Presentation and Management of Metastatic Periacetabular Disease: A Case Report.

Multiple myeloma is a malignancy of blood cells that commonly metastasizes to bone. Metastasis to the acetabulum can cause significant patient distress due to pathological fractures that can be extremely painful and affects patients' ability to ambulate. The lytic lesions are unique and often require surgical management in order to restore normal anatomy to allow for weight bearing. A multidisciplinary approach is necessary to establish patient goals and optimize treatment plans. This report describes the presentation and treatment of an elderly patient with multiple myeloma and an unstable acetabular fracture with a large lytic lesion of the pelvis.

Presentation and Management of a Traumatic Right Sided Diaphragmatic Hernia: A Case Report.

Traumatic diaphragmatic injury (TDI) is a rare and dangerous sequela of trauma. Right sided TDI is even more uncommon due to the usual protection of the diaphragm by the liver. TDI can present in a delayed fashion, making diagnosis difficult to obtain. In any case TDI needs to be taken very seriously as it can lead to bowel strangulation and require emergency surgery. Multiple approaches have been described in order to definitively repair diaphragmatic defects. This report describes a patient who developed a delayed onset right sided diaphragmatic hernia after experiencing blunt trauma.

Simplicial closure and higher-order link prediction.

Networks provide a powerful formalism for modeling complex systems by using a model of pairwise interactions. But much of the structure within these systems involves interactions that take place among more than two nodes at once-for example, communication within a group rather than person to person, collaboration among a team rather than a pair of coauthors, or biological interaction between a set of molecules rather than just two. Such higher-order interactions are ubiquitous, but their empirical study has received limited attention, and little is known about possible organizational principles of such structures. Here we study the temporal evolution of 19 datasets with explicit accounting for higher-order interactions. We show that there is a rich variety of structure in our datasets but datasets from the same system types have consistent patterns of higher-order structure. Furthermore, we find that tie strength and edge density are competing positive indicators of higher-order organization, and these trends are consistent across interactions involving differing numbers of nodes. To systematically further the study of theories for such higher-order structures, we propose higher-order link prediction as a benchmark problem to assess models and algorithms that predict higher-order structure. We find a fundamental difference from traditional pairwise link prediction, with a greater role for local rather than long-range information in predicting the appearance of new interactions.

The Influence of Redox-Innocent Donor Groups in Tetradentate Ligands Derived from o-Phenylenediamine: Electronic Structure Investigations with Nickel.

The continued development of redox-active ligands requires an understanding as to how ligand modifications and related factors affect the locus of redox activity and spin density in metal complexes. Here we describe the synthesis, characterization, and electronic structure of nickel complexes containing triaryl NNNN (1) and SNNS (2) ligands derived from o-phenylenediamine. The tetradentate ligands in 1 and 2 were investigated and compared to those in metal complexes with compositionally similar ligands to determine how ligand-centered redox properties change when redox-active flanking groups are replaced with redox-innocent NMe2 or SMe. A derivative of 2 in which the phenylene backbone was replaced with ethylene (3) was also prepared to interrogate the importance of o-phenylenediamine for ligand-centered redox activity. Cyclic voltammograms collected for 1 and 2 revealed two fully reversible ligand-centered redox events. Remarkably, several quasi-reversible ligand-centered redox waves were also observed for 3 despite the absence of the o-phenylenediamine subunit. Oxidizing 1 and 2 with silver salts containing different counteranions (BF4-, OTf-, NTf2-) allowed the electrochemically generated complexes to be analyzed as a function of different oxidation states using single-crystal X-ray diffraction (XRD), EPR spectroscopy, and S K-edge X-ray absorption spectroscopy. The experimental data are corroborated by DFT calculations, and together, they reveal how the location of unpaired spin density and electronic structure in singly and doubly oxidized salts of 1 and 2 varies depending on the coordinating ability of the counteranions and exogenous ligands such as pyridine.

Combinatorial characterizations and impossibilities for higher-order homophily.

Homophily is the seemingly ubiquitous tendency for people to connect and interact with other individuals who are similar to them. This is a well-documented principle and is fundamental for how society organizes. Although many social interactions occur in groups, homophily has traditionally been measured using a graph model, which only accounts for pairwise interactions involving two individuals. Here, we develop a framework using hypergraphs to quantify homophily from group interactions. This reveals natural patterns of group homophily that appear with gender in scientific collaboration and political affiliation in legislative bill cosponsorship and also reveals distinctive gender distributions in group photographs, all of which cannot be fully captured by pairwise measures. At the same time, we show that seemingly natural ways to define group homophily are combinatorially impossible. This reveals important pitfalls to avoid when defining and interpreting notions of group homophily, as higher-order homophily patterns are governed by combinatorial constraints that are independent of human behavior but are easily overlooked.

Did Rapid Expansion of Same Day Discharge Hip and Knee Arthroplasty During the COVID-19 Pandemic Increase Early Complications?

Background: The COVID-19 pandemic has had a lasting impact on patients seeking total hip and knee arthroplasty (THA, TKA) including more patients undergoing same day discharge (SDD) following total joint arthroplasty (TJA). The purpose of this study was to assess whether expansion of SDD TJA during the COVID-19 pandemic resulted in more early complications following TJA. We anticipated that as many institutions quickly launched SDD TJA programs there may be an increase in 30-day complications. Methods: We retrospectively queried the ACS-NSQIP database for all patients undergoing primary elective TJA from January 1, 2018, to December 31, 2020. Participants who underwent THA or TKA between January 1, 2018 and March 1, 2020 were grouped into pre-COVID and between March 1, 2020 and December 31, 2020 were grouped into post-COVID categories. Patients with length of stay greater than 0 were excluded. Primary outcome was any complication at 30 days. Secondary outcomes included readmission and re-operation 30 days. Results: A total of 14,438 patients underwent TKA, with 9,580 occurring pre-COVID and 4,858 post-COVID. There was no difference in rates of total complication between the pre-COVID (3.55%) and post-COVID (3.99%) groups (p=0.197). Rates of readmissions for were similar for the pre-COVID (1.75%) and post-COVID (1.98%) groups (p=0.381). There was no statistically significant difference in respiratory complications between the pre-COVID (0.41%) and post-COVID group (0.23%, p=0.03). A total of 12,265 patients underwent THA, with 7,680 occurring pre-COVID and 4,585 post-COVID. There was no difference in rates of total complication between the pre-COVID (3.25%) and post-COVID (3.49%) groups (p=0.52). Rates of readmissions for were similar for the pre-COVID (1.77%) and post-COVID (1.68%) groups (p=0.381). There was no statistically significant difference in respiratory complications between the pre-COVID (0.16%) and post-COVID group (0.07%, p=0.26). Combined data to include THA and TKA patients did not find a statistical difference in the rate of complications or readmission but did note a decrease in the rate of combined respiratory complications in the post-COVID group (0.15% vs. 0.30%, p=0.028). Conclusion: Rapid expansion of SDD TJA during the COVID-19 pandemic did not increase overall complication, readmission, or re-operation rates. Level of Evidence: IV.

Generative hypergraph clustering: From blockmodels to modularity.

Hypergraphs are a natural modeling paradigm for networked systems with multiway interactions. A standard task in network analysis is the identification of closely related or densely interconnected nodes. We propose a probabilistic generative model of clustered hypergraphs with heterogeneous node degrees and edge sizes. Approximate maximum likelihood inference in this model leads to a clustering objective that generalizes the popular modularity objective for graphs. From this, we derive an inference algorithm that generalizes the Louvain graph community detection method, and a faster, specialized variant in which edges are expected to lie fully within clusters. Using synthetic and empirical data, we demonstrate that the specialized method is highly scalable and can detect clusters where graph-based methods fail. We also use our model to find interpretable higher-order structure in school contact networks, U.S. congressional bill cosponsorship and committees, product categories in copurchasing behavior, and hotel locations from web browsing sessions.

Sixteen-Year Follow-up of Talar Body Prosthesis and Total Ankle Arthroplasty After Traumatic Talar Extrusion: A Case Report.

CASE: We report a case of an extruded talus treated with an agility custom talar body prosthesis with total ankle arthroplasty 2 years after the injury. At the 16-year follow-up, the patient has performed remarkably well. At postoperative year 15, she required 1 additional surgery in which the polyethene was exchanged and the medial and lateral gutters were debrided. CONCLUSION: Talar body prothesis with total ankle arthroplasty may be a viable option for extrusion of the talus.

Construction and Composition of the Squid Pen from Doryteuthis pealeii.

The pen, or gladius, of the squid is an internalized shell. It serves as a site of attachment for important muscle groups and as a protective barrier for the visceral organs. The pen's durability and flexibility are derived from its unique composition of chitin and protein. We report the characterization of the structure, development, and composition of pens from Doryteuthis pealeii. The nanofibrils of the polysaccharide ß-chitin are arranged in an aligned configuration in only specific regions of the pen. Chitin is secreted early in development, enabling us to characterize the changes in pen morphology prior to hatching. The chitin and proteins are assembled in the shell sac surrounded by fluid that has a significantly different ionic composition from squid plasma. Two groups of proteins are associated with the pen: those on its surface and those embedded within the pen. Only 20 proteins are identified as embedded within the pen. Embedded proteins are classified into six groups, including chitin associated, protease, protease inhibitors, intracellular, extracellular matrix, and those that are unknown. The pen proteins share many conserved domains with proteins from other chitinous structures. We conclude that the pen is one of the least complex, load-bearing, chitin-rich structures currently known and is amenable to further studies to elucidate natural construction mechanisms using chitin and protein.

Higher-order clustering in networks.

A fundamental property of complex networks is the tendency for edges to cluster. The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a triangle in the network. However, higher-order cliques beyond triangles are crucial to understanding complex networks, and the clustering behavior with respect to such higher-order network structures is not well understood. Here we introduce higher-order clustering coefficients that measure the closure probability of higher-order network cliques and provide a more comprehensive view of how the edges of complex networks cluster. Our higher-order clustering coefficients are a natural generalization of the traditional clustering coefficient. We derive several properties about higher-order clustering coefficients and analyze them under common random graph models. Finally, we use higher-order clustering coefficients to gain new insights into the structure of real-world networks from several domains.

Local Higher-Order Graph Clustering.

Local graph clustering methods aim to find a cluster of nodes by exploring a small region of the graph. These methods are attractive because they enable targeted clustering around a given seed node and are faster than traditional global graph clustering methods because their runtime does not depend on the size of the input graph. However, current local graph partitioning methods are not designed to account for the higher-order structures crucial to the network, nor can they effectively handle directed networks. Here we introduce a new class of local graph clustering methods that address these issues by incorporating higher-order network information captured by small subgraphs, also called network motifs. We develop the Motif-based Approximate Personalized PageRank (MAPPR) algorithm that finds clusters containing a seed node with minimal motif conductance, a generalization of the conductance metric for network motifs. We generalize existing theory to prove the fast running time (independent of the size of the graph) and obtain theoretical guarantees on the cluster quality (in terms of motif conductance). We also develop a theory of node neighborhoods for finding sets that have small motif conductance, and apply these results to the case of finding good seed nodes to use as input to the MAPPR algorithm. Experimental validation on community detection tasks in both synthetic and real-world networks, shows that our new framework MAPPR outperforms the current edge-based personalized PageRank methodology.

Higher-order organization of complex networks.

Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks--at the level of small network subgraphs--remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks, including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns.

Tensor Spectral Clustering for Partitioning Higher-order Network Structures.

Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of important higher-order network substructures such as triangles, cycles, and feed-forward loops. Here we propose a Tensor Spectral Clustering (TSC) algorithm that allows for modeling higher-order network structures in a graph partitioning framework. Our TSC algorithm allows the user to specify which higher-order network structures (cycles, feed-forward loops, etc.) should be preserved by the network clustering. Higher-order network structures of interest are represented using a tensor, which we then partition by developing a multilinear spectral method. Our framework can be applied to discovering layered flows in networks as well as graph anomaly detection, which we illustrate on synthetic networks. In directed networks, a higher-order structure of particular interest is the directed 3-cycle, which captures feedback loops in networks. We demonstrate that our TSC algorithm produces large partitions that cut fewer directed 3-cycles than standard spectral clustering algorithms.