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Detecting communities in networks is important in various domains of applications. While a variety of methods exist to perform this task, recent efforts propose Optimal Transport (OT) principles combined with the geometric notion of Ollivier-Ricci curvature to classify nodes into groups by rigorously comparing the information encoded into nodes' neighborhoods. We present an OT-based approach that exploits recent advances in OT theory to allow tuning between different transportation regimes. This allows for better control of the information shared between nodes' neighborhoods. As a result, our model can flexibly capture different types of network structures and thus increase performance accuracy in recovering communities, compared to standard OT-based formulations. We test the performance of our algorithm on both synthetic and real networks, achieving a comparable or better performance than other OT-based methods in the former case, while finding communities that better represent node metadata in real data. This pushes further our understanding of geometric approaches in their ability to capture patterns in complex networks.

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Images of natural systems may represent patterns of network-like structure, which could reveal important information about the topological properties of the underlying subject. However, the image itself does not automatically provide a formal definition of a network in terms of sets of nodes and edges. Instead, this information should be suitably extracted from the raw image data. Motivated by this, we present a principled model to extract network topologies from images that is scalable and efficient. We map this goal into solving a routing optimization problem where the solution is a network that minimizes an energy function which can be interpreted in terms of an operational and infrastructural cost. Our method relies on recent results from optimal transport theory and is a principled alternative to standard image-processing techniques that are based on heuristics. We test our model on real images of the retinal vascular system, slime mould and river networks and compare with routines combining image-processing techniques. Results are tested in terms of a similarity measure related to the amount of information preserved in the extraction. We find that our model finds networks from retina vascular network images that are more similar to hand-labelled ones, while also giving high performance in extracting networks from images of rivers and slime mould for which there is no ground truth available. While there is no unique method that fits all the images the best, our approach performs consistently across datasets, its algorithmic implementation is efficient and can be fully automatized to be run on several datasets with little supervision.

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Routing optimization is a relevant problem in many contexts. Solving directly this type of optimization problem is often computationally intractable. Recent studies suggest that one can instead turn this problem into one of solving a dynamical system of equations, which can instead be solved efficiently using numerical methods. This results in enabling the acquisition of optimal network topologies from a variety of routing problems. However, the actual extraction of the solution in terms of a final network topology relies on numerical details which can prevent an accurate investigation of their topological properties. In fact, in this context, theoretical results are fully accessible only to an expert audience and ready-to-use implementations for non-experts are rarely available or insufficiently documented. In particular, in this framework, final graph acquisition is a challenging problem in-and-of-itself. Here we introduce a method to extract network topologies from dynamical equations related to routing optimization under various parameters' settings. Our method is made of three steps: first, it extracts an optimal trajectory by solving a dynamical system, then it pre-extracts a network, and finally, it filters out potential redundancies. Remarkably, we propose a principled model to address the filtering in the last step, and give a quantitative interpretation in terms of a transport-related cost function. This principled filtering can be applied to more general problems such as network extraction from images, thus going beyond the scenarios envisioned in the first step. Overall, this novel algorithm allows practitioners to easily extract optimal network topologies by combining basic tools from numerical methods, optimization and network theory. Thus, we provide an alternative to manual graph extraction which allows a grounded extraction from a large variety of optimal topologies. The analysis of these may open up the possibility to gain new insights into the structure and function of optimal networks. We provide an open source implementation of the code online.

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Visualizing post-translational modifications, conformations, and interaction surfaces of protein structures at atomic resolution underpins the development of novel therapeutics to combat disease. As computational resources expand, in silico calculations coupled with experimentally derived structures and functional assays have led to an explosion in structure-based drug design (SBDD) with several compounds in clinical trials. It is increasingly clear that "hidden" transition-state structures along activation trajectories can be harnessed to develop novel classes of allosteric inhibitors. The goal of this mini-review is to empower the clinical researcher with a general knowledge of the strengths and weaknesses of nuclear magnetic resonance (NMR) spectroscopy in molecular medicine. Although NMR can determine protein structures at atomic resolution, its unrivaled strength lies in sensing subtle changes in a nuclei's chemical environment as a result of intrinsic conformational dynamics, solution conditions, and binding interactions. These can be recorded at atomic resolution, without explicit structure determination, and then incorporated with static structures or molecular dynamics simulations to produce a complete biological picture.

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We engineered covalently circularized nanodiscs (cNDs) which, compared with standard nanodiscs, exhibit enhanced stability, defined diameter sizes and tunable shapes. Reconstitution into cNDs enhanced the quality of nuclear magnetic resonance spectra for both VDAC-1, a ß-barrel membrane protein, and the G-protein-coupled receptor NTR1, an α-helical membrane protein. In addition, we used cNDs to visualize how simple, nonenveloped viruses translocate their genomes across membranes to initiate infection.

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An HPLC method for the assay of the heat shock protein 90 inhibitor, PU-H71 (NSC 750424), has been developed and validated. The stress testing of PU-H71 was carried out in accordance with ICH guidelines Q1A (R2) under aqueous, acidic, alkaline, oxidative, thermolytic and photolytic conditions. The separation of PU-H71 from its impurities and degradation products was achieved within 50min on a Mac-Mod ACE 3 C18 column (150mm×4.6mm i.d., 3µm) with a gradient mobile phase comprising 20-95% acetonitrile in water, with 0.1% trifluroacetic acid in both phases. LC-quadrupole TOF/MS was used to obtain accurate mass data on various components as well as on their fragments for characterization of impurities and degradation products. The proposed HPLC assay method was validated for specificity, linearity (concentration range 0.1-0.3mg/mL, r≥0.9998), accuracy (recovery 99.7-101.1%), precision (intra-lab RSD≤1.39%, inter-lab RSD≤0.91%), sensitivity (LOD 0.08µg/mL), and ruggedness. The developed method was suitable for the assay and stability monitoring of PU-H71 drug substance.

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Compound CU201 [SUIM-(d-Arg-Arg-Pro-Hyp-Gly-Igl-Ser-d-Igl-Oic-Arg)(2), where SUIM=suberimidyl; Hyp=trans-4-hydroxyproline; Igl=alpha-(2-indanyl)-glycine; Oic=octahydroindole-2-carboxylic acid], is a dimeric analog of the potent bradykinin antagonist peptide B9430. It blocks the G(alphaq,11) signal of the heterotrimeric G proteins, stimulates c-Jun kinases, and induces apoptosis in lung cancer cells with neuroendocrine features. CU201 shows potent inhibition for small-cell lung cancer cells in vitro (ED(50)=0.15microM), as well as for small-cell lung cancer SHP-77 tumor growth in vivo. An HPLC method was developed, as part of a study supported by the National Cancer Institute's (NCI's) Rapid Access to Interventional Development (RAID) program, to assess the purity and stability of CU201. Impurities and degradation products were characterized by LC/MS. The identity of a major impurity, with 1 mass unit different from CU201, was confirmed by high resolution LC/MS and the investigation of model compounds. Susceptible linkages in the peptide chains were revealed by the degradation study.

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