Altered whole-brain functional network in patients with frontal low-grade gliomas: a resting-state functional MRI study

PURPOSE

Gliomas are the most common primary brain tumor. Currently, topological alterations of whole-brain functional network caused by gliomas are not fully understood. The work here clarified the topological reorganization of the functional network in patients with unilateral frontal low-grade gliomas (LGGs).

METHODS

A total of 45 patients with left frontal LGGs, 19 with right frontal LGGs, and 25 healthy controls (HCs) were enrolled. All the resting-state functional MRI (rs-fMRI) images of the subjects were preprocessed to construct the functional network matrix, which was used for graph theoretical analysis. A two-sample t-test was conducted to clarify the differences in global and nodal network metrics between patients and HCs. A network-based statistic approach was used to identify the altered specific pairs of regions in which functional connectivity in patients with LGGs.

RESULTS

The local efficiency, clustering coefficient, characteristic path length, and normalized characteristic path length of patients with unilateral frontal LGGs were significantly lower than HCs, while there were no significant differences of global efficiency and small-worldness between patients and HCs. Compared with the HCs, betweenness centrality, degree centrality, and nodal efficiency of several brain nodes were changed significantly in patients. Around the tumor and its adjacent areas, the inter- and intra-hemispheric connections were significantly decreased in patients with left frontal LGGs.

CONCLUSION

The patients with unilateral frontal LGGs have altered global and nodal network metrics and decreased inter- and intra-hemispheric connectivity. These topological alterations may be involved in functional impairment and compensation of patients.

Topological features of functional brain networks and subclinical impulsivity: an investigation in younger and older adults

Impulsive traits (i.e., the tendency to act without forethought regardless of negative outcomes) are frequently found in healthy populations. When exposed to risk factors, individuals may develop debilitating disorders of impulse control (addiction, substance abuse, gambling) characterized by behavioral and cognitive deficits, eventually leading to huge socioeconomic costs. With the far-reaching aim of preventing the onset of impulsive disorders, it is relevant to investigate the topological organization of functional brain networks associated with impulsivity in sub-clinical populations. Taking advantage of the open-source LEMON dataset, we investigated the topological features of resting-state functional brain networks associated with impulsivity in younger (n = 146, age: 20-35) and older (n = 61, age: 59-77) individuals, using a graph-theoretical approach. Specifically, we computed indices of segregation and integration at the level of specific circuits and nodes known to be involved in impulsivity (frontal, limbic, and striatal networks). In younger individuals, results revealed that impulsivity was associated with a more widespread, less clustered and less efficient functional organization, at all levels of analyses and in all selected networks. Conversely, impulsivity in older individuals was associated with reduced integration and increased segregation of striatal regions. Speculatively, such alterations of functional brain networks might underlie behavioral and cognitive abnormalities associated with impulsivity, a working hypothesis worth being tested in future research. Lastly, differences between younger and older individuals might reflect the implementation of age-specific adaptive strategies, possibly accounting for observed differences in behavioral manifestations. Potential interpretations, limitations and implications are discussed.

Sleep deprivation changes frequency-specific functional organization of the resting human brain

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.01-0.08 Hz, interval = 0.01 Hz) caused by SD. First, we conducted a multivariate pattern analysis combining linear SVM classifiers with a robust feature selection algorithm, and the results revealed that accuracies of 74.29%-84.29% could be achieved in the classification between RW and SD states in leave-one-out cross-validation at different frequency bands, moreover, the spectral connection at the lowest and highest frequency bands exhibited higher discriminative power. Connection involving the cingulo-opercular network increased most, while connection involving the default-mode network decreased most following SD. Then we performed a graph-theoretic analysis and observed reduced low-frequency modularity and high-frequency global efficiency in the SD state. Moreover, hub regions, which were primarily situated in the cerebellum and the cingulo-opercular network after SD, exhibited high discriminative power in the aforementioned classification consistently. The findings may indicate the frequency-dependent effects of SD on the functional network topology and its efficiency of information exchange, providing new insights into the impact of SD on the human brain.

Differences of individual gray matter networks between MCI patients who converted to AD within 3 Years and nonconverters

Objective

Here we aimed to explore the differences in individual gray matter (GM) networks at baseline in mild cognitive impairment patients who converted to Alzheimer's disease (AD) within 3 years (MCI-C) and nonconverters (MCI-NC).

Materials and methods

Data from 461 MCI patients (180 MCI-C and 281 MCI-NC) were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). For each subject, a GM network was constructed using 3D-T1 imaging and the Kullback-Leibler divergence method. Gradient and topological analyses of individual GM networks were performed, and partial correlations were calculated to evaluate relationships among network properties, cognitive function, and apolipoprotein E (APOE) €4 alleles. Subsequently, a support vector machine (SVM) model was constructed to discriminate the MCI-C and MCI-NC patients at baseline.

Results

The gradient analysis revealed that the principal gradient score distribution was more compressed in the MCI-C group than in the MCI-NC group, with scores for the left lingual gyrus, right fusiform gyrus and left middle temporal gyrus being increased in the MCI-C group (p < 0.05, FDR corrected). The topological analysis showed significant differences in nodal efficiency in four nodes between the two groups. Furthermore, the regional gradient scores or nodal efficiency were found to be significantly related to the neuropsychological test scores, and the left middle temporal gyrus gradient scores were positively associated with the number of APOE €4 alleles (r = 0.192, p = 0.002). Ultimately, the SVM model achieved a balanced accuracy of 79.4% in classifying MCI-C and MCI-NC patients (p < 0.001).

Conclusion

The whole-brain GM network hierarchy in the MCI-C group was more compressed than that in the MCI-NC group, suggesting more serious cognitive impairments in the MCI-C group. The left middle temporal gyrus gradient scores were related to both cognitive function and APOE €4 alleles, thus serving as potential biomarkers distinguishing MCI-C from MCI-NC at baseline.

Synthesis, solvent role, absorption and emission studies of cytosine derivative

The (E)-4-((4-hydroxy-3-methoxy-5-nitrobenzylidene) amino) pyrimidin-2(1H)-one (C5NV) was synthesized from cytosine and 5-nitrovanilline by simple straightforward condensation reaction. The structural characteristics of the compound was determined and optimized by WB97XD/cc-pVDZ basis set. The vibrational frequencies were computed and subsequently compared to the experimental frequencies. We investiated the electronic properties of the synthesized compound in gas and solvent phases using the time-dependent density functional theory (TD-DFT) approach, and compared them to experimental values. The fluorescence study showed three different wavelengths indicating the nature of the optical material properties. Frontier molecular orbital (FMO) and molecular electrostatic potential (MEP) analyses were conducted for the title compound, and electron localized functions (ELF) and localized orbital locators (LOL) were used to identify the orbital positions of localized and delocalized atoms. Non-covalent interactions (H-bond interactions) were investigated using reduced density gradients (RDGs). The objective of the study was to determine the physical, chemical, and biological properties of the C5NV. The molecular docking study was conducted between C5NV and 2XNF protein, its lowest binding energy score is -7.92 kcal/mol.

Experimental and computational approaches for membrane protein insertion and topology determination

Membrane proteins play pivotal roles in a wide array of cellular processes and constitute approximately a quarter of the protein-coding genes across all organisms. Despite their ubiquity and biological significance, our understanding of these proteins remains notably less comprehensive compared to their soluble counterparts. This disparity in knowledge can be attributed, in part, to the inherent challenges associated with employing specialized techniques for the investigation of membrane protein insertion and topology. This review will center on a discussion of molecular biology methodologies and computational prediction tools designed to elucidate the insertion and topology of helical membrane proteins.

Sortase-Catalyzed Protein Domain Inversion

Topological transformations and permutations of proteins have attracted significant interest as strategies to generate new protein functionalities or stability. These efforts have mainly been inspired by naturally occurring post-translational modifications, such as head-to-tail cyclization, circular permutation, or lasso-like entanglement. Such approaches can be realized experimentally via genetic encoding, in the case of circular permutation, or via enzymatic processing, in the case of cyclization. Notably, these previously described strategies leave the polypeptide backbone orientation unaltered. Here we describe an unnatural protein permutation, the protein domain inversion, whereby a C-terminal portion of a protein is enzymatically inverted from the canonical N-to-C to a C-to-C configuration with respect to the N-terminal part of the protein. The closest conceptually analogous biological process is perhaps the inversion of DNA segments as catalyzed by recombinases. We achieve these inversions using an engineered sortase A, a widely used transpeptidase. Our reactions proceed efficiently under mild conditions at 4-25 °C and are compatible with entirely heterologously-produced protein substrates.

In situ characterization of heterogeneous surface wetting in porous materials

The performance of nano- and micro-porous materials in capturing and releasing fluids, such as during CO2 geo-storage and water/gas removal in fuel cells and electrolyzers, is determined by their wettability in contact with the solid. However, accurately characterizing wettability is challenging due to spatial variations in dynamic forces, chemical heterogeneity, and surface roughness. In situ measurements can potentially measure wettability locally as a contact angle - the angle a denser phase (e.g water) contacts solid in the presence of a second phase (e.g. hydrogen, air, CO2) - but suffer from difficulties in accurately capturing curvatures, contact areas, and contact loops of multiphase fluids. We introduce a novel extended topological method for in situ contact angle measurement and provide a comparative review of current geometric and topological methods, assessing their accuracy on ideal surfaces, porous rocks containing CO2, and water in gas diffusion layers. The new method demonstrates higher accuracy and reliability of in situ measurements for uniformly wetting systems compared to previous topological approaches, while geometric measurements perform best for mixed-wetting domains. This study further provides a comprehensive open-source platform for in situ characterization of wettability in porous materials with implications for gas geo-storage, fuel cells and electrolyzers, filtration, and catalysis.

Early life stress and functional network topology in children

Brain networks are continuously modified throughout development, yet this plasticity can also make functional networks vulnerable to early life stress. Little is currently known about the effect of early life stress on the functional organization of the brain. The current study investigated the association between environmental stressors and network topology using data from the Adolescent Brain Cognitive DevelopmentSM (ABCD®) Study. Hierarchical modeling identified a general factor of environmental stress, representing the common variance across multiple stressors, as well as four subfactors including familial dynamics, interpersonal support, neighborhood SES deprivation, and urbanicity. Functional network topology metrics were obtained using graph theory at rest and during tasks of reward processing, inhibition, and affective working memory. The general factor of environmental stress was associated with less specialization of networks, represented by lower modularity at rest. Local metrics indicated that general environmental stress was also associated with less efficiency in the subcortical-cerebellar and visual networks while showing greater efficiency in the default mode network at rest. Subfactors of environmental stress were associated with differences in specialization and efficiency in select networks. The current study illustrates that a wide range of stressors in a child's environment are associated with differences in brain network topology.

Reorganization of structural brain networks in Parkinson's disease with postural instability/gait difficulty

The Postural Instability/Gait Difficulty (PIGD) subtype of Parkinson's disease (PD) has a faster disease progression, a higher risk of cognitive and motor decline, yet the alterations of structural topological organization remain unknown. Diffusion Tensor Imaging (DTI) and 3D-TI scanning were conducted on 31 PD patients with PIGD (PD-PIGD), 30 PD patients without PIGD (PD-non-PIGD) and 35 Healthy Controls (HCs). Structural networks were constructed using DTI brain white matter fiber tractography. A graph theory approach was applied to characterize the topological properties of complex structural networks, and the relationships between significantly different network metrics and motor deficits were analyzed within the PD-PIGD group. PD-PIGD patients exhibited increased shortest path length compared with PD-non-PIGD and HCs (P < 0.05, respectively). Additionally, PD-PIGD patients exhibited decreased nodal properties, mainly in the cerebellar vermis, prefrontal cortex, paracentral lobule, and visual regions. Notably, the degree centrality of the cerebellar vermis was negatively correlated with the PIGD score (r = -0.390; P = 0.030) and Unified Parkinson's Disease Rating Scale Part III score (r = -0.436; P = 0.014) in PD-PIGD patients. Furthermore, network-based statistical analysis revealed decreased structural connectivity between the prefrontal lobe, putamen, supplementary motor area, insula, and cingulate gyrus in PD-PIGD patients. Our findings demonstrated that PD-PIGD patients existed abnormal structural connectomes in the cerebellar vermis, frontal-parietal cortex and visual regions. These topological differences can provide a topological perspective for understanding the potential pathophysiological mechanisms of PIGD in PD.

δ-Bonding and Spin-Orbit Coupling Make SrAg4Sb2 a Topological Insulator

Bonding interactions and spin-orbit coupling in the topological insulator SrAg4Sb2 are investigated using DFT with orbital projection analysis. Ag-Ag delta bonding is a key ingredient in the topological insulating state because the4 d x y + 4 d x 2 - y 2 ${4d_{xy} + 4d_{x^2 - y^2 } }$ delta antibonding band forms a band inversion with the 5 s sigma bonding band. Spin-orbit coupling is required to lift d orbital degeneracies and lower the antibonding band enough to create the band inversion. These bonding effects are enabled by a longer-than-covalent Ag-Ag distance in the crystal lattice, which might be a structural characteristic of other transition metal based topological insulators. A simplified model of the topological bands is constructed to capture the essence of the topological insulating state in a way that may be engineered in other materials.

1D Covalent Organic Frameworks Triggering Highly Efficient Photosynthesis of H2 O2 via Controllable Modular Design

The topological diversity of covalent organic frameworks (COFs) enables considerable space for exploring their structure-performance relationships. In this study, we report a sequence of novel 1D COFs (EO, ES, and ESe-COF) with typical 4-c sql topology that can be interconnected with VIA group elements (O, S, and Se) via a modular design strategy. It is found that the electronic structures, charge delivery property, light harvesting ability, and hydrophilicity of these 1D COFs can be profoundly influenced by the bridge-linked atom ordinal. Finally, EO-COF, possessing the highest quantity of active sites, the longest lifetime of the active electron, the strongest interaction with O2 , and the lowest energy barrier of O2 reduction, exhibits exceptional photocatalytic O2 -to-H2 O2 activity under visible light, with a production rate of 2675 μmol g-1  h-1 and a high apparent quantum yield of 6.57 % at 450 nm. This is the first systematic report on 1D COFs for H2 O2 photosynthesis, which enriches the topological database in reticular chemistry and promotes the exploration of structure-catalysis correlation.

Pentazolate Anion: A Rare and Preferred Five-Membered Ligand for Constructing Pentasil-Zeolite Topology Architectures

As the fourth full-nitrogen structure, the pentazolate anion (cyclo-N5 - ) was highly coveted for decades. In 2017, the first air-stable non-metal pentazolate salt, (N5 )6 (H3 O)3 (NH4 )4 Cl, was obtained, representing a milestone in this field. As the latest member of the azole family, cyclo-N5 - is comprised of five nitrogen atoms. Although significant attention has been paid to the potential of cyclo-N5 - as an energetic material, its poor thermostability hinders any practical application. However, the unique ring structure and multiple coordination capability of cyclo-N5 - provide a platform for the fabrication of various structures, among which pentasil-zeolite topologies are the most intriguing. In addition, the introduction of structure-directing auxiliaries enables the self-assembly of diverse topological architectures, potentially imparting cyclo-N5 - with the potential to impact wide-ranging areas of coordination chemistry and topology. In this minireview, different pentasil-zeolite topologies based on metal-pentazolate frameworks are evaluated. To date, three zeolitic and zeolite-like topologies have been reported, namely the melanophlogite (MEP), chibaite (MTN), and unj topologies. The MEP topology consists of two nanocages, Na20 N60 and Na24 N60 , whereas the MTN topology contains Na20 N60 and Na28 N80 nanocages. Furthermore, the unj topology features multiple homochiral channels consisting of two helical chains. Various possible strategies for obtaining additional pentasil-zeolite topologies are also discussed.

Exploring the folding landscape of leptin: Insights into threading pathways

The discovery of new protein topologies with entanglements and loop-crossings have shown the impact of local amino acid arrangement and global three-dimensional structures. This phenomenon plays a crucial role in understanding how protein structure relates to folding and function, affecting the global stability, and biological activity. Protein entanglements encompassing knots and non-trivial topologies add complexity to their folding free energy landscapes. However, the initial native contacts driving the threading event for entangled proteins remains elusive. The Pierced Lasso Topology (PLT) represents an entangled topology where a covalent linker creates a loop in which the polypeptide backbone is threaded through. Compared to true knotted topologies, PLTs are simpler topologies where the covalent-loop persists in all conformations. In this work, the PLT protein leptin, is used to visualize and differentiate the preference for slipknotting over plugging transition pathways along the folding route. We utilize the Energy Landscape Visualization Method (ELViM), a multidimensional projection technique, to visualize and distinguish early threaded conformations that cannot be observed in an in vitro experiment. Critical contacts for the leptin threading mechanisms were identified where the competing pathways are determined by the formation of a hairpin loop in the unfolded basin. Thus, prohibiting the dominant slipknotting pathway. Furthermore, ELViM offers insights into distinct folding pathways associated with slipknotting and plugging providing a novel tool for de novo design and in vitro experiments with residue specific information of threading events in silico.

Synthesis, spectroscopic, chemical reactivity, topology analysis and molecular docking study of ethyl 5-hydroxy-2-thioxo-4-(p-tolyl)-6-(trifluoromethyl)hexahydropyrimidine-5-carboxylate

The organofluorine hexahydropyrimidine derivatives are used in the drug discovery due to its steric nature to hydrogen and its extreme electronegativity. The Ethyl 5-hydroxy-2-thioxo-4-(p-tolyl)-6-(trifluoromethyl)hexahydropyrimidine-5-carboxylate (ETP5C) compound was synthesized and characterized by NMR (13C and 1H), FT-IR and UV-Vis spectroscopic techniques for experimentally and theoretically and elemental analyses, mass spectra also investigated. The most stable structure of synthesized molecule was studied by PES analysis in gas and liquid medium. The structural parameters such as bond length and bond angle of the title molecule have been obtained by DFT/B3LYP/6-311++G (d,p) set and compared with the structurally related experimental data of the compounds. The π-to-π* transition of the ETP5C molecule is identified using UV-Vis absorption spectral analysis. In addition, the chemical stability and reactivity are investigated using HOMO-LUMO analysis. The minimal HOMO-LUMO energy gap (4.6255 eV) clearly explains that the ETP5C molecule is more reactive for receptors. The nucleophilic and electrophilic regions such as active sites have been shown by MEP, ELF, LOL and Fukui functions. The second order optical effect has been explained by NLO analysis. The docking was performed with antineoplastic proteins that exhibit against the development of tumor cells.

High-pressure induces topology boosting thermoelectric performance of Bi2Te3

Decoupling conductivity (σ) and Seebeck coefficient (S) by electronic topological transitions (ETT) under high pressure (2-4 GPa) is a promising method for Bi2Te3 to optimize thermoelectric (TE) performance. However, the S cannot dramatically increase with increasing σ when ETT occurs in Bi2Te3, which impedes optimizing TE performance by utilizing ETT in Bi2Te3. A new strategy of enhanced ETT by combining lattice distortions and high pressure is proposed in this work. The lattice distortions in Bi2Te3 were introduced by high pressure and high temperature (HPHT) treatment to generate surplus dislocations. The in-situ measurements of σ and S at HPHT in Bi2Te3 with lattice distortions show an enhanced ETT effect at 2 GPa, which causes decouple σ and S with an anomalous increase in its |S| about 22 %. The ETT effect causes the figure of merit (ZT) of Bi2Te3 can be improved to 0.275 at 1.50-2.62 GPa, 460 K, it is more than 62 % compared with 0.79 GPa, at 450 K. The excellent TE performance of Bi2Te3 arising from the lattice distortions can result in local non-hydrostatic pressure which enhances ETT under high pressure. This work provides a new strategy to enhance ETT to decouple σ and S, and search for better TE materials from the pressure dimension in the future.

Representing and extracting knowledge from single-cell data

Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data.

Exploring Uniform, Dual, and Dynamic Topologies of Membrane Proteins by Substituted Cysteine Accessibility Method (SCAM™)

A described simple and advanced protocol for Substituted Cysteine Accessibility Method as applied to transmembrane (TM) orientation (SCAM™) permits a topology analysis of proteins in their native state and can be universally adapted to any membrane system to either systematically map an uniform or identify and quantify the degree of mixed topology or establish transmembrane assembly dynamics from relatively static experimental data such as endpoint topologies of membrane proteins. In this approach, noncritical individual amino acids that are thought to reside in the putative extracellular or intracellular loops of a membrane protein are replaced one at the time by cysteine residue, and the orientation with respect to the membrane is evaluated by using a pair of membrane-impermeable non-detectable and detectable thiol-reactive labeling reagents. For the most water-exposed cysteine residues in proteins, the thiol pKa lies in the range of 8-9, and formation of cysteinyl thiolate ions is optimum in aqueous rather in a nonpolar environment. These features and the ease of specific chemical modification with thiol reagents are central to SCAM™. Membrane side-specific sulfhydryl labeling allows to discriminate "exposed, protected or dynamic" cysteines strategically "implanted" at desired positions throughout cysteine less target protein template. The strategy described is widely used to map the topology of membrane protein and establish its transmembrane dynamics in intact cells of both diderm (two-membraned) Gram-negative and monoderm (one-membraned) Gram-positive bacteria, cell-derived oriented membrane vesicles, and proteoliposomes.

Probing Protein Topology and Conformation by Limited Proteolysis

Proteases are enzymes that catalyze the hydrolytic degradation of other proteins into peptides or amino acids through the digestion of the peptide bond. Promiscuous proteases that target a wide range of proteins are distinguished from specific proteases that have a narrow range of substrates. In terms of activity, endoproteases cleave their substrates at specific residues within the target proteins, whereas exoproteases cleave from one extremity and may have processive activities. Proteases are therefore very useful tools to study proteins, notably their structure or conformation. In addition, proteases can be used to probe the topology of bacterial membrane proteins. Here, we describe limited protease accessibility assays to define inner membrane protein topology and conformational changes based on digestion profiles.

Surface topologized ovalbumin scaffolds containing YIGSR peptides for modulating Schwann cell behavior

Peripheral nerve injuries (PNI) currently have limited therapeutic efficacy, and functional scaffolds have been shown to be effective for treating PNI. Ovalbumin (OVA) is widely used as a natural biomaterial for repairing damaged tissues due to its excellent biocompatibility and the presence of various bioactive components. However, there are few reports on the repair of PNI by ovalbumin. In this study, a novel bionic functionalized topological scaffold based on ovalbumin and grafted with tyrosine-isoleucine-glycine-serine-arginine (YIGSR) peptide was constructed by micro-molding method and surface-biomodification technology. The scaffolds were subjected to a series of evaluations in terms of morphology, mechanics, hydrophilicity, and biocompatibility, and the related molecular mechanisms were further penetrated. The results showed that the scaffolds prepared in this study had aligned ridge/groove structure, good mechanical properties and biocompatibility, and could be used as carriers to slowly release YIGSR, which effectively promoted the proliferation, migration and elongation of Schwann Cells (SCs), and significantly up-regulated the gene expression related to proliferation, apoptosis, migration and axon regeneration. Therefore, the bionic functional topological scaffold has significant application potential for promoting peripheral nerve regeneration and provides a new therapeutic option for repairing PNI.

Rational Design and Reticulation of Infinite qbe Rod Secondary Building Units into Metal-Organic Frameworks through a Global Desymmetrization Approach for Inverse C3 H8 /C3 H6 Separation

The development of reticular chemistry has enabled the construction of a large array of metal-organic frameworks (MOFs) with diverse net topologies and functions. However, dominating this class of materials are those built from discrete/finite secondary building units (SBUs), yet the designed synthesis of frameworks involving infinite rod-shaped SBUs remain underdeveloped. Here, by virtue of a global linker desymmetrization approach, we successfully targeted a novel Cu-MOF (Cu-ASY) incorporating infinite Cu-carboxylate rod SBUs with its structure determined by micro electron diffraction (MicroED) crystallography. Interestingly, the rod SBU can be simplified as a unique cylindric sphere packing qbe tubule made of [43 .62 ] tiles, which further connect the tritopic linkers to give a newly discovered 3,5-connected gfc net. Cu-ASY is a permanent ultramicroporous material featuring 1D channels with highly inert surfaces and shows a preferential adsorption of propane (C3 H8 ) over propene (C3 H6 ). The efficiency of C3 H8 selective Cu-ASY is validated by multicycle breakthrough experiments, giving C3 H6 productivity of 2.2 L/kg. Density functional theory (DFT) calculations reveal that C3 H8 molecules form multiple C-H⋅⋅⋅π and atypical C-H⋅⋅⋅ H-C van der Waals interactions with the inner nonpolar surfaces. This work therefore highlights the linker desymmetrization as an encouraging and intriguing strategy for achieving unique MOF structures and properties.

Magnetic resonance-based imaging biopsy with signatures including topological Betti number features for prediction of primary brain metastatic sites

This study incorporated topology Betti number (BN) features into the prediction of primary sites of brain metastases and the construction of magnetic resonance-based imaging biopsy (MRB) models. The significant features of the MRB model were selected from those obtained from gray-scale and three-dimensional wavelet-filtered images, BN and inverted BN (iBN) maps, and clinical variables (age and gender). The primary sites were predicted as either lung cancer or other cancers using MRB models, which were built using seven machine learning methods with significant features chosen by three feature selection methods followed by a combination strategy. Our study dealt with a dataset with relatively smaller brain metastases, which included effective diameters greater than 2 mm, with metastases ranging from 2 to 9 mm accounting for 17% of the dataset. The MRB models were trained by T1-weighted contrast-enhanced images of 494 metastases chosen from 247 patients and applied to 115 metastases from 62 test patients. The most feasible model attained an area under the receiver operating characteristic curve (AUC) of 0.763 for the test patients when using a signature including features of BN and iBN maps, gray-scale and wavelet-filtered images, and clinical variables. The AUCs of the model were 0.744 for non-small cell lung cancer and 0.861 for small cell lung cancer. The results suggest that the BN signature boosted the performance of MRB for the identification of primary sites of brain metastases including small tumors.

Enhancing the interpretation of genetic observations in KCNQ1 in unselected populations: relevance to secondary findings

AIMS

Rare variants in the KCNQ1 gene are found in the healthy population to a much greater extent than the prevalence of Long QT Syndrome type 1 (LQTS1). This observation creates challenges in the interpretation of KCNQ1 rare variants that may be identified as secondary findings in whole exome sequencing.This study sought to identify missense variants within sub-domains of the KCNQ1-encoded Kv7.1 potassium channel that would be highly predictive of disease in the context of secondary findings.

METHODS AND RESULTS

We established a set of KCNQ1 variants reported in over 3700 patients with diagnosed or suspected LQTS sent for clinical genetic testing and compared the domain-specific location of identified variants to those observed in an unselected population of 140 000 individuals. We identified three regions that showed a significant enrichment of KCNQ1 variants associated with LQTS at an odds ratio (OR) >2: the pore region, and the adjacent 5th (S5) and 6th (S6) transmembrane (TM) regions. An additional segment within the carboxyl terminus of Kv7.1, conserved region 2 (CR2), also showed an increased OR of disease association. Furthermore, the TM spanning S5-Pore-S6 region correlated with a significant increase in cardiac events.

CONCLUSION

Rare missense variants with a clear phenotype of LQTS have a high likelihood to be present within the pore and adjacent TM segments (S5-Pore-S6) and a greater tendency to be present within CR2. This data will enhance interpretation of secondary findings within the KCNQ1 gene. Further, our data support a more severe phenotype in LQTS patients with variants within the S5-Pore-S6 region.

On soft parametric somewhat-open sets and applications via soft topologies

In this work, we adopt a new approach to study a new class of soft sets depending on the generalizations of open subsets in the parametric topological spaces. We first define the class of soft parametric somewhat-open sets and explore its basic features. We illustrate this class represents a proper extension of soft open and soft somewhat-open sets under a full soft topology. We derive the next formula1 + ∏ η ∈ H ( | Θ η | - 1 ) ≤ | Ϝ | ≤ 1 + ( 2 | U | - 1 ) | H | , which determines the lower and upper bounds of the cardinality number Ϝ of the family of soft parametric somewhat-open subsets of a soft topological space ( U , Θ , H ) , where Θ η is a parametric topology inspired by Θ. Then, we introduce two novel kinds of soft compact and Lindelöf spaces inspired by the class of soft parametric somewhat-open sets and explain the relations between them with the aid of some counterexamples. We also examine the navigation of these spaces between soft and parametric (classical) structures and supply the necessary conditions that guarantee some directions. In the end, we introduce the concept of soft ps-connected spaces and give some of its equivalent descriptions. Furthermore, we prove the identity between this concept and soft hyperconnected spaces and show that the existence of a somewhat connected (parametric) space is used to confirm the possession of a soft ps-connected property.

K-Nearest-Neighbors Induced Topological PCA for Single Cell RNA-Sequence Data Analysis

Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells, which has given us insights into cell-cell communication, cell differentiation, and differential gene expression. However, analyzing scRNA-seq data is a challenge due to sparsity and the large number of genes involved. Therefore, dimensionality reduction and feature selection are important for removing spurious signals and enhancing downstream analysis. Traditional PCA, a main workhorse in dimensionality reduction, lacks the ability to capture geometrical structure information embedded in the data, and previous graph Laplacian regularizations are limited by the analysis of only a single scale. We propose a topological Principal Components Analysis (tPCA) method by the combination of persistent Laplacian (PL) technique and L2,1 norm regularization to address multiscale and multiclass heterogeneity issues in data. We further introduce a k-Nearest-Neighbor (kNN) persistent Laplacian technique to improve the robustness of our persistent Laplacian method. The proposed kNN-PL is a new algebraic topology technique which addresses the many limitations of the traditional persistent homology. Rather than inducing filtration via the varying of a distance threshold, we introduced kNN-tPCA, where filtrations are achieved by varying the number of neighbors in a kNN network at each step, and find that this framework has significant implications for hyper-parameter tuning. We validate the efficacy of our proposed tPCA and kNN-tPCA methods on 11 diverse benchmark scRNA-seq datasets, and showcase that our methods outperform other unsupervised PCA enhancements from the literature, as well as popular Uniform Manifold Approximation (UMAP), t-Distributed Stochastic Neighbor Embedding (tSNE), and Projection Non-Negative Matrix Factorization (NMF) by significant margins. For example, tPCA provides up to 628%, 78%, and 149% improvements to UMAP, tSNE, and NMF, respectively on classification in the F1 metric, and kNN-tPCA offers 53%, 63%, and 32% improvements to UMAP, tSNE, and NMF, respectively on clustering in the ARI metric.

Topology-based radiomic features for prediction of parotid gland cancer malignancy grade in magnetic resonance images

PURPOSE

The malignancy grades of parotid gland cancer (PGC) have been assessed for a decision of treatment policies. Therefore, we have investigated the feasibility of topology-based radiomic features for the prediction of parotid gland cancer (PGC) malignancy grade in magnetic resonance (MR) images.

MATERIALS AND METHODS

Two-dimensional T1- and T2-weighted MR images of 39 patients with PGC were selected for this study. Imaging properties of PGC can be quantified using the topology, which could be useful for assessing the number of the k-dimensional holes or heterogeneity in PGC regions using invariants of the Betti numbers. Radiomic signatures were constructed from 41,472 features obtained after a harmonization using an elastic net model. PGC patients were stratified using a logistic classification into low/intermediate- and high-grade malignancy groups. The training data were increased by four times to avoid the overfitting problem using a synthetic minority oversampling technique. The proposed approach was assessed using a 4-fold cross-validation test.

RESULTS

The highest accuracy of the proposed approach was 0.975 for the validation cases, whereas that of the conventional approach was 0.694.

CONCLUSION

This study indicated that topology-based radiomic features could be feasible for the noninvasive prediction of the malignancy grade of PGCs.

Topology-sensitive weighting model for myocardial segmentation

Accurate myocardial segmentation is crucial for the diagnosis of various heart diseases. However, segmentation results often suffer from topology structural errors, such as broken connections and holes, especially in cases of poor image quality. These errors are unacceptable in clinical diagnosis. We proposed a Topology-Sensitive Weight (TSW) model to keep both pixel-wise accuracy and topological correctness. Specifically, the Position Weighting Update (PWU) strategy with the Boundary-Sensitive Topology (BST) module can guide the model to focus on positions where topological features are sensitive to pixel values. The Myocardial Integrity Topology (MIT) module can serve as a guide for maintaining myocardial integrity. We evaluate the TSW model on the CAMUS dataset and a private echocardiography myocardial segmentation dataset. The qualitative and quantitative experimental results show that the TSW model significantly enhances topological accuracy while maintaining pixel-wise precision.

Human Cancer Cell Radiation Response Investigated through Topological Analysis of 2D Cell Networks

Clonogenic assays are routinely used to evaluate the response of cancer cells to external radiation fields, assess their radioresistance and radiosensitivity, estimate the performance of radiotherapy. However, classic clonogenic tests focus on the number of colonies forming on a substrate upon exposure to ionizing radiation, and disregard other important characteristics of cells such their ability to generate structures with a certain shape. The radioresistance and radiosensitivity of cancer cells may depend less on the number of cells in a colony and more on the way cells interact to form complex networks. In this study, we have examined whether the topology of 2D cancer-cell graphs is influenced by ionizing radiation. We subjected different cancer cell lines, i.e. H4 epithelial neuroglioma cells, H460 lung cancer cells, PC3 bone metastasis of grade IV of prostate cancer and T24 urinary bladder cancer cells, cultured on planar surfaces, to increasing photon radiation levels up to 6 Gy. Fluorescence images of samples were then processed to determine the topological parameters of the cell-graphs developing over time. We found that the larger the dose, the less uniform the distribution of cells on the substrate-evidenced by high values of small-world coefficient (cc), high values of clustering coefficient (cc), and small values of characteristic path length (cpl). For all considered cell lines, [Formula: see text] for doses higher or equal to 4 Gy, while the sensitivity to the dose varied for different cell lines: T24 cells seem more distinctly affected by the radiation, followed by the H4, H460 and PC3 cells. Results of the work reinforce the view that the characteristics of cancer cells and their response to radiotherapy can be determined by examining their collective behavior-encoded in a few topological parameters-as an alternative to classical clonogenic assays.

Topological analysis of vertebrate phylogenetic trees utilizing Horton's first law

Phylogenetic trees visually represent evolution and diversification. While many studies have focused on the number and length of edges (branches), topological properties, such as edge connection patterns, are also important. In this study, the topological properties of phylogenetic trees were quantified, focusing on edge connection patterns. Horton's first law was applied to quantify the overall, rather than local, topological properties of phylogenetic trees. The topological properties of vertebrate phylogenetic trees for spiny-rayed fishes, Amphibians, turtles, Squamata, Aves, and placental mammals were analyzed. The topological features discussed herein include the number of first-order edges, maximum order, and bifurcation ratio. The average bifurcation ratio of all trees was approximately 3, suggesting that phylogenetic trees for different taxa have a common mechanism of evolution. Vertebrate phylogenetic trees were compared with artificial branching objects created from neutral stochastic branching model simulations. The topological properties of the actual vertebrate phylogenetic trees agreed with those of the artificial branching objects. Our study suggests that evolutionary events do not change the overall topological properties of actual phylogenetic trees, even if the number and length of the edges change. Specifically, non-neutral events (e.g., environmental changes and mass extinction) are not main factors associated with topological properties. The results instead demonstrate a relationship between the bifurcation ratio and symmetricity in the context of temporal changes of topological properties. When the number of first-order edges increased and the maximum order remained constant, the bifurcation ratio increased and symmetricity decreased. When the number of first-order edges increased and the maximum order increased by one, the bifurcation ratio decreased and symmetricity increased.

On the nonlinear relationship between wall shear stress topology and multi-directionality in coronary atherosclerosis

BACKGROUND AND OBJECTIVE

In this paper we investigate twelve multi-directional/topological wall shear stress (WSS) derived metrics and their relationships with the formation of coronary plaques in both computational fluid dynamics (CFD) and dynamic fluid-structure interaction (FSI) frameworks. While low WSS is one of the most established biomechanical markers associated with coronary atherosclerosis progression, alone it is limited. Multi-directional and topological WSS derived metrics have been shown to be important in atherosclerosis related mechanotransduction and near-wall transport processes. However, the relationships between these twelve WSS metrics and the influence of both FSI simulations and coronary dynamics is understudied.

METHODS

We first investigate the relationships between these twelve WSS derived metrics, stenosis percentage and lesion length through a parametric, transient CFD study. Secondly, we extend the parametric study to FSI, both with and without the addition of coronary dynamics, and assess their correlations. Finally, we present the case of a patient who underwent invasive coronary angiography and optical coherence tomography imaging at two time points 18 months apart. Associations between each of the twelve WSS derived metrics in CFD, static FSI and dynamic FSI simulations were assessed against areas of positive/negative vessel remodelling, and changes in plaque morphology.

RESULTS

22-32% stenosis was the threshold beyond which adverse multi-directional/topological WSS results. Each metric produced a different relationship with changing stenoses and lesion length. Transient haemodynamics was impacted by coronary dynamics, with the topological shear variation index suppressed by up to 94%. These changes appear more critical at smaller stenosis levels, suggesting coronary dynamics could play a role in the earlier stages of atherosclerosis development. In the patient case, both dynamics and FSI vs CFD changes altered associations with measured changes in plaque morphology. An appendix of the linear fits between the various FSI- and CFD-based simulations is provided to assist in scaling CFD-based results to resemble the compliant walled characteristics of FSI more accurately.

CONCLUSIONS

These results highlight the potential for coronary dynamics to alter multi-directional/topological WSS metrics which could impact associations with changes in coronary atherosclerosis over time. These results warrant further investigation in a wider range of morphological settings and longitudinal cohort studies in the future.

Clinical relevance of disrupted topological organization of anatomical connectivity in behavioral variant frontotemporal dementia

Graph theory is a novel approach used to examine the balance of brain connectomes. However, the clinical relevance of white matter (WM) connectome changes in the behavioral variant frontotemporal dementia (bvFTD) is not well understood. We aimed to investigate the clinical relevance of WM topological alterations in bvFTD. Thirty patients with probable bvFTD and 30 healthy controls underwent diffusion tensor imaging, structural MRI, and neuropsychological assessment. WM connectivity between 90 brain regions was calculated and the graph approach was applied to capture the individual characteristics of the anatomical network. Voxel-based morphometry and tract-based spatial statistics were used to present the gray matter atrophy and disrupted WM integrity. The topological organization was disrupted in patients with bvFTD both globally and locally. Compared to controls, bvFTD data showed a different pattern of hub region distributions. Notably, the nodal efficiency of the right superior orbital frontal gyrus was associated with apathy and disinhibition. Topological measures may be potential image markers for early diagnosis and disease severity monitoring of bvFTD.

Biochemical reaction network topology defines dose-dependent Drug-Drug interactions

Drug combination therapy is a promising strategy to enhance the desired therapeutic effect, while reducing side effects. High-throughput pairwise drug combination screening is a commonly used method for discovering favorable drug interactions, but is time-consuming and costly. Here, we investigate the use of reaction network topology-guided design of combination therapy as a predictive in silico drug-drug interaction screening approach. We focused on three-node enzymatic networks, with general Michaelis-Menten kinetics. The results revealed that drug-drug interactions critically depend on the choice of target arrangement in a given topology, the nature of the drug, and the desired level of change in the network output. The results showed a negative correlation between antagonistic interactions and the dosage of drugs. Overall, the negative feedback loops showed the highest synergistic interactions (the lowest average combination index) and, intriguingly, required the highest drug doses compared to other topologies under the same condition.

Neuronal Ganglioside and Glycosphingolipid (GSL) Metabolism and Disease : Cascades of Secondary Metabolic Errors Can Generate Complex Pathologies (in LSDs)

Glycosphingolipids (GSLs) are a diverse group of membrane components occurring mainly on the surfaces of mammalian cells. They and their metabolites have a role in intercellular communication, serving as versatile biochemical signals (Kaltner et al, Biochem J 476(18):2623-2655, 2019) and in many cellular pathways. Anionic GSLs, the sialic acid containing gangliosides (GGs), are essential constituents of neuronal cell surfaces, whereas anionic sulfatides are key components of myelin and myelin forming oligodendrocytes. The stepwise biosynthetic pathways of GSLs occur at and lead along the membranes of organellar surfaces of the secretory pathway. After formation of the hydrophobic ceramide membrane anchor of GSLs at the ER, membrane-spanning glycosyltransferases (GTs) of the Golgi and Trans-Golgi network generate cell type-specific GSL patterns for cellular surfaces. GSLs of the cellular plasma membrane can reach intra-lysosomal, i.e. luminal, vesicles (ILVs) by endocytic pathways for degradation. Soluble glycoproteins, the glycosidases, lipid binding and transfer proteins and acid ceramidase are needed for the lysosomal catabolism of GSLs at ILV-membrane surfaces. Inherited mutations triggering a functional loss of glycosylated lysosomal hydrolases and lipid binding proteins involved in GSL degradation cause a primary lysosomal accumulation of their non-degradable GSL substrates in lysosomal storage diseases (LSDs). Lipid binding proteins, the SAPs, and the various lipids of the ILV-membranes regulate GSL catabolism, but also primary storage compounds such as sphingomyelin (SM), cholesterol (Chol.), or chondroitin sulfate can effectively inhibit catabolic lysosomal pathways of GSLs. This causes cascades of metabolic errors, accumulating secondary lysosomal GSL- and GG- storage that can trigger a complex pathology (Breiden and Sandhoff, Int J Mol Sci 21(7):2566, 2020).

Governing taste: data, temporality and everyday kiwifruit dry matter performances

Data is essential to governing those emerging matters of concern that confront the agrifood every day. But data is no neutral intermediary. It disrupts, exposes, and creates new social, economic, political, and environmental possibilities, whilst simultaneously hiding, excluding, and foreclosing others. Scholars have become attuned to both the constitutive role of data in creating everyday worlds, and the need to develop critical accounts of the materialities, spatialities and multiplicities of data relationships. Whereas this emerging work develops insight to the capacity for data topologies to reterritorialise the spatial performances of everyday life, it has largely reduced the associated temporal dimensions to matters of fact. The effect of these performances has been to naturalize the temporal quality of speed and elide the multiple temporalities required to enact contemporary data worlds. Applying the lenses of infrastructuring, performativity and ferality, this paper explores temporality and data in the everyday worlds produced through the New Zealand kiwifruit industry's focus on dry matter. The paper argues that temporalities are deeply embedded in the kiwifruit industry's data relations. We show that while temporal data relations are critical to the industry, we also highlight ways in which those relations introduce new, potentially destabilizing performances into kiwifruit relations.

ProteinCT: An implementation of the protein circuit topology framework

The ability to describe the topology of a folded protein conformation is critically important for functional analysis, protein engineering, and drug design. Circuit topology is a unique topological framework which is widely applicable to protein analysis, yet a state-of-the art implementation of this concept is lacking. Here, we present an open-source Python-implemented circuit topology tool called ProteinCT. The platform provides a method for acquiring, visualizing, analyzing, and quantifying circuit topology data from proteins of interest. We mapped the universe of human proteins to a circuit topology space using conventional hardware within a few hours, demonstrating the performance of ProteinCT. In brief,•A Python-implemented circuit topology tool is developed to extract global and local topological information from a protein structure file.•Modules are developed to combine topological information with geometric and energetic information.•It is demonstrated that the method can be efficiently applied to a large set of proteins, opening a wide range of possibilities for structural proteomics research.

Evolution, gravity, and the topology of consciousness

It has previously been hypothesized that consciousness is the aggregate of our evolutionary history, forged by ontogeny and phylogeny via cell-cell communications. In an on-going effort to identify the serial pre-adaptations that gave rise to consciousness, certain fundamental properties of the emerging cell are addressed herein. Evolution is topologic because it began as a phase transition caused by gravity attracting lipid molecules, spontaneously forming micelles submersed in the ocean that covered the primordial Earth, forming a surface boundary between the exterior Cosmos and the interior of micelles. Such protocells comply with the First Principles of Physiology-negative entropy, chemiosmosis and homeostasis-the first two principles being deterministic, the last being probabilistic, bestowing them with far more than just random chance. The mechanism of cellular evolution is based on exaptations of sequentially earlier and earlier genetic traits, working in reverse from present-day physiology all the way back to the unicellular state, which is homologous with mathematical 'knots'. Ironically, that relationship is evidence for the ontologic and epistemologic primacy of the cell, which supersedes mathematics and physics as manifestations of the Implicate Order since a conscious cell can conceive of a circle, but an unconscious circle is not able to conceive of a cell.

Identifying and Differentiating Topological G-Quadruplex Structures with DNA-Encoded Plasmonic Gold Nanoparticles

DNA G-quadruplexes (G4s) have been identified as critical elements in modulating genomic functions and many other biological processes. Their functions are highly dependent on the primary nucleotides and secondary folding structures. Therefore, to understand their functions, methods to identify and differentiate structures of G4 with speed and accuracy are required but limited. In this report, we have applied a synthetic G4 DNA-encoded nanoparticle approach to identify and differentiate G4 DNA molecules with different topologies and nucleotide residues. We found that the resulting plasmonic properties of the gold nanoparticles, monitored by UV/Vis spectroscopy, are quite sensitive to different G4 structures, including stacking layers, loop sequences, capping bases on G4s, and topological structures. Through these systematic investigations, we demonstrate that this G4-encoded gold nanoparticle approach can be used to profile the G4 structures and distinguish G4s from human telomeres. Such a method may have wide applications in G4 research.

SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks

BACKGROUND

Representing the complex interplay between different types of biomolecules across different omics layers in multi-omics networks bears great potential to gain a deep mechanistic understanding of gene regulation and disease. However, multi-omics networks easily grow into giant hairball structures that hamper biological interpretation. Module detection methods can decompose these networks into smaller interpretable modules. However, these methods are not adapted to deal with multi-omics data nor consider topological features. When deriving very large modules or ignoring the broader network context, interpretability remains limited. To address these issues, we developed a SUbgraph BAsed mulTi-OMIcs Clustering framework (SUBATOMIC), which infers small and interpretable modules with a specific topology while keeping track of connections to other modules and regulators.

RESULTS

SUBATOMIC groups specific molecular interactions in composite network subgraphs of two and three nodes and clusters them into topological modules. These are functionally annotated, visualized and overlaid with expression profiles to go from static to dynamic modules. To preserve the larger network context, SUBATOMIC investigates statistically the connections in between modules as well as between modules and regulators such as miRNAs and transcription factors. We applied SUBATOMIC to analyze a composite Homo sapiens network containing transcription factor-target gene, miRNA-target gene, protein-protein, homologous and co-functional interactions from different databases. We derived and annotated 5586 modules with diverse topological, functional and regulatory properties. We created novel functional hypotheses for unannotated genes. Furthermore, we integrated modules with condition specific expression data to study the influence of hypoxia in three cancer cell lines. We developed two prioritization strategies to identify the most relevant modules in specific biological contexts: one considering GO term enrichments and one calculating an activity score reflecting the degree of differential expression. Both strategies yielded modules specifically reacting to low oxygen levels.

CONCLUSIONS

We developed the SUBATOMIC framework that generates interpretable modules from integrated multi-omics networks and applied it to hypoxia in cancer. SUBATOMIC can infer and contextualize modules, explore condition or disease specific modules, identify regulators and functionally related modules, and derive novel gene functions for uncharacterized genes. The software is available at https://github.com/CBIGR/SUBATOMIC .

Topological Gradients for Metal Film-Based Strain Sensors

Metal film-based stretchable strain sensors hold great promise for applications in various domains, which require superior sensitivity-stretchability-cyclic stability synergy. However, the sensitivity-stretchability trade-off has been a long-standing dilemma and the metal film-based strain sensors usually suffer from weak cyclic durability, both of which significantly limit their practical applications. Here, we propose an extremely facile, low-cost and spontaneous strategy that incorporates topological gradients in metal film-based strain sensors, composed of intrinsic (grain size and interface) and extrinsic (film thickness and wrinkle) microstructures. The topological gradient strain sensor exhibits an ultrawide stretchability of 100% while simultaneously maintaining a high sensitivity at an optimal topological gradient of 4.5, due to the topological gradients-induced multistage film cracking. Additionally, it possesses a decent cyclic stability for >10 000 cycles between 0 and 40% strain enabled by the gradient-mixed metal/elastomer interfaces. It can monitor the full-range human activities from subtle pulse signals to vigorous joint movements.

The impact of the size of acetylated cyclodextrin on the stability of amorphous metronidazole

Modified oligosaccharides with cyclic topology seem to be promising excipients for the preparation of Amorphous Solid Dispersions (ASDs), especially with those Active Pharmaceutical Ingredients (APIs), which have a strong crystallization tendency from the amorphous/glassy state. Herein, the usefulness of two acetylated cyclodextrins (ac-α-CD and ac-β-CD) with various molecular weights (M_w) as stabilizers for the supercooled metronidazole (Met) has been discussed. X-ray diffraction (XRD) studies carried out on Met-acCDs mixtures (prepared in molar ratios from 1:2 to 5:1) showed that the system with ac-α-CD containing the highest amount of API (5:1 m/m) crystallizes immediately after preparation, whereas all Met-ac-β-CD ASDs remain stable. What is more, long-term XRD measurements confirmed that the Met-ac-α-CD 2:1 m/m system crystallizes after 100 days of storage in contrast to the same system containing ac-β-CD. The non-isothermal calorimetric data revealed that the activation barrier for crystallization (E_cr) in ASDs with the oligosaccharide having a greater M_w (i.e., composed of seven acGLU molecules) is slightly higher. Finally, to explain the differences in behavior between the mixtures with both acCDs, infrared studies, DFT calculations and Molecular Dynamics simulations were performed. All methods excluded the scenario of API incorporation inside the acCDs' core. On the other hand, obtained results suggested that in comparison to ac-α-CD, the greater amount of Met molecules might be bounded on the outside surface of ac-β-CD. Therefore, this modified saccharide is a better stabilizer of the examined API.

TopoFit: Rapid Reconstruction of Topologically-Correct Cortical Surfaces

Mesh-based reconstruction of the cerebral cortex is a fundamental component in brain image analysis. Classical, iterative pipelines for cortical modeling are robust but often time-consuming, mostly due to expensive procedures that involve topology correction and spherical mapping. Recent attempts to address reconstruction with machine learning methods have accelerated some components in these pipelines, but these methods still require slow processing steps to enforce topological constraints that comply with known anatomical structure. In this work, we introduce a novel learning-based strategy, TopoFit, which rapidly fits a topologically-correct surface to the white-matter tissue boundary. We design a joint network, employing image and graph convolutions and an efficient symmetric distance loss, to learn to predict accurate deformations that map a template mesh to subject-specific anatomy. This technique encompasses the work of current mesh correction, fine-tuning, and inflation processes and, as a result, offers a 150× faster solution to cortical surface reconstruction compared to traditional approaches. We demonstrate that TopoFit is 1.8× more accurate than the current state-of-the-art deep-learning strategy, and it is robust to common failure modes, such as white-matter tissue hypointensities.

G4Boost: a machine learning-based tool for quadruplex identification and stability prediction

Background

G-quadruplexes (G4s), formed within guanine-rich nucleic acids, are secondary structures involved in important biological processes. Although every G4 motif has the potential to form a stable G4 structure, not every G4 motif would, and accurate energy-based methods are needed to assess their structural stability. Here, we present a decision tree-based prediction tool, G4Boost, to identify G4 motifs and predict their secondary structure folding probability and thermodynamic stability based on their sequences, nucleotide compositions, and estimated structural topologies.

Results

G4Boost predicted the quadruplex folding state with an accuracy greater then 93% and an F1-score of 0.96, and the folding energy with an RMSE of 4.28 and R² of 0.95 only by the means of sequence intrinsic feature. G4Boost was successfully applied and validated to predict the stability of experimentally-determined G4 structures, including for plants and humans.

Conclusion

G4Boost outperformed the three machine-learning based prediction tools, DeepG4, Quadron, and G4RNA Screener, in terms of both accuracy and F1-score, and can be highly useful for G4 prediction to understand gene regulation across species including plants and humans.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04782-z.

Quantification of vascular networks in photoacoustic mesoscopy

Mesoscopic photoacoustic imaging (PAI) enables non-invasive visualisation of tumour vasculature. The visual or semi-quantitative 2D measurements typically applied to mesoscopic PAI data fail to capture the 3D vessel network complexity and lack robust ground truths for assessment of accuracy. Here, we developed a pipeline for quantifying 3D vascular networks captured using mesoscopic PAI and tested the preservation of blood volume and network structure with topological data analysis. Ground truth data of in silico synthetic vasculatures and a string phantom indicated that learning-based segmentation best preserves vessel diameter and blood volume at depth, while rule-based segmentation with vesselness image filtering accurately preserved network structure in superficial vessels. Segmentation of vessels in breast cancer patient-derived xenografts (PDXs) compared favourably to ex vivo immunohistochemistry. Furthermore, our findings underscore the importance of validating segmentation methods when applying mesoscopic PAI as a tool to evaluate vascular networks in vivo.

A reappraisal of the form: function problem-theory and phenomenology

This paper is aimed at demonstrating that some geometrical and topological transformations and operations serve not only as promoters of many specific genetic and cellular events in multicellular living organisms, but also as initiators of the organization and regulation of their functions. Thus, changes in the form and structure of macromolecular and cellular systems must be directly associated to their functions. There are specific classes of enzymes that manipulate the geometry and topology of complex DNA-protein structures, and thereby they perform many important cellular processes, including segregation of daughter chromosomes, gene regulation, and DNA repair. We argue that form has an organizing power, hence a causal action, in the sense that it enables to induce functional events during different biological processes, at the supramolecular, cellular, and organismal levels of organization. Clearly, topological forms must be matched with specific kinetic and dynamical parameters to have a functional effectiveness in living systems. This effectiveness is remarkably apparent, to give an example, in the regulation of the genome functions and in cell activity. In more general terms, we try to show that the conformational plasticity of biological systems depends on different kinds of topological manipulations performed by specific families of enzymes. In doing so, they catalyze all those spatial and dynamical changes of biological structures that are suitable for the functions to be acted by the organism.

RestraintMaker: a graph-based approach to select distance restraints in free-energy calculations with dual topology

The calculation of relative binding free energies (RBFE) involves the choice of the end-state/system representation, of a sampling approach, and of a free-energy estimator. System representations are usually termed “single topology” or “dual topology”. As the terminology is often used ambiguously in the literature, a systematic categorization of the system representations is proposed here. In the dual-topology approach, the molecules are simulated as separate molecules. Such an approach is relatively easy to automate for high-throughput RBFE calculations compared to the single-topology approach. Distance restraints are commonly applied to prevent the molecules from drifting apart, thereby improving the sampling efficiency. In this study, we introduce the program RestraintMaker, which relies on a greedy algorithm to find (locally) optimal distance restraints between pairs of atoms based on geometric measures. The algorithm is further extended for multi-state methods such as enveloping distribution sampling (EDS) or multi-site \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda $$\end{document}λ-dynamics. The performance of RestraintMaker is demonstrated for toy models and for the calculation of relative hydration free energies. The Python program can be used in script form or through an interactive GUI within PyMol. The selected distance restraints can be written out in GROMOS or GROMACS file formats. Additionally, the program provides a human-readable JSON format that can easily be parsed and processed further. The code of RestraintMaker is freely available on GitHub https://github.com/rinikerlab/restraintmaker.

The polarization dependence of 2D IR cross-peaks distinguishes parallel-stranded and antiparallel-stranded DNA G-quadruplexes

Guanine-rich nucleic acid sequences have a tendency to form four-stranded non-canonical motifs known as G-quadruplexes. These motifs may adopt a wide range of structures characterized by size, strand orientation, guanine base conformation, and fold topology. Using three K⁺-bound model systems, we show that vibrational coupling between guanine C6 = O and ring modes varies between parallel-stranded and antiparallel-stranded G-quadruplexes, and that such structures can be distinguished by comparison of the polarization dependences of cross-peaks in their two-dimensional infrared (2D IR) spectra. Combined with previously defined vibrational frequency trends, this analysis reveals key features of a 30-nucleotide unimolecular variant of the Bcl-2 proximal promoter that are consistent with its reported structure. This study shows that 2D IR spectroscopy is a convenient method for analyzing G-quadruplex structures that can be applied to complex sequences where traditional high-resolution methods are limited by solubility and disorder.

Validating atlas-based lesion disconnectomics in multiple sclerosis: A retrospective multi-centric study

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Structural disconnectomes can be modelled without diffusion using tractography atlases.

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Atlas-based and DTI-derived disconnectome topological metrics correlate strongly.

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MS patient disconnectomes relate to clinical scores.

The translational potential of MR-based connectivity modelling is limited by the need for advanced diffusion imaging, which is not part of clinical protocols for many diseases. In addition, where diffusion data is available, brain connectivity analyses rely on tractography algorithms which imply two major limitations. First, tracking algorithms are known to be sensitive to the presence of white matter lesions and therefore leading to interpretation pitfalls and poor inter-subject comparability in clinical applications such as multiple sclerosis. Second, tractography quality is highly dependent on the acquisition parameters of diffusion sequences, leading to a trade-off between acquisition time and tractography precision.

Here, we propose an atlas-based approach to study the interplay between structural disconnectivity and lesions without requiring individual diffusion imaging. In a multi-centric setting involving three distinct multiple sclerosis datasets (containing both 1.5 T and 3 T data), we compare our atlas-based structural disconnectome computation pipeline to disconnectomes extracted from individual tractography and explore its clinical utility for reducing the gap between radiological findings and clinical symptoms in multiple sclerosis. Results using topological graph properties showed that overall, our atlas-based disconnectomes were suitable approximations of individual disconnectomes from diffusion imaging. Small-worldness was found to decrease for larger total lesion volumes thereby suggesting a loss of efficiency in brain connectivity of MS patients. Finally, the global efficiency of the created brain graph, combined with total lesion volume, allowed to stratify patients into subgroups with different clinical scores in all three cohorts.

Topological Features of Electroencephalography are Robust to Re-referencing and Preprocessing

Electroencephalography (EEG) is among the most widely diffused, inexpensive, and adopted neuroimaging techniques. Nonetheless, EEG requires measurements against a reference site(s), which is typically chosen by the experimenter, and specific pre-processing steps precede analyses. It is therefore valuable to obtain quantities that are minimally affected by reference and pre-processing choices. Here, we show that the topological structure of embedding spaces, constructed either from multi-channel EEG timeseries or from their temporal structure, are subject-specific and robust to re-referencing and pre-processing pipelines. By contrast, the shape of correlation spaces, that is, discrete spaces where each point represents an electrode and the distance between them that is in turn related to the correlation between the respective timeseries, was neither significantly subject-specific nor robust to changes of reference. Our results suggest that the shape of spaces describing the observed configurations of EEG signals holds information about the individual specificity of the underlying individual's brain dynamics, and that temporal correlations constrain to a large degree the set of possible dynamics. In turn, these encode the differences between subjects' space of resting state EEG signals. Finally, our results and proposed methodology provide tools to explore the individual topographical landscapes and how they are explored dynamically. We propose therefore to augment conventional topographic analyses with an additional-topological-level of analysis, and to consider them jointly. More generally, these results provide a roadmap for the incorporation of topological analyses within EEG pipelines.

Borderline Personality Disorder, Lived Space, and the Stimmung

Most articles and theories about borderline personality disorder (BPD), either in the psychoanalytical field or the cognitivist one, explicitly or implicitly inscribe themselves in a topographical framework that either carry a fundamental representational a priori or give prominence to causal explanations. Less is written about the phenomenological everyday life-world of borderline people. This article aims to contribute to the description of such a world. Drawing upon clinical sequences that give prominence to the first-person perspective, we will analyse the experience of some typical "symptoms" of BPD in a phenomenological and topological way. We will be led to conclude that the borderline stimmung seems to display the following characteristics: a pervading immediacy of lived experience, a territorialization that tends towards ubiquity, a certain difficulty to deal with the unity and difference poles, a quite horizontal concern with ecstasy and elation, and a waning of reflexivity in the lived space.

TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging

BACKGROUND

3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture.

RESULTS

We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both CT scans of excavated field-grown root crowns and simulated images of root systems, and in both cases, it was shown to improve the accuracy of traits over existing methods. TopoRoot runs within a few minutes on a desktop workstation for images at the resolution range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image.

CONCLUSIONS

TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D imaging. The automation and efficiency make TopoRoot suitable for batch processing on large numbers of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.