In this paper, we propose a new distortion quantification method for point clouds, the multiscale potential energy discrepancy (MPED). Currently, there is a lack of effective distortion quantification for a variety of point cloud perception tasks. Specifically, for dense point clouds, a distortion quantification method is used to predict human subjective scores and optimize the selection of human perception tasks parameters, such as compression and enhancement. For sparse point clouds, a distortion quantification methods is work as a loss function to guide the training of deep neural networks for unsupervised learning tasks (e.g., point cloud reconstruction, completion and upsampling). Therefore, an effective distortion quantification should be differentiable, distortion discriminable and have a low computational complexity. However, current distortion quantification cannot satisfy all three conditions. To fill this gap, we propose a new point cloud feature description method, the point potential energy (PPE), inspired by the classical physics. We regard the point clouds are systems that have potential energy and the distortion can change the total potential energy. By evaluating at various neighborhood sizes, the proposed MPED achieves global-local tradeoffs, capturing distortion in a multiscale fashion. We further theoretically show that classical Chamfer distance is a special case of our MPED. Extensive experiments show the proposed MPED superior to current methods on both human and machine perception tasks. Our code is avaliable at https://github.com/Qi-Yangsjtu/MPED.

Glycans are fundamental cellular building blocks, involved in many organismal functions. Advances in glycomics are elucidating the essential roles of glycans. Still, it remains challenging to properly analyze large glycomics datasets, since the abundance of each glycan is dependent on many other glycans that share many intermediate biosynthetic steps. Furthermore, the overlap of measured glycans can be low across samples. We address these challenges with GlyCompare, a glycomic data analysis approach that accounts for shared biosynthetic steps for all measured glycans to correct for sparsity and non-independence in glycomics, which enables direct comparison of different glycoprofiles and increases statistical power. Using GlyCompare, we study diverse N-glycan profiles from glycoengineered erythropoietin. We obtain biologically meaningful clustering of mutant cell glycoprofiles and identify knockout-specific effects of fucosyltransferase mutants on tetra-antennary structures. We further analyze human milk oligosaccharide profiles and find mother's fucosyltransferase-dependent secretor-status indirectly impact the sialylation. Finally, we apply our method on mucin-type O-glycans, gangliosides, and site-specific compositional glycosylation data to reveal tissues and disease-specific glycan presentations. Our substructure-oriented approach will enable researchers to take full advantage of the growing power and size of glycomics data.

GlyCompareCT is a portable command-line tool to facilitate downstream glycomic data analyses, by addressing data inherent sparsity and non-independence. Inputting glycan abundances, users can run GlyCompareCT with one line of code to obtain the abundances of a minimal substructure set, named glycomotif, thereby quantifying hidden biosynthetic relationships between measured glycans. Optional parameters tuning and annotation are supported for personal preference. For complete details on the use and execution of this protocol, please refer to Bao et al. (2021).¹.

Ewing sarcoma (ES) is an uncommon mesenchymal neoplasm that typically develops as a bone mass, although up to 30% arise in extraskeletal sites. ES of the gastrointestinal (GI) and hepatobiliary tract is rare and may be misdiagnosed as other, more common neoplasms that occur in these sites. However, the correct classification of extraskeletal ES is important for timely clinical management and prognostication. We reviewed our experience of ES in the GI and hepatobiliary tract in order to further highlight the clinicopathologic features of these neoplasms and document the potential for misdiagnosis in this setting. The archives and consultation files of 6 academic institutions were retrospectively queried for cases of ES occurring in the GI and hepatobiliary tract. The histologic slides and ancillary studies were reviewed and clinical data were retrieved for each case through the electronic medical records, when available. Twenty-three patients with ES in the GI and/or hepatobiliary tract were identified from 2000 to 2022. Of these, 11 were women and 12 were men with a median age of 38 years (range, 2 to 64). Tumor locations included the pancreas (n=5), liver (n=2), stomach (n=3), colorectum (n=3), and small intestine (n=5), as well as tumors involving multiple organs, pelvis and retroperitoneum (n=5). Tumor size varied between 2 cm and 18 cm. Twenty were primary and 3 were metastases. Of the 23 cases, only 17% were initially diagnosed as ES. The most common misdiagnoses involved various forms of neuroendocrine neoplasia due to expression of synaptophysin and other neuroendocrine markers (22%). A wide variety of diagnoses including GI stromal tumor was considered due to aberrant CD117 expression (4%). The diagnosis of ES was ultimately confirmed by detection of the EWSR1 rearrangement in 22 cases. The remaining case was diagnosed using traditional immunohistochemistry. Follow-up information was available in 20 cases, with follow-up time varying between 2 and 256 months. Six patients with follow-up died of disease between 6 and 60 months following initial presentation. Our data indicate ES in the GI and hepatobiliary tract is commonly misdiagnosed leading to a delay in therapy. In light of the attendant therapeutic and prognostic implications, ES should be considered in the differential diagnosis of any GI or hepatobiliary tumor with epithelioid and/or small round cell morphology.

Systems glycobiology aims to provide models and analysis tools that account for the biosynthesis, regulation, and interactions with glycoconjugates. To facilitate these methods, there is a need for a clear glycan representation accessible to both computers and humans. Linear Code, a linearized and readily parsable glycan structure representation, is such a language. For this reason, Linear Code was adapted to represent reaction rules, but the syntax has drifted from its original description to accommodate new and originally unforeseen challenges. Here, we delineate the consensuses and inconsistencies that have arisen through this adaptation. We recommend options for a consensus-based extension of Linear Code that can be used for reaction rule specification going forward. Through this extension and specification of Linear Code to reaction rules, we aim to minimize inconsistent symbology thereby making glycan database queries easier. With a clear guide for generating reaction rule descriptions, glycan synthesis models will be more interoperable and reproducible thereby moving glycoinformatics closer to compliance with FAIR standards. Here, we present Linear Code for Reaction Rules (LiCoRR), version 1.0, an unambiguous representation for describing glycosylation reactions in both literature and code.

Auxin inactivation is critical for plant growth and development. To develop plant growth regulators functioning in auxin inactivation pathway, we performed a phenotype-based chemical screen in Arabidopsis and identified a chemical, nalacin, that partially mimicked the effects of auxin. Genetic, pharmacological, and biochemical approaches demonstrated that nalacin exerts its auxin-like activities by inhibiting indole-3-acetic acid (IAA) conjugation that is mediated by Gretchen Hagen 3 (GH3) acyl acid amido synthetases. The crystal structure of Arabidopsis GH3.6 in complex with D4 (a derivative of nalacin) together with docking simulation analysis revealed the molecular basis of the inhibition of group II GH3 by nalacin. Sequence alignment analysis indicated broad bioactivities of nalacin and D4 as inhibitors of GH3s in vascular plants, which were confirmed, at least, in tomato and rice. In summary, our work identifies nalacin as a potent inhibitor of IAA conjugation mediated by group II GH3 that plays versatile roles in hormone-regulated plant development and has potential applications in both basic research and agriculture.