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Abstract In the classical information theoretic framework, information “value” is proportional to how novel/surprising the information is. Recent work building on such notions claimed that false news spreads faster than truth online because false news is more novel and therefore surprising. However, another determinant of surprise, semantic meaning (e.g., information’s consistency or inconsistency with prior beliefs), should also influence value and sharing. Examining sharing behavior on Twitter, we observed separate relations of novelty and belief consistency with sharing. Though surprise could not be assessed in those studies, belief consistency should relate to less surprise, suggesting the relevance of semantic meaning beyond novelty. In two controlled experiments, belief-consistent (vs. belief-inconsistent) information was shared more despite consistent information being the least surprising. Manipulated novelty did not predict sharing or surprise. Thus, classical information theoretic predictions regarding perceived value and sharing would benefit from considering semantic meaning in contexts where people hold pre-existing beliefs.

Background and Aims: Recommendations on paediatric single-injection local anaesthetic (LA) dosing for peripheral nerve blocks (PNBs) are based on the children's weight and limited by weight-based toxicity concerns. In this study, we assessed the extent of circumferential spread and block characteristics following the injection of an age-based volume (age in years = LA volume) of 0.25% bupivacaine following popliteal sciatic nerve block (PSNB). Methods: Thirty children aged between 2 and 12 years with the American Society of Anesthesiologists (ASA) physical status I and II and undergoing foot and ankle surgical procedures were given single-injection ultrasound-guided subparaneural PSNB using 0.25% bupivacaine at age-based LA volume after the administration of anaesthesia. The circumferential pattern of LA spread (primary objective) was assessed along the nerve (both cephalad and caudal) using ultrasound from the point of administration and the block characteristics in terms of duration of sensory block. Results: The mean [standard deviation (SD)] cephalic circumferential LA spread distance was 2.52 (0.68) [95% confidence interval (CI): 2.27–2.76] cm. The mean (SD) caudal circumferential LA spread distance was 2.27 (0.48) [95% CI: 2.09–2.44] cm. The mean (SD) duration of the sensory block was 9.03 (0.97) [95% CI: 8.67–9.38] h. Conclusion: The age-based LA volume of bupivacaine for ultrasound-guided PSNB resulted in a longitudinal circumferential spread of around 4.7 cm (adding both cephalic and caudal spread) and provided adequate analgesia for nine postoperative hours.

Abstract Human mobility analysis plays a crucial role in urban analysis, city planning, epidemic modeling, and even understanding neighborhood effects on individuals’ health. Often, these studies model human mobility in the form of co-location networks. We have recently seen the tremendous success of network representation learning models on several machine learning tasks on graphs. To the best of our knowledge, limited attention has been paid to identifying communities using network representation learning methods specifically for co-location networks. We attempt to address this problem and study user mobility behavior through the communities identified with latent node representations. Specifically, we select several diverse network representation learning models to identify communities from a real-world co-location network. We include both general-purpose representation models that make no assumptions on network modality as well as approaches designed specifically for human mobility analysis. We evaluate these different methods on data collected in the Adolescent Health and Development in Context study. Our experimental analysis reveals that a recently proposed method (LocationTrails) offers a competitive advantage over other methods with respect to its ability to represent and reflect community assignment that is consistent with extant findings regarding neighborhood racial and socio-economic differences in mobility patterns. We also compare the learned activity profiles of individuals by factoring in their residential neighborhoods. Our analysis reveals a significant contrast in the activity profiles of individuals residing in white-dominated versus black-dominated neighborhoods and advantaged versus disadvantaged neighborhoods in a major metropolitan city of United States. We provide a clear rationale for this contrastive pattern through insights from the sociological literature.

Many real-world applications deal with data that have an underlying graph structure associated with it. To perform downstream analysis on such data, it is crucial to capture relational information of nodes over their expanded neighborhood efficiently. Herein, we focus on the problem of Collective Classification (CC) for assigning labels to unlabeled nodes. Most deep learning models for CC heavily rely on differentiable variants of Weisfeiler-Lehman (WL) kernels. However, due to current computing architectures' limitations, WL kernels and their differentiable variants are limited in their ability to capture useful relational information only over a small expanded neighborhood of a node. To address this concern, we propose the framework, I-HOP, that couples differentiable kernels with an iterative inference mechanism to scale to larger neighborhoods. I-HOP scales differentiable graph kernels to capture and summarize information from a larger neighborhood in each iteration by leveraging a historical neighborhood summary obtained in the previous iteration. This recursive nature of I-HOP provides an exponential reduction in time and space complexity over straightforward differentiable graph kernels. Additionally, we point out a limitation of WL kernels where the node's original information is decayed exponentially with an increase in neighborhood size and provide a solution to address it. Finally, extensive evaluation across 11 datasets showcases the improved results and robustness of our proposed iterative framework, I-HOP.

Thoracolumbar Interfascial Plane Blocks (TLIP) is commonly used for pain management after spine surgeries. It involves blocking the dorsal rami of the thoracolumbar nerves in the fascial plane between the multifidus and longissimus muscles. The clinical efficacy of the conventional TLIP block is well documented in the postoperative pain management for lumbar spinal surgeries. The novel ultrasound-guided modified TLIP block described here is relatively safe and easier to perform by the trainee Anaesthesiologists. Besides, the relevant sonoanatomic landmarks of the novel technique and its perioperative analgesic efficacy in patients undergoing lumbar spine fusion surgery are described here. Further ultrasound-anatomy correlation studies are required to investigate this novel approach TLIP block.

Abstract Learning on graphs is a subject of great interest due to the abundance of relational data from real-world systems. Many of these systems involve higher-order interactions (super-dyadic) rather than mere pairwise (dyadic) relationships; examples of these are co-authorship, co-citation, and metabolic reaction networks. Such super-dyadic relations are more adequately modeled using hypergraphs rather than graphs. Learning on hypergraphs has thus been garnering increased attention with potential applications in network analysis, VLSI design, and computer vision, among others. Especially, hypergraph clustering is gaining attention because of its enormous applications such as component placement in VLSI, group discovery in bibliographic systems, image segmentation in CV, etc. For the problem of clustering on graphs, modularity maximization has been known to work well in the pairwise setting. Our primary contribution in this article is to provide a generalization of the modularity maximization framework for clustering on hypergraphs. In doing so, we introduce a null model for graphs generated by hypergraph reduction and prove its equivalence to the configuration model for undirected graphs. The proposed graph reduction technique preserves the node degree sequence from the original hypergraph. The modularity function can be defined on a thus reduced graph, which can be maximized using any standard modularity maximization method, such as the Louvain method. We additionally propose an iterative technique that provides refinement over the obtained clusters. We demonstrate both the efficacy and efficiency of our methods on several real-world datasets.

Biological networks catalog the complex web of interactions happening between different molecules, typically proteins, within a cell. These networks are known to be highly modular, with groups of proteins associated with specific biological functions. Human diseases often arise from the dysfunction of one or more such proteins of the biological functional group. The ability, to identify and automatically extract these modules has implications for understanding the etiology of different diseases as well as the functional roles of different protein modules in disease. The recent DREAM challenge posed the problem of identifying disease modules from six heterogeneous networks of proteins/genes. There exist many community detection algorithms, but all of them are not adaptable to the biological context, as these networks are densely connected and the size of biologically relevant modules is quite small. The contribution of this study is 3-fold: first, we present a comprehensive assessment of many classic community detection algorithms for biological networks to identify non-overlapping communities, and propose heuristics to identify small and structurally well-defined communities—core modules. We evaluated our performance over 180 GWAS datasets. In comparison to traditional approaches, with our proposed approach we could identify 50% more number of disease-relevant modules. Thus, we show that it is important to identify more compact modules for better performance. Next, we sought to understand the peculiar characteristics of disease-enriched modules and what causes standard community detection algorithms to detect so few of them. We performed a comprehensive analysis of the interaction patterns of known disease genes to understand the structure of disease modules and show that merely considering the known disease genes set as a module does not give good quality clusters, as measured by typical metrics such as modularity and conductance. We go on to present a methodology leveraging these known disease genes, to also include the neighboring nodes of these genes into a module, to form good quality clusters and subsequently extract a “gold-standard set” of disease modules. Lastly, we demonstrate, with justification, that “overlapping” community detection algorithms should be the preferred choice for disease module identification since several genes participate in multiple biological functions.

Background: Subclinical hypothyroidism (SCH) is a biochemical disease which is characterized by elevated serum levels of thyroid stimulating hormone (TSH) with normal thyroid hormone levels. In an attempt to correct the disease at its entry point, we wished to find out the incidence of subclinical hypothyroidism in female college students in Kumbakonam, a semiurban town of India. Methods: Around 260 female college students who had no history of thyroid disease were screened for thyroid dysfunction by a TSH assay. Results: The mean age ± standard deviation was 18.72 ± 2.27 years. The mean TSH value was 3.98 mIU/mL. The incidence of abnormally high TSH values was around 11.5%. The number of such cases was 30 with low T3 values in six students. One had a value of 150 with no symptoms. Another student had a value of 0.15 and her T3-T4 profile was normal. All students were asymptomatic. None of the students had goiter.Conclusions: In an unpublished but accepted study, we found an incidence of 3.5% in the school female children in the age group of 15–17. A sudden jump in the incidence is occurring in the age group of 18–22. This needs a workup of the causative factors and their possible correction.