural networks were constructed by tracking fiber tracts in diffusion tensor imaging, and structural network topology metrics were calculated using a graph-theoretic approach. Independent component analysis, the sliding window method, and k-means clustering were used to calculate dynamic functional connectivity and to estimate different dynamic connectivity states. The temporal patterns and intergroup differences of FC-SC coupling were analyzed within each state. We also calculated dynamic FC-SC coupling and its relationship with functional network efficiency. In addition, the correlation between FC-SC coupling and the Fugl-Meyer assessment scale was analyzed.

RESULTS

For SC, stroke patients showed lower global efficiency than HCs (all P < 0.05), and severely affected patients had a higher characteristic path length (P = 0.003). For FC and FC-SC coupling, stroke patients predominantly showed lower local efficiency and reduced FC-SC coupling than HCs in state 2 (all P < 0.05). Furthermore, severely affected patients also showed lower local efficiency (P = 0.031) and reduced FC-SC coupling (P = 0.043) in state 3, which was markedly linked to the severity of motor dysfunction after stroke. In addition, FC-SC coupling was correlated with functional network efficiency in state 2 in moderately affected patients (r = 0.631, P = 0.004) but not significantly in severely affected patients.

CONCLUSIONS

Stroke patients show abnormal dynamic FC-SC coupling characteristics, especially in individuals with severe injuries. These findings may contribute to a better understanding of the anatomical functional interactions underlying motor deficits in stroke patients and provide useful information for personalized rehabilitation strategies.

An ER-targeted "reserve-release" fluorogen for topological quantification of reticulophagy

The endoplasmic reticulum's (ER) dynamic nature, essential for maintaining cellular homeostasis, can be influenced by stress-induced damage, which can be assessed by examining the morphology of ER dynamics and, more locally, ER properties such as hydrophobicity, viscosity, and polarity. Although numerous ER-specific chemical probes have been developed to monitor the ER's physical and chemical parameters, the quantitative detection and super-resolution imaging of its local hydrophobicity have yet to be explored. Here, we describe a photostable ER-targeted probe with high signal-to-noise ratio for super-resolution imaging that can specifically respond to changes in ER hydrophobicity under stress based on a "reserve-release" mechanism. The probe shows an excellent ability to target ER over commercial ER dyes and can be used to track local changes of hydrophobicity by fluorescence intensity and morphology during the selective autophagy of ER (i.e., reticulophagy). By correlating the level and location of ER damage with the distribution of fluorescence intensity, we were able to assess reticulophagy at the subcellular level. Beyond that, we developed a topological analytical tool adaptable to any ER probe for detecting structural changes in ER and thus quantitatively identifying reticulophagy. The algorithm-assisted tool can also be adapted to a wide range of molecular probes and organelles. Altogether, the new probe and analytical strategy described here show promise for the quantitative detection and analysis of subtle ER damage and stress.

Noncovalent interactions between benzochalcogenadiazoles and nitrogen bases

A theoretical study has been carried out on the intermolecular interactions between tetrafluoro-benzochalcogenadiazoles (chalcogen = S, Se, Te) and a series of nitrogen bases (FCN, ClCN, NP, trans-N₂H₂, pyridine, pyrazole, imidazole) at the B97-D3/def2-TZVP level, to obtain a better insight into the nature and strength of Ch···N chalcogen bond and secondary interaction in the binary and 1:2 ternary complexes. The dispersion force plays a prominent role on the stability of the sulfur complexes, and the electrostatic effect enhanced for the heavier chalcogen complexes. Most of intermolecular bonds display the characters of closed-shell and noncovalent interaction. For the complexes involving pyridine and imidazole, chalcogen bond is stronger than hydrogen bond, while the strength of chalcogen bond is equivalent to the secondary interaction for other complexes. With the addition of nitrogen base in the 1:2 complexes, chalcogen bond is weakened, while the secondary interaction remains unchanged. In the 1:2 complexes formed by pyridine and imidazole, stronger chalcogen bond results in larger negative cooperativity than that of other complexes.

NORMA: The Network Makeup Artist - A Web Tool for Network Annotation Visualization

The Network Makeup Artist (NORMA) is a web tool for interactive network annotation visualization and topological analysis, able to handle multiple networks and annotations simultaneously. Precalculated annotations (e.g., Gene Ontology, Pathway enrichment, community detection, or clustering results) can be uploaded and visualized in a network, either as colored pie-chart nodes or as color-filled areas in a 2D/3D Venn-diagram-like style. In the case where no annotation exists, algorithms for automated community detection are offered. Users can adjust the network views using standard layout algorithms or allow NORMA to slightly modify them for visually better group separation. Once a network view is set, users can interactively select and highlight any group of interest in order to generate publication-ready figures. Briefly, with NORMA, users can encode three types of information simultaneously. These are 1) the network, 2) the communities or annotations of interest, and 3) node categories or expression values. Finally, NORMA offers basic topological analysis and direct topological comparison across any of the selected networks. NORMA service is available at http://norma.pavlopouloslab.info, whereas the code is available at https://github.com/PavlopoulosLab/NORMA.

Altered resting-state functional networks in patients with premenstrual syndrome: a graph-theoretical based study

Premenstrual syndrome (PMS) is a menstrual cycle-related disorder. Previous studies have indicated alterations of brain functional connectivity in PMS patients. However, little is known about the overall organization of brain network in PMS patients. Functional magnetic resonance imaging data deriving from 20 PMS patients and 21 healthy controls (HCs). Pearson correlation between mean time-series was used to estimate connectivity matrix between each paired regions of interest, and the connectivity matrix for each participant was then binarized. Graph theory analysis was applied to assess each participant's global and local topological properties of brain functional network. Correlation analysis was performed to evaluate relationships between the daily rating of severity of problems (DRSP) and abnormal network properties. PMS patients had lower small-worldness values than HCs. PMS-related alterations of nodal properties were mainly found in the posterior cingulate cortex, precuneus and angular gyrus. The PMS-related abnormal connectivity components were mainly associated with the thalamus, putamen and middle cingulate cortex. In the PMS group, the DRSP score were negatively correlated with the area under the curves of nodal local efficiency in the posterior cingulate cortex. Our study suggests that the graph-theory method may be one potential tool to detect disruptions of brain connections and may provide important evidence for understanding the PMS from the disrupted network organization perspective.

Network topology analysis of essential genes interactome of Helicobacter pylori to explore novel therapeutic targets

The Helicobacter pylori chronic colonization produces a wide range of gastric diseases in the gastric mucosa by abetting inflammation. Amidst coevolution and reorganization of its metabolism with humans, it has become difficult still imperative to understand and prevent its growth. This study focus to explore functional insights into identification of hub proteins/genes by aggregating the behavior of genes connected in a protein-protein interaction (PPI) network. We have constructed a PPI network of 123 essential genes along with 1213 interactions in H. pylori 26695. The degree and other centrality measures analysis assist in identifying the important hub nodes, which are top-ranked proteins. A total of nine proteins (recA, guaA, dnaK, rpsB, rplQ, rpmA, rpmC, rpmF, and rpsE) were obtained with high degree (k), betweenness centrality (BC) value. Gene ontology analysis reveals 8, 5 and 3 GO terms correspond to biological processes, cellular components and molecular function respectively. Gene complexes of hypothetical proteins (HPs) were related to aminoacyl-tRNA biosynthesis, biosynthesis of secondary metabolites, bacterial secretion system and protein export. The MCODE analysis revealed that protein from module M1, M3 and M6 include the proteins which have highest degree and BC values. It is noteworthy to mention that the bifunctional GMP synthase/glutamine amidotransferase protein (guaA), molecular chaperon (dnaK), recombinase A (recA) constitute as hub proteins. As a result, these genes are considered as network hub nodes that might be used as therapeutic targets. Our analysis affords a detailed understanding of the molecular process and pathways regulated by the essential genes in H. pylori 26695.

Theoretical investigations on forward-backward ESIPT processes of three fluorophores deriving from 2-(2'-hydroxyphenyl)thiazole

The photophysical properties and excited-state intramolecular proton transfer (ESIPT) processes for 2-(2'-hydroxyphenyl)-4-chloromethylthiazole (1), 2-(2'-hydroxyphenyl)-4-phenylthiazole (2), 2-(2'-hydroxyphenyl)-4-hydroxymethyl-thiazole (3) were studied at the TD-B3PW91/6-31 + G(d, p)/IEFPCM level. The structures of 1-3 were fully optimized and the corresponding structural parameters, infrared spectra and electron densities in the ground (S₀) and the first excited (S₁) states were analyzed. The calculated absorption and fluorescence wavelengths of 1-3 reproduced the experimental data. The potential energy curves of the S₀ and S₁ states were built and the ESIPT processes were clarified. Our results showed that the intramolecular H-bonds of 3 and 2 in the S₁ state were the strongest and the weakest, respectively, and then the ESIPT potential barriers of 3 and 2 were the lowest and highest, respectively. Among the three phenol-thiazole type probes, the compound 2 with phenyl ring group at the 4 position of the thiazole ring had the larger π-conjugation, and had the higher ESIPT potential barrier at the same time. The corresponding compound 1 and 3 with CH₂Cl and CH₂OH had the lower ESIPT barrier.

Topological Analysis of Regulatory Networks Reveals Functionally Key Genes and miRNAs Involved in the Differentiation of Mesenchymal Stem Cells

Background

The details of molecular mechanisms underlying the differentiation of Mesenchymal Stem Cells (MSCs) into specific lineages are not well understood.

Objectives

We aimed to construct the interactome network and topology analysis of bone marrow mesenchymal stem cell of CAGE data. Applying the enrichment results, we wanted to introduce the common genes and hub-microRNA and hub-genes of these giant network.

Materials and Methods

In this study, we constructed gene regulatory networks for each non-mesenchymal cell lineage according to their gene expression profiles obtained from FANTOM5 database. The putative interactions of TF-gene and protein-protein were determined using TRED, STRING, HPRD and GeneMANIA servers. In parallel, a regulatory network including corresponding miRNAs and total differentially expressed genes (DEGs) was constructed for each cell lineage.

Results

The results indicated that analysis of networks' topology can significantly distinguish the hub regulatory genes and miRNAs involved in the differentiation of MSCs. The functional annotation of identified hub genes and miRNAs revealed that several signal transduction pathways i.e. AKT, WNT and TGFβ and cell proliferation related pathways play a pivotal role in the regulation of MSCs differentiation. We also classified cell lineages into two groups based on their predicted miRNA profiles.

Conclusions

In conclusion, we found a number of hub genes and miRNAs which seem to have key regulatory functions during differentiation of MSCs. Our results also introduce a number of new regulatory genes and miRNAs which can be considered as the new candidates for genetic manipulation of MSCs in vitro.

Substituent control of photophysical properties for excited-state intramolecular proton transfer (ESIPT) of o-LHBDI derivatives: a TD-DFT investigation

The substituted effect on excited-state intramolecular proton transfer (ESIPT) of o-LHBDI derivatives (4R-o-LHBDI) was investigated by DFT and TD-DFT methods. The structures of 4R-o-LHBDI (R: OH, NH₂, CN, NO₂, CF₃) were fully optimized, and the H-bond distances, bond angles, and infrared spectra of the atoms involved in PT process in the S₀ and S₁ states were analyzed. The absorption and fluorescence spectra were calculated, and the potential energy curves in both S₀ and S₁ states were constructed. Moreover, the effects of different substituents on the ESIPT mechanism of 4R-o-LHBDI (R: OH, NH₂, CN, NO₂, CF₃) were studied. The results indicate that ESIPT in the 4R-o-LHBDI is a little harder to proceed than that in o-LHBDI since the ESIPT barrier of 4R-o-LHBDI is slightly bigger than that value of o-LHBDI. When the substituent has stronger electron-withdrawing ability or weaker electron-donating ability, the ESIPT process has the smaller potential barrier. Graphical abstract.

Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls

The human gut microbiota plays a major role in maintaining human health and was recently recognized as a promising target for disease prevention and treatment. Many diseases are traceable to microbiota dysbiosis, implicating altered gut microbial ecosystems, or, in many cases, disrupted microbial enzymes carrying out essential physio-biochemical reactions. Thus, the changes of essential microbial enzyme levels may predict human disorders. With the rapid development of high-throughput sequencing technologies, metagenomics analysis has emerged as an important method to explore the microbial communities in the human body, as well as their functionalities. In this study, we analyzed 156 gut metagenomics samples from patients with colorectal cancer (CRC) and adenoma, as well as that from healthy controls. We estimated the abundance of microbial enzymes using the HMP Unified Metabolic Analysis Network method and identified the differentially abundant enzymes between CRCs and controls. We constructed enzymatic association networks using the extended local similarity analysis algorithm. We identified CRC-associated enzymic changes by analyzing the topological features of the enzymatic association networks, including the clustering coefficient, the betweenness centrality, and the closeness centrality of network nodes. The network topology of enzymatic association network exhibited a difference between the healthy and the CRC environments. The ABC (ATP binding cassette) transporter and small subunit ribosomal protein S19 enzymes, had the highest clustering coefficient in the healthy enzymatic networks. In contrast, the Adenosylhomocysteinase enzyme had the highest clustering coefficient in the CRC enzymatic networks. These enzymic and metabolic differences may serve as risk predictors for CRCs and are worthy of further research.

Probing the structures, electronic and bonding properties of multidecker lanthanides: Neutral and anionic Lnn(COT)m (Ln = Ce, Nd, Eu, Ho and Yb; n, m = 1, 2) complexes

The ground state structures of neutral and anionic Ln_n(COT)_m (Ln = Ce, Nd, Eu, Ho and Yb; n, m = 1, 2) complexes have been identified by density functional theory. Ln(COT)_1,2^0/- and Ln₂(COT)₂^0/- complexes are found to possess sandwich ground state structures in which Ln atoms and COT molecules are alternately stacked except for Nd₂COT₂^0/-. Our calculated AEA and VDE values show good agreement with the available experimental values, which validates that our obtained ground state structures are credible. Based on the frontier molecular orbitals, we find that the bond formation between the 4f electrons of Ln atoms and the π clouds of COT molecules is weak. Then, the bond strength within these complexes is further analyzed based on the topological analysis of electron density at bond critical point. By analyzing Hirshfeld charge, we find Ln_n(COT)_m^0/- are charge-transfer complexes with weak bonding feature.

The excited-state intramolecular proton transfer in NH-type dye molecules with a seven-membered-ring intramolecular hydrogen bond: A theoretical insight

Excited-state intramolecular proton transfer (ESIPT) reactions of a series of N(R)H⋯N-type seven-membered-ring hydrogen-bonding compounds were explored by employing density functional theory/time-dependent density functional theory calculations with the PBE0 functional. Our results indicate that the absorption and emission spectra predicted theoretically match very well the experimental findings. Additionally, as the electron-withdrawing strength of R increases, the intramolecular H-bond of the NS₁ form gradually enhances, and the forward energy barrier along the ESIPT reaction gradually decreases. For compound 4, its ESIPT reaction is found to be a barrierless process due to the involvement of a strong electron-withdrawing COCF₃ group. It is therefore a reasonable presumption that the ESIPT efficiency of these N(R)H⋯N-type seven-membered-ring H-bonding systems can be improved when a strong electron-withdrawing group in R is introduced.

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.

Characterization of B-H agostic compounds involved in the dehydrogenation of amine-boranes by group 4 metallocenes

For over a decade, amine-borane has been considered as a potential chemical hydrogen vector in the context of a search for cleaner energy sources. When catalyzed by organometallic complexes, the reaction mechanisms currently considered involve the formation of β-BH agostic intermediates. A thorough understanding of these intermediates may constitute a crucial step toward the identification of ideal catalysts. Topological approaches such as QTAIM and ELF revealed to be particularly suitable for the description of β-agostic interactions. When studying model catalysts, accurate theoretical calculations may be carried out. However, for a comparison with experimental data, calculations should also be carried out on large organo-metallic species, often including transition metals belonging to the second or the third row. In such a case, DFT methods are particularly attractive. Unfortunately, triple-ζ all electrons basis sets are not easily available for heavy transition metal elements. Thus, a subtle balance should be reached between the affordable level of calculations and the required accuracy of the electronic description of the systems. Herein we propose the use of B3LYP functional in combination with the LanL2DZ pseudopotential for the metal atom and 6-311++G(2d,2p) basis set for the other atoms, followed by a single point using the DKH2 relativistic Hamiltonian in combination with the B3LYP/DZP-DKH level, as a "minimum level of theory" leading to a consistent topological description of the interaction within the ELF and QTAIM framework, in the context of isolated (gas-phase) group 4 metallocene catalysts.

A parallel Homological Spanning Forest framework for 2D topological image analysis

In [14], a topologically consistent framework to support parallel topological analysis and recognition for 2D digital objects was introduced. Based on this theoretical work, we focus on the problem of finding efficient algorithmic solutions for topological interrogation of a 2D digital object of interest D of a presegmented digital image I, using 4-adjacency between pixels of D. In order to maximize the degree of parallelization of the topological processes, we use as many elementary unit processing as pixels the image I has. The mathematical model underlying this framework is an appropriate extension of the classical concept of abstract cell complex: a primal-dual abstract cell complex (pACC for short). This versatile data structure encompasses the notion of Homological Spanning Forest fostered in [14,15]. Starting from a symmetric pACC associated with I, the modus operandi is to construct via combinatorial operations another asymmetric one presenting the maximal number of non-null primal elementary interactions between the cells of D. The fundamental topological tools have been transformed so as to promote an efficient parallel implementation in any parallel-oriented architecture (GPUs, multi-threaded computers, SIMD kernels and so on). A software prototype modeling such a parallel framework is built.

Topological characteristics of target genes regulated by abiotic-stress-responsible miRNAs in a rice interactome network

A great number of microRNAs (miRNAs) have been identified in responding and acting in gene regulatory networks associated with plant tolerance to abiotic stress conditions, such as drought, salinity, and high temperature. The topological exploration of target genes regulated by abiotic-stress-responsible miRNAs (ASRmiRs) in a network facilitates to discover the molecular basis of plant abiotic stress response. This study was based on the staple food rice (Oryza sativa) in which ASRmiRs were manually curated. After having compared the topological properties of target genes (stress-miR-targets) with those (non-stress-miR-targets) not regulated by ASRmiRs in a rice interactome network, we found that stress-miR-targets exhibited distinguishable topological properties. The interaction probability analysis and k-core decomposition showed that stress-miR-targets preferentially interacted with non-stress-miR-targets and located at the peripheral positions in the network. Our results indicated an obvious topological distinction between the two types of genes, reflecting the specific mechanisms of action of stress-miR-targets in rice abiotic stress response. Also, the results may provide valuable clues to elucidate molecular mechanisms of crop response to abiotic stress.

Differences in the known cellular composition of benign pigmented skin lesions reflected in computer-aided image analysis

BACKGROUND

Computer-aided image analysis (CAIA) has been suggested as an effective diagnostic tool for pigmented skin lesions (PSLs), especially melanoma. However, few studies on benign PSLs have been reported.

OBJECTIVE

The purpose of this study was to evaluate benign PSLs with our CAIA software and analyze the differences between the parameters of those lesions.

METHODS

By using homegrown CAIA software, we analyzed 3 kinds of PSLs-nevus, lentigo, and seborrheic keratosis. The group of seborrheic keratosis was divided into pigmented seborrheic keratosis, sebolentigine, and hyperkeratotic seborrheic keratosis. The CAIA was used to extract the color, as well as the morphological, textural, and topological features from each image.

RESULTS

In line with clinical observations, the objective parameters indicated that nevus was dark and round, lentigo was small and bright, and seborrheic keratosis was large and spiky. The surface of nevus showed the highest contrast and correlation. In topological analysis, the concentricity clearly separated melanocytic lesions from seborrheic keratosis. The parameters of pigmented seborrheic keratosis were between those of typical nevus and seborrheic keratosis.

CONCLUSION

We confirmed that definite correlations exist between the subjective differentiation by experts' examination and the objective evaluation by using CAIA. We also found that the morphological differences observed in CAIA were greatly influenced by the composition ratios of keratinocytes and melanocytes, which are already known histopathological characteristics of each PSL.

Metabolic cancer biology: structural-based analysis of cancer as a metabolic disease, new sights and opportunities for disease treatment

The cancer cell metabolism or the Warburg effect discovery goes back to 1924 when, for the first time Otto Warburg observed, in contrast to the normal cells, cancer cells have different metabolism. With the initiation of high throughput technologies and computational systems biology, cancer cell metabolism renaissances and many attempts were performed to revise the Warburg effect. The development of experimental and analytical tools which generate high-throughput biological data including lots of information could lead to application of computational models in biological discovery and clinical medicine especially for cancer. Due to the recent availability of tissue-specific reconstructed models, new opportunities in studying metabolic alteration in various kinds of cancers open up. Structural approaches at genome-scale levels seem to be suitable for developing diagnostic and prognostic molecular signatures, as well as in identifying new drug targets. In this review, we have considered these recent advances in structural-based analysis of cancer as a metabolic disease view. Two different structural approaches have been described here: topological and constraint-based methods. The ultimate goal of this type of systems analysis is not only the discovery of novel drug targets but also the development of new systems-based therapy strategies.