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Miyauchi, Atsushi, and Yasushi Kawase. "Z-Score-Based Modularity for Community Detection in Networks." PLOS ONE 11, no. 1 (January 25, 2016): e0147805. http://dx.doi.org/10.1371/journal.pone.0147805.
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Sun, Huaqi, Hui Shu, Fei Kang, and Yan Guang. "ModDiff: Modularity Similarity-Based Malware Homologation Detection." Electronics 12, no. 10 (May 16, 2023): 2258. http://dx.doi.org/10.3390/electronics12102258.
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In recent years, the number and scale of malicious codes have grown exponentially, posing an increasing threat to cybersecurity. Hence, it is of great research value to quickly identify variants of malware and master their family information. Binary code similarity detection, as a key technique in reverse analysis, plays an indispensable role in malware analysis. However, most existing methods focus on similarity at the function or basic block level, ignoring the modular composition of malware. Implementing similarity detection among malware modules would greatly improve the efficiency and accuracy of homology detection. Inspired by the successful application of deep-learning techniques in program analysis, we propose a binary code module similarity detection method called ModDiff. It abstracts malware into attribute graphs, clusters functions using graph-embedded clustering algorithms to decompose malware into function-based modules, and calculates module similarity using graph-matching algorithms and natural language processing-based function similarity detection algorithms. The experimental results indicated that ModDiff improves the accuracy of module partitioning by 10.8% compared with previous work, and the highest F1 score of 89% is achieved in malware homologation detection. These results demonstrate the effectiveness of ModDiff in detecting and analyzing malware with important application value and development prospects.
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Sachdeva, Shubhangini, Sridhar Mangalesh, and Sharmila Dudani. "Knowledge, Attitude and Practices of Breast Self-Examination Amongst Indian Women: A Pan-India Study." Asian Pacific Journal of Cancer Care 6, no. 2 (May 6, 2021): 141–47. http://dx.doi.org/10.31557/apjcc.2021.6.2.141-147.
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Objective: Breast Cancer is the leading cause of mortality and morbidity amongst Indian females. Breast self-examination (BSE) as an early detection modularity can have a profound effect on lowering mortality, if it is practiced regularly. The aim of this study was to assess the knowledge, attitude, and practice of Breast Self-examination amongst Indian women, aged 30 years and above. Methods: A validated questionnaire, consisting 35 questions measuring ‘Knowledge, Attitudes and Practice of Breast Self-Examination’ was widely circulated amongst 1000 females throughout India and responses were collected for a duration of 3 weeks, from 2nd, September 2020 to 23rd,September 2020. Results: Despite having knowledge about the technique of performing BSE, the respondents did not have a positive attitude towards it and were reluctant to practice BSE. A total of 54.4%, 15.1%, 21.3% and 9.2% of respondents were from the Northern, Southern, Western and Eastern regions of India, respectively. The overall median (IQR) KAP score was 70.0 (16.0) out of a maximum of 110. The knowledge, attitude and practice domain scores were 22.0 (5.0), 36.0 (7.0), and 13.0 (8.0) out of a maximum of 30, 52 and, 28, respectively. All three individual score domains were significantly correlated with each other and the total score.Conclusion: Despite having knowledge about the technique of performing BSE, the respondents did not have a positive attitude towards it and were reluctant to practice BSE.
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De Meo, Federico, Giorgio Cacciola, Vittorio Bellotti, Antongiolio Bruschetta, and Pietro Cavaliere. "Trabecular Titanium acetabular cups in hip revision surgery: mid-term clinical and radiological outcomes." HIP International 28, no. 2_suppl (November 2018): 61–65. http://dx.doi.org/10.1177/1120700018812992.
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Introduction: Severe acetabular bone loss during revision hip arthroplasty can be restored with different surgical techniques. Best results have been observed using porous tantalum cementless cups. Trabecular Titanium (TT) cups were designed to reproduce the structure, mechanical and biological characteristics of trabecular bone. Few clinical studies have been published with these cups. The aim of this study is to evaluate the clinical and radiological outcome of patients with large acetabular defects (Paprosky IIb or higher) revised using TT cups. Materials and methods: 64 consecutive patients who underwent revision for total hip arthroplasty (THA) between 2011 and 2015 at our institution were enrolled. All were classified as Paprosky IIb, IIc, IIIa and IIIb. Periprosthetic joint infections and fractures were not included in this study. Radiographic and clinical (Harris Hip Score (HHS)) outcomes were evaluated. Kaplan-Meyer survivorship curve was performed. Results: Complete data of 58 patients (mean follow-up 48.3 months) were analysed. 6 patients underwent a further revision (10.3%). Causes of re-revision were instability (5.2%), infection (3.4%) and aseptic loosening (1.7%). No radiolucent lines or signs of migration were observed. Survivorship was 94.8%. HHS Improved from a mean of 36.5–83.7. Discussion: TT cups demonstrated encouraging results at mid-term follow-up. Cup modularity represented a helpful tool to restore hip rotation centre and to reduce the risk of dislocation. The capacity of trabecular titanium to induce bone growth and the versatility of modularity, make the Delta TT System a very useful aid for surgeons who are more frequently faced with complex revision.
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Wang, Guohai, Yang Huang, Wei Yao, Qiuchan Huang, Yongping Huang, Lijuan Wei, and Qihai Zhou. "Structure and characteristics of the plant-frugivore bird network from the Guilin Botanical Garden." PeerJ 11 (March 16, 2023): e15028. http://dx.doi.org/10.7717/peerj.15028.
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The interaction between plants and frugivores is crucial to ecosystem function and community diversity. However, little is known about the interaction between plants and frugivorous bird species in urban green spaces. We observed interactions between plants and frugivorous birds in the Guilin Botanical Garden for one year and determined the structure and characteristics of the interaction network. We also analyzed the impact of species traits on their network roles. Interactions between 14 frugivorous birds and 13 fruit plant species were recorded in the study area. Autumn interactions comprised 38.79% of the overall network, and winter interactions comprised 33.15%. The modularity (Q, z-score) of the network was higher in autumn; the weighted nestedness (wNODF, z-score) and interaction evenness (E2, z-score) of the network were higher in winter; the connectance (C, z-score) and interaction diversity (z-score) of the network were higher in spring; and the specialization (H2′, z-score) of the network was higher in summer. The observed network showed lower C, lower interaction H2, lower E2 , lower wNODF, higher H2′ and higher Q when compared to the random networks. The bird species most important to network stability were Hemixos castanonotus, Parus venustulus, and Pycnonotus sinensis. The most important plant species were Alocasia macrorrhiza, Cinnamomum camphora, and Machilus nanmu. Of all the bird and plant traits included in this study, only plant color had a significant impact on species strength, with black fruit having a higher species strength. Our results suggest that interaction networks in urban green spaces can be temporally complex and variable and that a network approach can be an important monitoring tool for detecting the status of crucial ecosystem functions.
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Feklicheva, Inna, Ilya Zakharov, Nadezda Chipeeva, Ekaterina Maslennikova, Svetlana Korobova, Timofey Adamovich, Victoria Ismatullina, and Sergey Malykh. "Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity." Brain Sciences 11, no. 1 (January 13, 2021): 94. http://dx.doi.org/10.3390/brainsci11010094.
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The present study investigates the relationship between individual differences in verbal and non-verbal cognitive abilities and resting-state EEG network characteristics. We used a network neuroscience approach to analyze both large-scale topological characteristics of the whole brain as well as local brain network characteristics. The characteristic path length, modularity, and cluster coefficient for different EEG frequency bands (alpha, high and low; beta1 and beta2, and theta) were calculated to estimate large-scale topological integration and segregation properties of the brain networks. Betweenness centrality, nodal clustering coefficient, and local connectivity strength were calculated as local network characteristics. We showed that global network integration measures in the alpha band were positively correlated with non-verbal intelligence, especially with the more difficult part of the test (Raven’s total scores and E series), and the ability to operate with verbal information (the “Conclusions” verbal subtest). At the same time, individual differences in non-verbal intelligence (Raven’s total score and C series), and vocabulary subtest of the verbal intelligence tests, were negatively correlated with the network segregation measures. Our results show that resting-state EEG functional connectivity can reveal the functional architecture associated with an individual difference in cognitive performance.
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Liu, Zhihua, Hongmei Wang, Guishen Wang, and Yu Zhou. "Link community detection based on ensemble learning." Modern Physics Letters B 34, no. 27 (June 2, 2020): 2050293. http://dx.doi.org/10.1142/s0217984920502930.
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Overlapping community detection is a hot topic in the research of data mining and graph theory. In this paper, we propose a link community detection method based on ensemble learning (LCDEL). First, we transform graph into line graph and construct node adjacency matrix of line graph. Second, we calculate node distance of line graph through a new distance metric and get node distance matrix of line graph. Third, we use PCA method to reduce dimensions of node distance matrix of line graph. Then, we cluster on the reduced node distance matrix by k-means clustering algorithm. Finally, we convert line graph back into original graph and get overlapping communities of original graph with ensemble learning. Experimental results on several real-world networks demonstrate effectiveness of LCDEL method in terms of Normalized Mutual Information (NMI), Extended Modularity (EQ) and F-score evaluation metrics.
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LU, ZAIXIN, WEILI WU, WEIDONG CHEN, JIAOFEI ZHONG, YUANJUN BI, and ZHENG GAO. "THE MAXIMUM COMMUNITY PARTITION PROBLEM IN NETWORKS." Discrete Mathematics, Algorithms and Applications 05, no. 04 (December 2013): 1350031. http://dx.doi.org/10.1142/s1793830913500316.
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The community structure detection is an important problem in many areas such as biology network, computer network and social network. The objective of this problem is to analyze the relationships among data via the network topology. In the literature, many works have been done for partitioning a network into communities or clustering data into groups. In this paper, we define a series of conditions for communities and formulate the community detection problem into a combinatorial optimization problem which aims at partitioning a given network into disjoint communities such that all the communities satisfy the community conditions. We show that the maximization version of this problem is [Formula: see text]-hard for general networks under some natural conditions, and we develop a greedy heuristic algorithm for it. We also develop a refine algorithm to improve the modularity score of a community partition, subject to the community conditions.
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Wang, Lu, Yong Quan Liang, Qi Jia Tian, Jie Yang, Chao Song, and Zhuang Wu. "A Community Detection Method Based on Multi-Objective Optimization Method." Applied Mechanics and Materials 571-572 (June 2014): 177–82. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.177.
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Community detection in complex network has been an active research area in data mining and machine learning. This paper proposed a community detection method based on multi-objective evolutionary algorithm, named CDMOEA, which tries to find the Pareto front by maximize two objectives, community score and community fitness. Fast and Elitist Multi-objective Genetic Algorithm is used to attained a set of optimal solutions, and then use Modularity function to choose the best one from them. The locus based adjacency representation is used to realize genetic representation, which ensures the effective connections of the nodes in the network during the process of population Initialization and other genetic operator. Uniform crossover is introduced to ensure population’s diversity. We compared it with some popular community detection algorithms in computer generated network and real world networks. Experiment results show that it is more efficient in community detection.
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Zheng, Gang, Liping Zhang, Long Jiang Zhang, Qiang Li, Zhiying Pan, Xue Liang, Donghong Shi, and Guang Ming Lu. "Altered Modular Organization of Functional Connectivity Networks in Cirrhotic Patients without Overt Hepatic Encephalopathy." BioMed Research International 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/727452.
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Minimal hepatic encephalopathy (MHE) is associated with changes in functional connectivity. To investigate the patterns of modular changes of the functional connectivity in the progression of MHE, resting-state functional magnetic resonance imaging was acquired in 24 MHE patients, 31 cirrhotic patients without minimal hepatic encephalopathy (non-HE), and 38 healthy controls. Newman’s metric, the modularityQvalue, was maximized and compared in three groups. Topological roles with the progression of MHE were illustrated by intra- and intermodular connectivity changes. Results showed that theQvalue of MHE patients was significantly lower than that of controlsP<0.01rather than that of non-HE patientsP>0.05, which was correlated with neuropsychological test scores rather than the ammonia level and Child-Pugh score. Less intrasubcortical connections and more isolated subcortical modules were found with the progression of MHE. The non-HE patients had the same numbers of connect nodes as controls and had more hubs compared with MHE patients and healthy controls. Our findings supported that both intra- and intermodular connectivity, especially those related to subcortical regions, were continuously impaired in cirrhotic patients. The adjustments of hubs and connector nodes in non-HE patients could be a compensation for the decreased modularity in their functional connectivity networks.
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Lertampaiporn, Supatcha, Chinae Thammarongtham, Chakarida Nukoolkit, Boonserm Kaewkamnerdpong, and Marasri Ruengjitchatchawalya. "Identification of non-coding RNAs with a new composite feature in the Hybrid Random Forest Ensemble algorithm." Nucleic Acids Research 42, no. 11 (April 25, 2014): e93-e93. http://dx.doi.org/10.1093/nar/gku325.
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Abstract To identify non-coding RNA (ncRNA) signals within genomic regions, a classification tool was developed based on a hybrid random forest (RF) with a logistic regression model to efficiently discriminate short ncRNA sequences as well as long complex ncRNA sequences. This RF-based classifier was trained on a well-balanced dataset with a discriminative set of features and achieved an accuracy, sensitivity and specificity of 92.11%, 90.7% and 93.5%, respectively. The selected feature set includes a new proposed feature, SCORE. This feature is generated based on a logistic regression function that combines five significant features—structure, sequence, modularity, structural robustness and coding potential—to enable improved characterization of long ncRNA (lncRNA) elements. The use of SCORE improved the performance of the RF-based classifier in the identification of Rfam lncRNA families. A genome-wide ncRNA classification framework was applied to a wide variety of organisms, with an emphasis on those of economic, social, public health, environmental and agricultural significance, such as various bacteria genomes, the Arthrospira (Spirulina) genome, and rice and human genomic regions. Our framework was able to identify known ncRNAs with sensitivities of greater than 90% and 77.7% for prokaryotic and eukaryotic sequences, respectively. Our classifier is available at http://ncrna-pred.com/HLRF.htm.
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Naik, Anil S. "Text and Voice Based Emotion Monitoring System." Oriental journal of computer science and technology 12, no. 4 (February 13, 2020): 194–200. http://dx.doi.org/10.13005/ojcst12.04.05.
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An Emotion monitoring system for a call-center is proposed. It aims to simplify the tracking and management of emotions extracted from call center Employee-Customer conversations. The system is composed of four modules: Emotion Detection, Emotion Analysis and Report Generation, Database Manager, and User Interface. The Emotion Detection module uses Tone Analyzer to extract them for reliable emotion; it also performs the Utterance Analysis for detecting emotion. The 14 emotions detected by the tone analyzer are happy, joy, anger, sad and neutral, etc. The Emotion Analysis module performs classification into the 3 categories: Neutral, Anger and Joy. By using this category, it applies the point-scoring technique for calculating the Employee Score. This module also polishes the output of the Emotion Detection module to provide a more presentable output of a sequence of emotions of the Employee and the Customer. The Database Manager is responsible for the management of the database wherein it handles the creation, and update of data. The Interface module serves as the view and user interface for the whole system. The system is comprised of an Android application for conversation and a web application to view reports. The Android application was developed using Android Studio to maintain the modularity and flexibility of the system. The local server monitors the conversation, it displays the detected emotions of both the Customer and the Employee. On the other hand, the web application was constructed using the Django Framework to maintain its modularity and abstraction by using a model. It provides reports and analysis of the emotions expressed by the customer during conversations. Using the Model View Template (MVT) approach, the Emotion monitoring system is scalable, reusable and modular.
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Wang, Yishu, Dejie Yang, and Minghua Deng. "Low-Rank and Sparse Matrix Decomposition for Genetic Interaction Data." BioMed Research International 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/573956.
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Background. Epistatic miniarray profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms. One approach to analyze EMAP data is to identify gene modules with densely interacting genes. In addition, genetic interaction score (Sscore) reflects the degree of synergizing or mitigating effect of two mutants, which is also informative. Statistical approaches that exploit both modularity and the pairwise interactions may provide more insight into the underlying biology. However, the high missing rate in EMAP data hinders the development of such approaches. To address the above problem, we adopted the matrix decomposition methodology “low-rank and sparse decomposition” (LRSDec) to decompose EMAP data matrix into low-rank part and sparse part.Results. LRSDec has been demonstrated as an effective technique for analyzing EMAP data. We applied a synthetic dataset and an EMAP dataset studying RNA-related processes inSaccharomyces cerevisiae. Global views of the genetic cross talk between different RNA-related protein complexes and processes have been structured, and novel functions of genes have been predicted.
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Rachwał, Alicja, Emilia Popławska, Izolda Gorgol, Tomasz Cieplak, Damian Pliszczuk, Łukasz Skowron, and Tomasz Rymarczyk. "Determining the Quality of a Dataset in Clustering Terms." Applied Sciences 13, no. 5 (February 24, 2023): 2942. http://dx.doi.org/10.3390/app13052942.
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The purpose of the theoretical considerations and research conducted was to indicate the instruments with which the quality of a dataset can be verified for the segmentation of observations occurring in the dataset. The paper proposes a novel way to deal with mixed datasets containing categorical and continuous attributes in a customer segmentation task. The categorical variables were embedded using an innovative unsupervised model based on an autoencoder. The customers were then divided into groups using different clustering algorithms, based on similarity matrices. In addition to the classic k-means method and the more modern DBSCAN, three graph algorithms were used: the Louvain algorithm, the greedy algorithm and the label propagation algorithm. The research was conducted on two datasets: one containing on retail customers and the other containing wholesale customers. The Calinski–Harabasz index, Davies–Bouldins index, NMI index, Fowlkes–Mallows index and silhouette score were used to assess the quality of the clustering. It was noted that the modularity parameter for graph methods was a good indicator of whether a given set could be meaningfully divided into groups.
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Vanbiervliet, Jens, and Jan F. A. Somers. "Excellent Mid-Term Results of a New Polished Tapered Modular Cemented Stem: A Study of 113 Hip Replacements with Minimum 5-Year Follow-Up." HIP International 27, no. 5 (February 18, 2017): 455–59. http://dx.doi.org/10.5301/hipint.5000486.
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Background This prospective cohort study reports the medium-term clinical and radiographic results of 113 hips treated with a hybrid total hip replacement (THR) with a new cemented tapered cobalt-chrome (Co-Cr) stem with a titanium (Ti) modular neck (ProfemurXm®). Method Between October 2008 and December 2010 we performed 115 consecutive hybrid THR with the ProfemurXm® in 105 patients. Results Survivorship of the implant (stem and modular neck) at a mean of 6.5 years (min 5-max 8) was 100% with the endpoint revision for any reason. No implant was at risk for revision or showed signs of loosening. The mean Harris Hip Score was 89/100, mean Oxford Hip Score was 43/48, mean WOMAC was 91/100. No patient had thigh pain, no patient reported squeaking. There were no dislocations in this cohort. No implant showed development of radiolucent lines (RLL), either at the stem-cement or cement-bone interface. No hip showed osteolysis or calcar resorption. The mean femoral subsidence of the stem within the cement mantel was 0.31 mm (range 0-0.6 mm) after 6.5 years. With the use of this modular stem, 93% of hips showed no measurable leg length difference after THR, and leg length could be restored within a 5-mm limit in 99% of hips. Conclusions The mid-term results of this new polished stem were excellent, without adverse effects from the use of modularity.
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Munegato, Daniele, Agostino Dario Caminita, Romeo Sotiri, Andrea Rossi, Marco Bigoni, and Giovanni Zatti. "Femoral revision in periprosthetic fractures using a titanium modular fluted tapered stem: mortality and clinical and radiological outcomes." HIP International 30, no. 2_suppl (December 2020): 101–7. http://dx.doi.org/10.1177/1120700020971313.
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Introduction: The aim of the study was to evaluate the mortality and the outcomes of femoral revision in periprosthetic femoral fractures (PFF) using a titanium modular fluted tapered stem. Methods: We retrospectively reviewed femoral revisions performed for PFF with a titanium modular fluted tapered stem in 25 patients. The average age was 74.32 years. In 21 patients the type of fracture was Vancouver B2 and type B3 in 4 cases. The mean follow-up was 29.16 (0.83–104.1) months SD ± 29.49. Results: The 30-day and 1-year mortality were 8% and 12% respectively. The mean value of the Charlson Comorbidity Index was 4.8 and the mean value of ASA score was 2.4. The average time to surgery from admittance was 5.45 days with 20% of patients operated on within 48 hours. A significant correlation was found between ASA score and mortality. The mean HHS was 76 points; good-excellent results were recorded in 56% of patients. 72% had no pain and 28% had slight pain (VAS 1–3). 52% of patients were able to walk without pain, limp or aids. The mean implant subsidence was 1.57 mm and superior to 5 mm in 2 cases. As complications we recorded 4 implant dislocations (16%) and 2 infections. Reoperation rate was 12%. Conclusions: Femoral revision with a modular fluted tapered stem in patients with periprosthetic fractures results in good clinical and radiological outcomes. However, mortality remains high and, despite the modularity design, dislocation is the most frequent complication.
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Wang, Tiantian, Djoko Budi Santoso, Kechao Wang, and Xiaohong Su. "Automatic Grading for Complex Multifile Programs." Complexity 2020 (June 13, 2020): 1–15. http://dx.doi.org/10.1155/2020/3279053.
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This paper presents an automatic grading method DGRADER, which handles complex multifile programs. Both the dynamic and the static grading support multifile program analysis. So, it can be an advantage to handle complex programming problem which requires more than one program file. Dynamic analysis takes advantage of object file linker in compilation to link complex multifile program. The static grading module consists of the following steps. Firstly, the program is parsed into abstract syntax tree, which is mapped into abstract syntax tree data map. Then, the information of preprocessor is used for linking external sources called in main program by complex multifile program linker-fusion algorithm. Next, standardization process is performed for problematic code removal, unused function removal, and function sequence ordering based on function call. Finally, program matching successfully tackles structure variance problem by previous standardization process and by simple tree matching using tag classifier. The novelty of the approach is that it handles complex multifile program analysis with flexible grading with consideration of modularity and big scale of programming problem complexity. The results have shown improvement in grading precision which gives reliable grading score delivered with intuitive system.
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Eddahmani, Ikram, Chi-Hieu Pham, Thibault Napoléon, Isabelle Badoc, Jean-Rassaire Fouefack, and Marwa El-Bouz. "Unsupervised Learning of Disentangled Representation via Auto-Encoding: A Survey." Sensors 23, no. 4 (February 20, 2023): 2362. http://dx.doi.org/10.3390/s23042362.
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In recent years, the rapid development of deep learning approaches has paved the way to explore the underlying factors that explain the data. In particular, several methods have been proposed to learn to identify and disentangle these underlying explanatory factors in order to improve the learning process and model generalization. However, extracting this representation with little or no supervision remains a key challenge in machine learning. In this paper, we provide a theoretical outlook on recent advances in the field of unsupervised representation learning with a focus on auto-encoding-based approaches and on the most well-known supervised disentanglement metrics. We cover the current state-of-the-art methods for learning disentangled representation in an unsupervised manner while pointing out the connection between each method and its added value on disentanglement. Further, we discuss how to quantify disentanglement and present an in-depth analysis of associated metrics. We conclude by carrying out a comparative evaluation of these metrics according to three criteria, (i) modularity, (ii) compactness and (iii) informativeness. Finally, we show that only the Mutual Information Gap score (MIG) meets all three criteria.
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Mall, Raghvendra, Ehsan Ullah, Khalid Kunji, Michele Ceccarelli, and Halima Bensmail. "An unsupervised disease module identification technique in biological networks using novel quality metric based on connectivity, conductance and modularity." F1000Research 7 (March 26, 2018): 378. http://dx.doi.org/10.12688/f1000research.14258.1.
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Disease processes are usually driven by several genes interacting in molecular modules or pathways leading to the disease. The identification of such modules in gene or protein networks is the core of computational methods in biomedical research. With this pretext, the Disease Module Identification (DMI) DREAM Challenge was initiated as an effort to systematically assess module identification methods on a panel of 6 diverse genomic networks. In this paper, we propose a generic refinement method based on ideas of merging and splitting the hierarchical tree obtained from any community detection technique for constrained DMI in biological networks. The only constraint was that size of community is in the range [3, 100]. We propose a novel model evaluation metric, called F-score, computed from several unsupervised quality metrics like modularity, conductance and connectivity to determine the quality of a graph partition at given level of hierarchy. We also propose a quality measure, namely Inverse Confidence, which ranks and prune insignificant modules to obtain a curated list of candidate disease modules (DM) for biological network. The predicted modules are evaluated on the basis of the total number of unique candidate modules that are associated with complex traits and diseases from over 200 genome-wide association study (GWAS) datasets. During the competition, we identified 42 modules, ranking 15th at the official false detection rate (FDR) cut-off of 0.05 for identifying statistically significant DM in the 6 benchmark networks. However, for stringent FDR cut-offs 0.025 and 0.01, the proposed method identified 31 (rank 9) and 16 DMIs (rank 10) respectively. From additional analysis, our proposed approach detected a total of 44 DM in the networks in comparison to 60 for the winner of DREAM Challenge. Interestingly, for several individual benchmark networks, our performance was better or competitive with the winner.
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Komarova, A., A. Kiselnikov, M. Yurlova, E. Slovenko, I. Tan, D. Mitiureva, P. Kabanova, E. Terlichenko, V. Zubko, and E. Shcherbakova. "Resting-state EEG networks characterized by intramodular and global hyperconnectivity in depressive sample." European Psychiatry 65, S1 (June 2022): S213—S214. http://dx.doi.org/10.1192/j.eurpsy.2022.557.
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Introduction Depression is characterized by a pattern of specific changes in the network organization of brain functioning. Objectives We researched a graph structure specificity in a depressive student sample by analyzing resting-state EEG. All possible combinations of graph metrics, frequency bands, and sensors/sources levels of networks were examined. Methods We recorded resting-state EEG in fourteen participants with high Beck Depression Inventory score (24.4 ± 9.7; 20.4 ± 1.5 y.o.; 14 females; 1 left-handed) and fourteen participants with a low score (6.8 ± 3.7; 21.3 ± 2.0 y.o.; 8 females; 1 left-handed). We applied weighted phase-lag index (wPLI) to construct functional networks at sensors and sources levels and computed characteristic path length (CPL), clustering coefficient (CC), index of modularity (Q), small-world index (SWI) in 4-8, 8-13, 13-30, and 4-30 Hz frequency bands. We used Mann-Whitney U-test (p < 0.05) to investigate between-group differences in the graph metrics. Results The depressive sample was characterized by increased CC and Q in the 4-30 Hz band networks and decreased CPL in the beta-band network (sensors-level for CPL and CC, and sources-level for Q). Conclusions Elevated CC and Q may relate to an increase of intramodular connectivity, and CPL reduction reflects the global connectivity increasing. We hypothesize that intramodular hyperconnectivity could explain the rise of global functional connectivity in participants with depressive symptoms. Funding: This research has been supported by the Interdisciplinary Scientific and Educational School of Lomonosov Moscow State University ‘Brain, Cognitive Systems, Artificial Intelligence’. Disclosure No significant relationships.
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Alami Machichi, Mouad, Loubna El Mansouri, Yasmina Imani, Omar Bourja, Rachid Hadria, Ouiam Lahlou, Samir Benmansour, Yahya Zennayi, and François Bourzeix. "CerealNet: A Hybrid Deep Learning Architecture for Cereal Crop Mapping Using Sentinel-2 Time-Series." Informatics 9, no. 4 (November 30, 2022): 96. http://dx.doi.org/10.3390/informatics9040096.
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Remote sensing-based crop mapping has continued to grow in economic importance over the last two decades. Given the ever-increasing rate of population growth and the implications of multiplying global food production, the necessity for timely, accurate, and reliable agricultural data is of the utmost importance. When it comes to ensuring high accuracy in crop maps, spectral similarities between crops represent serious limiting factors. Crops that display similar spectral responses are notorious for being nearly impossible to discriminate using classical multi-spectral imagery analysis. Chief among these crops are soft wheat, durum wheat, oats, and barley. In this paper, we propose a unique multi-input deep learning approach for cereal crop mapping, called “CerealNet”. Two time-series used as input, from the Sentinel-2 bands and NDVI (Normalized Difference Vegetation Index), were fed into separate branches of the LSTM-Conv1D (Long Short-Term Memory Convolutional Neural Networks) model to extract the temporal and spectral features necessary for the pixel-based crop mapping. The approach was evaluated using ground-truth data collected in the Gharb region (northwest of Morocco). We noted a categorical accuracy and an F1-score of 95% and 94%, respectively, with minimal confusion between the four cereal classes. CerealNet proved insensitive to sample size, as the least-represented crop, oats, had the highest F1-score. This model was compared with several state-of-the-art crop mapping classifiers and was found to outperform them. The modularity of CerealNet could possibly allow for injecting additional data such as Synthetic Aperture Radar (SAR) bands, especially when optical imagery is not available.
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Lieberman, J. R. "METAL ARTIFACT REDUCTION SEQUENCE MRI ABNORMALITIES IN ASYMPTOMATIC PATIENTS WITH DUAL MOBILITY HIP PROSTHESES." Orthopaedic Proceedings 105-B, SUPP_12 (June 23, 2023): 45. http://dx.doi.org/10.1302/1358-992x.2023.12.045.
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Modular dual mobility (DM) articulations are increasingly utilized during total hip arthroplasty (THA). However, concerns remain regarding the metal liner modularity. This study aims to correlate metal artifact reduction sequence (MARS) magnetic resonance imaging (MRI) abnormalities with serum metal ion levels in patients with DM articulations.All patients with an asymptomatic, primary THA and DM articulation with >2-year follow-up underwent MARS-MRI of the operative hip. Each patient had serum cobalt, chromium, and titanium levels drawn. Patient satisfaction, Oxford Hip Score, and Forgotten Joint Score-12 (FJS-12) were collected. Each MARS-MRI was independently reviewed by fellowship-trained musculoskeletal radiologists blinded to serum ion levels.Forty-five patients (50 hips) with a modular DM articulation were included with average follow-up of 3.7±1.2 years. Two patients (4.4%) had abnormal periprosthetic fluid collections on MARS-MRI with cobalt levels >3.0 μg/L. Four patients (8.9%) had MARS-MRI findings consistent with greater trochanteric bursitis, all with cobalt levels < 1.0 μg/L. A seventh patient had a periprosthetic fluid collection with normal ion levels. Of the 38 patients without MARS-MRI abnormalities, 37 (97.4%) had cobalt levels <1.0 μg/L, while one (2.6%) had a cobalt level of 1.4 μg/L. One patient (2.2%) had a chromium level >3.0 μg/L and a periprosthetic fluid collection. Of the 41 patients with titanium levels, five (12.2%) had titanium levels >5.0 μg/L without associated MARS-MRI abnormalities.Periprosthetic fluid collections associated with elevated serum cobalt levels in patients with asymptomatic dual mobility articulations occur infrequently (4.4%), but further assessment of these patients is necessary.Level of Evidence: Level IV
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Iacobescu, G., R. Ene, A. Cursaru, D. Anghelescu, and C. Cîrstoiu. "Long-Term Follow-Up in Tumoral Arthroplasty." Romanian Journal of Orthopaedic Surgery and Traumatology 1, Supplement (June 1, 2018): 54. http://dx.doi.org/10.2478/rojost-2018-0065.
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Abstract Introduction. Efficient management of a segmental resection or major osteolysis in the distal femur secondary to a tumor formation remains a controversial problem. Available options include the use of a modular or customized megaprosthetic implant. Modularity allows versatility for reconstruction and avoids the delay required to make a customized implant. Hypothesis and type of study. Performing a clinical and radiological retrospective study that aims to evaluate long-term efficiency in the use of megaprostheses in segmental distal femur resections. Elaboration of patient selection criteria for modular prosthesis. Materials and methods. We followed retrospectively 33 patients for 5 years from the time of the first surgery. We evaluated the implant stability, the late complications rate, and the long-term functional recovery of patients with distal femoral tumors who underwent segmental resections and subsequently reconstructive arthroplasty. Results. Thirty of the 33 patients maintained a mobile knee joint. An intermediate staging was performed at 30 months, which determined tumor recurrence in 2 patients, aseptic degradation of the components in 3 of them, and septic degradation in two of the evaluated cases. Because a tumoral recurrence occurred on the 45th month, the need for amputation of the prosthetic limb was imposed. The degradation of the polyethylene component (in 5 cases) was observed in the 5-year assessment. The functional results were excellent with the Musculoskeletal Tumor Society Score of 88% and a Toronto Extremity Severity Scale Score of 94%. Conclusions. Patients with distal femoral bone tumors undergoing modular reconstruction prosthetic arthroplasty have excellent functional results with retaining the affected limb and knee mobility. There was a close correlation between correctly applying the selection criteria for patients undergoing prosthesis intervention and functional recovery results.
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Tamagusko, Tiago, Matheus Gomes Correia, Luís Rita, Tudor-Codrin Bostan, Miguel Peliteiro, Rodrigo Martins, Luísa Santos, and Adelino Ferreira. "Data-Driven Approach for Urban Micromobility Enhancement through Safety Mapping and Intelligent Route Planning." Smart Cities 6, no. 4 (August 17, 2023): 2035–56. http://dx.doi.org/10.3390/smartcities6040094.
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Micromobility responds to urban transport challenges by reducing emissions, mitigating traffic, and improving accessibility. Nevertheless, the safety of micromobility users, particularly cyclists, remains a concern in urban environments. This study aims to construct a safety map and a risk-averse routing system for micromobility users in diverse urban environments, as exemplified by a case study in Lisbon. A data-driven methodology uses object detection algorithms and image segmentation techniques to identify potential risk factors on cycling routes from Google Street View images. The ‘Bikeable’ Multilayer Perceptron neural network measures these risks, assigning safety scores to each image. The method analyzed 5321 points across 24 parishes in Lisbon, with an average safety score of 4.5, indicating a generally safe environment for cyclists. Carnide emerged as the safest area, while Alcântara exhibited a higher level of potential risks. Additionally, an equation is proposed to compute route efficiency, enabling comparisons between different routes for identical origin-destination pairs. Preliminary findings suggest that the presented routing solution exhibits higher efficiency than the commercial routing benchmark. Risk-averse routes did not result in a substantial rise in travel distance or time, with increments of 7% on average. The study also contributed to increasing the existing amount of cycle path data in Lisbon by 12%, correcting inaccuracies, and updating the network in OpenStreetMap, providing access to more precise information and, consequently, more routes. The key contributions of this study, such as the safety map and risk-averse router, underscore the potential of data-driven tools for boosting urban micromobility. The solutions proposed demonstrate modularity and adaptability, making them fit for a range of urban scenarios and highlighting their value for cities prioritizing safe, sustainable urban mobility.
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Asmi, Khawla, Dounia Lotfi, and Mohamed El Marraki. "Overlapping community detection based on the union of all maximum spanning trees." Library Hi Tech 38, no. 2 (January 8, 2020): 276–92. http://dx.doi.org/10.1108/lht-01-2019-0003.
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Purpose The state-of-the-art methods designed for overlapping community detection are limited by their high execution time as in CPM or the need to provide some parameters like the number of communities in Bigclam and Nise_sph, which is a nontrivial information. Hence, there is a need to develop the accuracy that represents the primordial goal, where the actual state-of-the-art methods do not succeed to achieve high correspondence with the ground truth for many instances of networks. The paper aims to discuss this issue. Design/methodology/approach The authors offer a new method that explore the union of all maximum spanning trees (UMST) and models the strength of links between nodes. Also, each node in the UMST is linked with its most similar neighbor. From this model, the authors extract local community for each node, and then they combine the produced communities according to their number of shared nodes. Findings The experiments on eight real-world data sets and four sets of artificial networks show that the proposed method achieves obvious improvements over four state-of-the-art (BigClam, OSLOM, Demon, SE, DMST and ST) methods in terms of the F-score and ONMI for the networks with ground truth (Amazon, Youtube, LiveJournal and Orkut). Also, for the other networks, it provides communities with a good overlapping modularity. Originality/value In this paper, the authors investigate the UMST for the overlapping community detection.
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Meghanathan, Natarajan. "Complex Network Analysis of the Contiguous United States Graph." Computer and Information Science 10, no. 1 (February 5, 2017): 54. http://dx.doi.org/10.5539/cis.v10n1p54.
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We model the contiguous states (48 states and the District of Columbia) of the United States (US) as an undirected network graph with each state represented as a node and there is an edge between two nodes if the corresponding two states share a common border. We determine a ranking of the states in the US with respect to a suite of node-level metrics: the centrality metrics (degree, eigenvector, betweenness and closeness), eccentricity, maximal clique size, and local clustering coefficient. We propose a normalization-based approach to obtain a comprehensive centrality ranking of the vertices (that is most likely to be tie-free) encompassing the normalized values of the four centrality metrics. We have applied the proposed normalization-based approach on the US States graph to obtain a tie-free ranking of the vertices based on a comprehensive centrality score. We observe the state of Missouri to be the most central state with respect to all the four centrality metrics. We have also analyzed the US States graph with respect to a suite of network-level metrics: bipartivity index, assortativity index, modularity, size of the minimum connected dominating set, algebraic connectivity and degree metrics. The approach taken in this paper could be useful for several application domains: transportation networks (to identify central hubs), politics (to identify campaign venues with larger geographic coverage), cultural and electoral studies (to identify communities of states that are relatively proximal to each other) and etc.
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El Ghazawy, Ahmed K., Ayman Abdelaziz Bassiony, Haytham Abdelazim, and Saleh Gameel. "Acetabular revision using trabecular titanium (Delta TT) revision cups: A retrospective case series." SICOT-J 8 (2022): 49. http://dx.doi.org/10.1051/sicotj/2022049.
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Background: The annual rate of primary THA has been increasing with new designs promoting THA in the younger population, therefore increasing rates and complexity of hip revision surgeries. Different types of acetabular defects in hip revisions, usually make the use of primary cementless cups quite difficult. In complex defects, using cages with cemented cups or combining cementless cups with metal augments, are possible reconstruction solutions. The Delta TT acetabular revision system provides a solution to complex defects combining the advantages of both cage construct and primary implants, with modularity that helps restore anatomical hip centre and biomechanics. The aim of this study is to evaluate the short-term results of the use of the Delta TT revision system in acetabular revision surgeries. Type of the study: A retrospective case series. Methods: 24 patients underwent acetabular revision using (Delta TT) revision system, from 2018 to 2021. The mean follow-up was 20.75 months. Clinical and functional outcomes were assessed using Harris Hip Score. Results: The use of the Delta TT revision system in acetabular revision surgery provided adequate pain relief, and early patient mobilization. The preoperative HHS mean of 29.88 improved to a mean of 85.21, at the last, follow-up. None of the patients developed periprosthetic infection or loosening or nerve palsy during the follow-up period. Conclusion: Short-term clinical outcomes for the use of the Delta TT revision cup system in acetabular revision are encouraging with good functional outcomes and patient satisfaction.
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Arun, C. P. "Brain modeling - from electrical and electronic circuits and modules to objects and threads: Application to Tourette’s Syndrome." European Psychiatry 26, S2 (March 2011): 1191. http://dx.doi.org/10.1016/s0924-9338(11)72896-6.
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From the time of the discovery of electricity, scientists have actively borrowed ideas from technology to help understand brain function. The earliest ‘models’ depicted connexions between various parts of the nervous system as if they were electrical circuits. With the development of valve and transistor technology, excitatory, inhibitory circuits (and the actions of receptors and ligands) and neural loops came into fashion. Integrated Circuit (IC) hardware technology and modular software design (e.g. in languages such as C and FORTRAN) no doubt ushered in the move towards ‘modularity’. A review of the literature has revealed that multithreading, a key feature of modern software design has not been applied to modeling movement disorders. We aimed to model the clinical phenomena in Tourette’s Syndrome (TS) using multithreading. Using the programming language Java 6, we modelled motor and verbal tics as running on different threads. Tics were reproduced by impulsive generation of motivational threads for motor or verbal actions which were subject to voluntary control. Voluntary suppression was implemented using a ‘psychic threshold score’ which if exceeded allowed the motivational thread to initiate a motor thread which is turn was under some voluntary control. We find that from an internal representation standpoint (’software’) for the brain, motor and verbal tics are identical phenomena: only the peripheral manifestations are different. Various clinical phenomena such as tic suggestibility, suppression, release, etc can be demonstrated. Our experience of modeling TS leads us to recommend threads as the means to model other movement disorders and neuropsychiatric conditions.
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Fadhliani, Zed Zulkafli, Badronnisa Yusuf, and Siti Nurhidayu. "Assessment of Streamflow Simulation for a Tropical Forested Catchment Using Dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) Framework and Generalized Likelihood Uncertainty Estimation (GLUE)." Water 13, no. 3 (January 28, 2021): 317. http://dx.doi.org/10.3390/w13030317.
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Rainfall runoff modeling has been a subject of interest for decades due to a need to understand a catchment system for management, for example regarding extreme event occurrences such as flooding. Tropical catchments are particularly prone to the hazards of extreme precipitation and the internal drivers of change in the system, such as deforestation and land use change. A model framework of dynamic TOPMODEL, DECIPHeR v1—considering the flexibility, modularity, and portability—and Generalized Likelihood Uncertainty Estimation (GLUE) method are both used in this study. They reveal model performance for the streamflow simulation in a tropical catchment, i.e., the Kelantan River in Malaysia, that is prone to flooding and experiences high rates of land use change. Thirty-two years’ continuous simulation at a daily time scale simulation along with uncertainty analysis resulted in a Nash Sutcliffe Efficiency (NSE) score of 0.42 from the highest ranked parameter set, while 25.35% of the measurement falls within the uncertainty boundary based on a behavioral threshold NSE 0.3. The performance and behavior of the model in the continuous simulation suggests a limited ability of the model to represent the system, particularly along the low flow regime. In contrast, the simulation of eight peak flow events achieves moderate to good fit, with the four peak flow events simulation returning an NSE > 0.5. Nonetheless, the parameter scatter plot from both the continuous simulation and analyses of peak flow events indicate unidentifiability of all model parameters. This may be attributable to the catchment modeling scale. The results demand further investigation regarding the heterogeneity of parameters and calibration at multiple scales.
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Madden, Gregory, Matthew Bielskas, Methun Kamruzzaman, Parantapa Bhattacharya, Bryan Lewis, Eili Klein, Costi Sifri, and Anil Vullikanti. "173. Deciphering COVID-19-Associated Effects on Hospital MRSA Transmission and Social Networks." Open Forum Infectious Diseases 8, Supplement_1 (November 1, 2021): S104—S106. http://dx.doi.org/10.1093/ofid/ofab466.173.
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Abstract Background The COVID-19 pandemic was associated with a significant (28%) reduction of methicillin-resistant Staphylococcus aureus (MRSA) acquisition at UVA Hospital (P=0.016). This “natural experiment” allowed us to analyze 3 key mechanisms by which the pandemic may have influenced nosocomial transmission: 1) enhanced infection control measures (i.e., barrier precautions and hand hygiene), 2) patient-level risk factors, and 3) networks of healthcare personnel (HCP)-mediated contacts. Figure 1. Monthly MRSA Acquisition Rates Pre- and Post-COVD-19 Hospital MRSA acquisition was defined as a new clinical or surveillance positive in patients with prior unknown or negative MRSA status occurring >72h after admission. 10 month time periods pre- (5/6/2019 to 2/23/2020) and post-COVID-19 (5/4/2020 to 2/28/2021) were chosen to mitigate the effects of seasonality. A 6-week wash-in period was utilized coinciding with the onset of several major hospital-wide infection control measures (opening of 2 special pathogen units with universal contact/airborne precautions on 4/1/21 and 5/1/21, universal mask 4/10/21 and eye protection 4/20/20 policies instituted along with staff education efforts including the importance of standard precautions). Box and whisker plots depict quartile ranges, median (dotted line), and mean values. Mean MRSA acquisition rates pre- (0.92 events per 1,000 patient days) significantly declined post-COVD-19 (to 0.66; P=0.016). Independent-samples t tests were used (2-tailed) for statistical comparisons except for variables without a normal distribution (Shorr Scores), for which a Mann-Whitney U test was used. Methods Census-adjusted hospital-acquired MRSA acquisition events were analyzed over 10 months pre- (5/6/2019 to 2/23/2020) and post-COVD-19 (5/4/2020 to 2/28/2021), with a 6-week wash-in period coinciding with hospital-wide intensification of infection control measures (e.g., universal masking). HCP hand hygiene compliance rates were examined to reflect adherence to infection control practices. To examine impacts of non-infection control measures on MRSA transmission, we analyzed pre/post-COVD-19 differences in individual risk profiles for MRSA acquisition as well as a broad suite of properties of the hospital social network using person-location and person-person interactions inferred from the electronic medical record. Figure 2. Social Network Construction We constructed a contact network of hospitalized patients and staff at University of Virginia Hospital to analyze the properties of both person-location and person-person networks and their changes pre- and post-COVID-19. Colocation data (inferred from shared patient rooms and healthcare personnel (HCP)-patient interactions recorded in the electronic health record, e.g., medication administration) were used to construct contact networks, with nodes representing patients and HCP, and edges representing contacts. The above schematic shows how the temporal networks are inferred. In the figure, circles represent patients and the small filled squares represent HCP, while the larger rectangles represent patient rooms. The first room is a shared room with two patients. At each time step, co-location is inferred from the EMR data, which specifies interactions between HCP and patients. This can be represented as the temporal network (t) at the bottom. Results Hand hygiene compliance significantly improved post-COVD-19, in parallel with other infection control measures. Patient Shorr Scores (an index of individual MRSA risk) were statistically similar pre-/post-COVD-19. Analysis of various network properties demonstrated no trends to suggest a reduced outbreak threshold post-COVD-19. Figure 3. Hand Hygiene Compliance Rates Analysis of hospital-wide hand hygiene auditing data (anonymous auditors deployed to various units across UVA Hospital with an average 1,710 observations per month (range 340 - 7,187)) demonstrated a statistically significant (6%) improvement in average monthly hand hygiene compliance (86.9% pre- versus 93.1% post-COVD-19; P=0.008). Figure 4. Individual MRSA Risk Factors We calculated the Shorr Score (a validated tool to estimate individual risk for MRSA carriage in hospitalized patients; Shorr et al. Arch Intern Med. 2008;168(20):2205-10) for patients using data from the electronic health record to test the hypothesis that individual risk factors in aggregate did not change significantly in the post-COVD-19 period to explain changes in MRSA acquisition. Values for this score ranged from 0 to 10 with the following criteria: recent hospitalization (4), nursing home residence (3), hemodialysis (2), ICU admission (1). Pictured are frequency distributions of Shorr scores in the pre-COVID-19 and post-COVID-19 periods. The Mann-Whitney effect size (E), 0.53 (P=0.51), indicated that pre- and post-COVD-19 distributions were very similar. Figure 5. Social Network Properties and Analysis (Effect Size, P-Value) We analyzed three major types of network properties for this analysis: (1) Node properties of the pre- and post-COVID-19 networks consisted of all the edges in the pre- and post-COVID-19 periods, respectively. We considered a number of standard properties used in social network analysis to quantify opportunities for patient-patient transmission: degree centrality (links held by each node), betweenness centrality (times each node acts as the shortest ‘bridge’ between two other nodes), closeness centrality (how close each node is to other nodes in network), Eigenvector centrality (node’s relative influence on the network), and clustering coefficient (degree to which nodes cluster together) in the first five panels (left to right, top to bottom); (Newman, Networks: An Introduction, 2010). Each panel shows the frequency distributions of these properties. These properties generally did not have a normal distribution and therefore we used a Mann Whitney U test on random subsets of nodes in these networks to compare pre- and post-COVID properties. The mean effect size (E) and P-values are shown for each metric in parenthesis. We concluded that all of these pre- versus post-COVID-19 network properties were statistically similar. (2) Properties of the ego networks (networks induced by each node and its ‘one-hop’ neighbors). We considered density (average number of neighbors for each node; higher density generally favors lower outbreak threshold) and degree centrality (number of links held by each node) of ego networks (middle right and bottom left panels). The mean effect size and p-values using the Mann Whitney test are shown in parenthesis; there were no statistically significant differences in these properties in the pre- and post-COVID networks. (3) Aggregate properties of the weekly networks, consisting of all the interactions within a week. We considered modularity (measure of how the community structure differs from a random network; higher modularity means a stronger community structure and lower likelihood of transmission) and density (average number of neighbors each node; higher density generally favors lower outbreak threshold) of the weekly networks (bottom middle and bottom right panels). The modularity in the post-COVID weekly networks was slightly lower (i.e., it has a weaker community structure, and the network is more well mixed), while density was slightly higher, the differences of which were statistically significant; a caveat is that these are relatively small datasets (about 40 weeks). These differences (higher density, and better connectivity) both increase the risk of transmission in the post-COVID networks. In summary, the post-COVID networks either have similar properties as the pre-COVID networks, or had changes which are unlikely to have played a role in reducing MRSA transmission. Conclusion A significant reduction in post-COVD-19 MRSA transmission may have been an unintended positive effect of enhanced infection control measures, particularly hand hygiene and increased mask use. A modest (11.6%) post-COVD-19 reduction in surveillance testing may have also played a role. Despite pandemic-related cohorting and census fluctuations, most network properties were not significantly different post-COVID-19, except for aggregate density and modularity which varied in a direction that instead favored transmission; therefore, HCP-based networks did not play a significant role in reducing MRSA transmission. Multivariate modeling to isolate relative contributions of these factors is underway. Figure 6. Surveillance Testing and Clinical Culturing Post-COVD-19, there was a modest (11.6%) but statistically significant reduction in surveillance PCR testing (42.4 mean tests per 1,000 patient days pre- versus 37.5 post-COVD-19; P<0.002). There was not a statistically significant difference in rates of clinical cultures sent (2.48 cultures per 1,000 patient days pre- versus 2.23 post-COVD-19; P=0.288). Disclosures All Authors: No reported disclosures
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Yue, Qiuhai, Randi C. Martin, Simon Fischer-Baum, Aurora I. Ramos-Nuñez, Fengdan Ye, and Michael W. Deem. "Brain Modularity Mediates the Relation between Task Complexity and Performance." Journal of Cognitive Neuroscience 29, no. 9 (September 2017): 1532–46. http://dx.doi.org/10.1162/jocn_a_01142.
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Recent work in cognitive neuroscience has focused on analyzing the brain as a network, rather than as a collection of independent regions. Prior studies taking this approach have found that individual differences in the degree of modularity of the brain network relate to performance on cognitive tasks. However, inconsistent results concerning the direction of this relationship have been obtained, with some tasks showing better performance as modularity increases and other tasks showing worse performance. A recent theoretical model [Chen, M., & Deem, M. W. 2015. Development of modularity in the neural activity of children's brains. Physical Biology, 12, 016009] suggests that these inconsistencies may be explained on the grounds that high-modularity networks favor performance on simple tasks whereas low-modularity networks favor performance on more complex tasks. The current study tests these predictions by relating modularity from resting-state fMRI to performance on a set of simple and complex behavioral tasks. Complex and simple tasks were defined on the basis of whether they did or did not draw on executive attention. Consistent with predictions, we found a negative correlation between individuals' modularity and their performance on a composite measure combining scores from the complex tasks but a positive correlation with performance on a composite measure combining scores from the simple tasks. These results and theory presented here provide a framework for linking measures of whole-brain organization from network neuroscience to cognitive processing.
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Graceffa, Angelo, Pier Indelli, Leonardo Latella, Paolo Poli, Alexander Fulco, and Massimiliano Marcucci. "Clinical outcome of design modifications to the CLS Spotorno Stem in total hip replacement." Joints 04, no. 03 (July 2016): 134–41. http://dx.doi.org/10.11138/jts/2016.4.3.134.
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Purpose: historically, the original CLS Spotorno Stem has demonstrated excellent survival. The design of this stem was recently modified, resulting in the introduction of a shorter, modular version (CLS Brevius). The purpose of the current study was to evaluate the functional, radiological and survivorship outcomes of the cementless CLS Brevius Stem in a multi-surgeon, single center, consecutive series study at two years post-surgery. Methods: the Authors performed 170 total hip arthroplasties in 155 patients using the shorter, tripletaper stem design (CLS Brevius). The patients’ diagnoses were primary hip osteoarthritis (OA) in 74.4%, secondary hip OA in 22.6%, and post-traumatic hip OA in 3%. All operations were performed through a mini-posterior approach, with the patient in the lateral decubitus position. The mean follow-up was 32 months (24-44 months). Outcome was assessed using the Harris Hip Score (HHS). Results: the mean HHS improved from 32 preoperatively to 92 points at final follow-up, while the stem survival rate was 99.4%.Overall, the results were excellent in148 hips (87%), good in 14 hips (8.2%), fair in six hips (3.6%), and poor in two hips (1.2%). Intraoperative complications included a calcar fissure in three hips (1.7%). Correct femoral offset was reproduced in 97% while the planned center of hip rotation was achieved in 98%. Only one hip underwent early stem revision; this was due to major subsidence. Conclusions: the modified CLS stem design showed excellent short-term results with a low rate of early postoperative complications. One of the main findings of this study was the high correlation between the planned femoral offset and center of hip rotation and the final radiographic measurements. This high reproducibility, which indicates the ability of the system to restore normal hip anatomy, is indeed due to the extensive modularity that characterizes this stem system. Long-term follow-up studies are necessary to fully compare the outcomes of the new design with its highly successful predecessor. Level of Evidence: Level IV, therapeutic cases series.
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Beckett, Stephen J. "Improved community detection in weighted bipartite networks." Royal Society Open Science 3, no. 1 (January 2016): 140536. http://dx.doi.org/10.1098/rsos.140536.
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Real-world complex networks are composed of non-random quantitative interactions. Identifying communities of nodes that tend to interact more with each other than the network as a whole is a key research focus across multiple disciplines, yet many community detection algorithms only use information about the presence or absence of interactions between nodes. Weighted modularity is a potential method for evaluating the quality of community partitions in quantitative networks. In this framework, the optimal community partition of a network can be found by searching for the partition that maximizes modularity. Attempting to find the partition that maximizes modularity is a computationally hard problem requiring the use of algorithms. QuanBiMo is an algorithm that has been proposed to maximize weighted modularity in bipartite networks. This paper introduces two new algorithms, LPAwb+ and DIRTLPAwb+, for maximizing weighted modularity in bipartite networks. LPAwb+ and DIRTLPAwb+ robustly identify partitions with high modularity scores. DIRTLPAwb+ consistently matched or outperformed QuanBiMo, while the speed of LPAwb+ makes it an attractive choice for detecting the modularity of larger networks. Searching for modules using weighted data (rather than binary data) provides a different and potentially insightful method for evaluating network partitions.
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Serban, Maria. "Exploring modularity in biological networks." Philosophical Transactions of the Royal Society B: Biological Sciences 375, no. 1796 (February 24, 2020): 20190316. http://dx.doi.org/10.1098/rstb.2019.0316.
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Network theoretical approaches have shaped our understanding of many different kinds of biological modularity. This essay makes the case that to capture these contributions, it is useful to think about the role of network models in exploratory research. The overall point is that it is possible to provide a systematic analysis of the exploratory functions of network models in bioscientific research. Using two examples from molecular and developmental biology, I argue that often the same modelling approach can perform one or more exploratory functions, such as introducing new directions of research, offering a complementary set of concepts, methods and algorithms for individuating important features of natural phenomena, generating proofs of principle demonstrations and potential explanations for phenomena of interest and enlarging the scope of certain research agendas. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.
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ALBajjari, Ismael F. Hussain. "A Cognitive-Functionalist Interpretation of Modularity." Journal of Tikrit University for Humanities 27, no. 2 (April 22, 2020): 1–23. http://dx.doi.org/10.25130/jtuh.27.2.2020.23.
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Modularity has been the subject of intense debate in philosophy, psychology, and especially, in the cognitive sciences since the early 1980s, due to the publication of Fodor's ground-breaking book The Modularity of Mind (1983). However, in most cases of such modular views of the mind, misunderstandings have impeded an access to a more conceptually plausible account of modularity; a case that concerns us most here. This paper identifies the most striking arguments in the relevant literature, with special attention on the modularity argument proclaimed by J. Fodor (1983) that has been either limited in scope and depth, or misconceptualized by proponents. In particular, it reviews briefly the most modular assumptions made in this argument; those related to the cognitive architecture of the mind, and the perceptual-linguistic processes that are structured in terms of modules, or "organs". It is proposed here that modularity, clearly defined, may provide a useful framework for directing research works about human cognitive system, in general, and cognitive systematic processes of language use, in particular. Modularity might prove indispensable for understanding the structure of the mind, and offering insights into those mental mechanisms of human language processing as well. To that end, the paper, largely following the stance of Modern Massive Modularity, proposes a hierarchically cognitive-functionalist model of the modularity of mind, whose biggest claim is to argue that the architecture of the mind is more pervasively modular than the Fodorian perspective permits, and that the line of modularity might be drawn, not only up to the high-level systems responsible for thought, but also at the low-level systems (sub-systems) underlying perception and language.
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Helfat, Constance E., and Kathleen M. Eisenhardt. "Inter-temporal economies of scope, organizational modularity, and the dynamics of diversification." Strategic Management Journal 25, no. 13 (October 22, 2004): 1217–32. http://dx.doi.org/10.1002/smj.427.
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Fazelabdolabadi, Babak, and Mohammad Hossein Golestan. "Towards Bayesian Quantification of Permeability in Micro-scale Porous Structures – The Database of Micro Networks." HighTech and Innovation Journal 1, no. 4 (December 1, 2020): 148–60. http://dx.doi.org/10.28991/hij-2020-01-04-02.
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This article develops a Bayesian framework to quantify the absolute permeability of water in a porous structure from the geometry and clustering parameters of its underlying pore-throat network. These parameters include the network`s diameter, transivity, degree, centrality, assortativity, edge density, K-core decomposition, Kleinberg’s hub centrality scores, Kleinberg's authority centrality scores, length, and porosity. In addition, the incorporated clustering aspects of the networks have been determined with respect to several clustering criteria – edge betweenness, greedy optimization of modularity, multi-level optimization of modularity, and short random walks. As such, the article takes the first footsteps of creating a Database of Micro Networks for micro-scale porous structures, to be used as main input stream for the proposed Bayesian scheme. Doi: 10.28991/HIJ-2020-01-04-02 Full Text: PDF
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Janhunen, T., E. Oikarinen, H. Tompits, and S. Woltran. "Modularity Aspects of Disjunctive Stable Models." Journal of Artificial Intelligence Research 35 (August 28, 2009): 813–57. http://dx.doi.org/10.1613/jair.2810.
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Practically all programming languages allow the programmer to split a program into several modules which brings along several advantages in software development. In this paper, we are interested in the area of answer-set programming where fully declarative and nonmonotonic languages are applied. In this context, obtaining a modular structure for programs is by no means straightforward since the output of an entire program cannot in general be composed from the output of its components. To better understand the effects of disjunctive information on modularity we restrict the scope of analysis to the case of disjunctive logic programs (DLPs) subject to stable-model semantics. We define the notion of a DLP-function, where a well-defined input/output interface is provided, and establish a novel module theorem which indicates the compositionality of stable-model semantics for DLP-functions. The module theorem extends the well-known splitting-set theorem and enables the decomposition of DLP-functions given their strongly connected components based on positive dependencies induced by rules. In this setting, it is also possible to split shared disjunctive rules among components using a generalized shifting technique. The concept of modular equivalence is introduced for the mutual comparison of DLP-functions using a generalization of a translation-based verification method.
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Komarova, A., E. Shcherbakova, A. Kiselnikov, D. Mitiureva, M. Yurlova, P. Kabanova, E. Slovenko, E. Terlichenko, I. Tan, and V. Zubko. "Beta-band network modularity in resting-state EEG negatively correlates with level of intelligence." European Psychiatry 65, S1 (June 2022): S639. http://dx.doi.org/10.1192/j.eurpsy.2022.1639.
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Introduction Recent studies mostly focus on the links between measures of alpha-band EEG networks and intelligence. However, associations between wide frequency range EEG networks and general intelligence level remain underresearched. Objectives In this study in a student sample we aimed to correlate the intelligence level and graph metrics of the sensors/sources-level networks constructed in different frequency EEG bands. Methods We recorded eyes-closed resting-state EEG in 28 healthy participants (21.4±2.1 y.o., 18 females, 1 left-handed). The Raven’s Standard Progressive Matrices Plus (‘SPM Plus’, 60 figures) was used as an intelligence measure. We constructed networks for all possible combinations of sensors/sources-level and 4-8, 8-13, 13-30, or 4-30 Hz frequency bands using Weighted Phase-Lag Index (wPLI), and calculated four graph metrics (Characteristic Path Length, Clustering Coefficient, Modularity, and Small World Index) for each network. Spearman correlation (with Holm-Sidak correction) was applied to characterize the relations between the SPM Plus scores and all the network metrics. Results SPM Plus scores varied from 35 to 57 (mean 45.3±4.2), and the intelligence level negatively correlated with Modularity in beta-band (r = -0.63, pcorr = 0.0253). Conclusions High modularity may reflect relatively high segregation, but not integration, of networks (Girn, Mills, Christoff, 2019). Accordingly, our findings may shed light on the neural mechanisms of the general inefficiency of global cognitive processing in the case of intellectual decline related to different mental disorders. Funding: This research has been supported by the Interdisciplinary Scientific and Educational School of Lomonosov Moscow State University ‘Brain, Cognitive Systems, Artificial Intelligence’. Disclosure No significant relationships.
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Nacher, Jose C. "Community structure of non-coding RNA interaction network." Journal of Integrative Bioinformatics 10, no. 2 (June 1, 2013): 24–34. http://dx.doi.org/10.1515/jib-2013-217.
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Summary Rapid technological advances have shown that the ratio of non-protein coding genes rises to 98.5% in humans, suggesting that current knowledge on genetic information processing might be largely incomplete. It implies that protein-coding sequences only represent a small fraction of cellular transcriptional information. Here, we examine the community structure of the network defined by functional interactions between noncoding RNAs (ncRNAs) and proteins related bio-macrolecules (PRMs) using a two-fold approach: modularity in bipartite network and k-clique community detection. First, the high modularity scores as well as the distribution of community sizes showing a scaling-law revealed manifestly non-random features. Second, the k-clique sub-graphs and overlaps show that the identified communities of the ncRNA molecules of H. sapiens can potentially be associated with certain functions. These findings highlight the complex modular structure of ncRNA interactions and its possible regulatory roles in the cell.
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Singavi, Arun, Guangyu Chen, Nancy Wandersee, Collin Hubler, Amanda M. Brandow, Pippa Simpson, Shi-Jiang Li, and Joshua J. Field. "Daily Pain Is Associated with Alterations in Functional Connectivity of the Brain on fMRI in Adults with Sickle Cell Disease." Blood 128, no. 22 (December 2, 2016): 3656. http://dx.doi.org/10.1182/blood.v128.22.3656.3656.
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Abstract Background:One-third of adults with sickle cell disease (SCD) have daily, chronic pain. Despite the high prevalence of chronic pain in adults with SCD, the mechanism of is not well defined. In other chronic pain disorders, functional MRI (fMRI) demonstrates a re-organization of the brain's connectivity, which may be maladaptive and contribute to the development of a chronic pain syndrome. We performed fMRI in adults with SCD as well as age-matched controls in order to test two hypotheses: 1) functional connectivity is different between adults with SCD and controls, and 2) differences in functional connectivity among adults with SCD are associated with a more severe pain phenotype. Methods:We performed resting-state fMRI in adults with SCD and age-matched controls. Functional connectivity was calculated using two approaches: 1) a seed-voxel approach with the seed being periaqueductal gray (PAG), an area of the brain known to inhibit pain sensation, and 2) an inter-network functional connectivity strength (FCS) analysis, in which seven brain functional networks were selected based on previous brain modularity analysis findings. To calculate the inter-network FCS between networks A and B, the summation of all functional connectivities between two networks are used. Thereafter, the networks that were significantly different in FCS between SCD and controls were used to determine the association between altered functional connectivity and pain phenotype within SCD subjects. Pain phenotype measurements in SCD subjects included a day-of-study pain score, a 15-day diary to document daily pain and opioid use, McGill pain and Pain DETECT questionnaires, and quantitative sensory testing in response to mechanical, cold, and heat stimuli. Statistical analyses were performed using FSL and Matlab software. Results: A total of 27 adults were examined, including 13 with SCD (9 HbSS, 4 HbSC) and 14 age-matched controls. Seed-based functional connectivity analyses revealed significantly decreased connectivity in SCD as compared to controls between PAG and the regions involved in pain, sensation, salience, emotion, learning, and memory (temporal gyrus, anterior/posterior insula, parahippocampal gyrus, fusiform gyrus, precunes, posterior cingulate gyrus, anterior cingulate, subcallosal gyrus, paracentral gyrus, inferior/superior parietal lobe, inferior frontal gyrus and superior temporal gyrus) (P<0.001, t-test with AlphaSim correction). Furthermore, inter-network analyses show significantly decreased FCS in SCD as compared to controls among networks involved in salience, emotion, learning, and memory (between the salience network and the striatum network, between the salience network and the temporal network, and within both the salience network and the hippocampus network) (P<0.001, t-test). When these inter-network differences in FCS between SCD subjects and controls were examined within SCD subjects to determine the association with clinical phenotype, significant associations were found with age (rs=0.63, P<0.024, Spearman correlation analysis), SCD genotype (SS vs SC) (r2=0.43, P<0.016, linear regression analysis), and number of diary days with pain score >5 (r2=0.5, P<0.011, linear regression analysis). Conclusions: In adults with SCD compared to controls, there were differences in inter-network FCS, including the salience, striatum, temporal, and hippocampus networks, which are crucial networks for salience, emotion, learning, and memory. When these inter-network FCS differences were examined within adults with SCD, significant associations were found with age, SCD genotype and number of pain days. Taken together, these data suggest that altered connectivity in the brain of adults with SCD contributes to the development of a chronic pain syndrome. These changes in functional connectivity on fMRI could be used as a biomarker to determine the efficacy of interventions targeted to decrease chronic pain. Disclosures Field: NKT Therapeutics: Research Funding; Astellas Pharmaceuticals: Research Funding.
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Han, Kihwan, Sandra B. Chapman, and Daniel C. Krawczyk. "Cognitive Training Reorganizes Network Modularity in Traumatic Brain Injury." Neurorehabilitation and Neural Repair 34, no. 1 (August 22, 2019): 26–38. http://dx.doi.org/10.1177/1545968319868710.
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Background. Graph-theoretic approaches are increasingly popular for identifying the patterns of disrupted neural systems after traumatic brain injury (TBI). However, the patterns of neuroplasticity in brain organization after cognitive training in TBI are less well understood. Objective. We identified the patterns of training-induced neuroplasticity of the whole-brain network in TBI, using resting-state functional connectivity and graph theory. Methods. A total of 64 civilians and veterans with TBI were randomized into either a strategy-based cognitive training group (n = 33) or a knowledge-based training group (active control group; n = 31) for 8 weeks. The participants experienced mild to severe TBI without focal damage and persistent cognitive dysfunctions. A subset of participants complained of subclinical but residual psychiatric symptoms. We acquired their resting-state functional magnetic resonance imaging before training, immediately posttraining, and 3 months posttraining. From participants’ resting-state networks, we obtained the modularity, participation coefficient, within-module connectivity, global efficiency, and local efficiency over multiple network densities. We next performed longitudinal analyses on those measures corrected for multiple comparisons across network densities using false discovery rate (FDR). Results. Relative to the knowledge-based training group, the strategy-based cognitive training group had reduced modularity and increased participation coefficient, global efficiency, and local efficiency over time ( Pnodal < .05; qFDR < 0.05). Brain behavior analysis revealed that the participation coefficient and global efficiency within the strategy-based cognitive training group correlated with trail-making scores in the context of training ( Pnodal < .05; qFDR < 0.05). Conclusions. Cognitive training reorganized modular networks in TBI over the whole brain. Graph-theoretic approaches may be useful in identifying a potential brain-based marker of training efficacy in TBI.
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Hellström, Magnus. "Solution business models based on functional modularity – the case of complex capital goods." Journal of Service Management 25, no. 5 (October 14, 2014): 654–76. http://dx.doi.org/10.1108/josm-07-2013-0198.
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Purpose – The purpose of the paper is to outline a business model for product system solutions that is based on functional modularity. Design/methodology/approach – The paper employs an illustrative case study design. The cases constitute two action research projects in two organisations delivering complex product systems. Findings – The paper illustrates the viability of a movement towards a solution business model based on the use of modular networks and integrated sub-supplies for complex product systems. It develops the idea of suppliers assuming responsibility for larger functional wholes in the delivery of large capital goods. Research limitations/implications – Future research should strive to further validate and falsify the proposed model, and for other product systems too, with a particular focus on their operational phases. Practical implications – The paper outlines a business model based on modularity for both main suppliers and sub-suppliers that wish to extend their scope of delivery and to share responsibility with their networks. Originality/value – The proposed model is in considerable contrast to conventional models in which the main supplier typically assumes most of the responsibility itself.
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Harnos, Andrea, Péter Fehérvári, Imre Sándor Piross, Nóra Ágh, Zsolt Karcza, Krisztina Konrád, and Tibor Csörgő. "Exploratory analyses of migration timing and morphometrics of the Dunnock (Prunella modularis)." Ornis Hungarica 24, no. 2 (December 1, 2016): 127–44. http://dx.doi.org/10.1515/orhu-2016-0020.
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Abstract Ornithological studies often rely on large temporal scale ringing datasets as source of information. However, basic descriptive statistics of collected data are rarely provided. In order to fill this gap, here we present the second item of a series of exploratory analyses of migration timing and body size measurements of the most frequent Passerine species at a ringing station located in Central Hungary (1984–2015). First, we give a concise description of foreign ring recoveries of the Dunnock in relation to Hungary. We then shift focus to data of 11,617 individuals deriving from the ringing station, where birds have been trapped, handled and ringed with standardized methodology since 1984. Timing is described through annual and daily capture and recapture frequencies and their descriptive statistics. We show annual mean arrival dates within the study period and we present the cumulative distribution of first captures with stopover durations. We present the distributions of wing, third primary, tail length and body mass, and the annual means of these variables. Furthermore, we show the distribution of individual fat and muscle scores, and the distribution of body mass within each fat score category. We distinguish migration periods (spring and autumn), and age groups (i.e. juveniles and adults). Our aim is to provide a comprehensive overview of the analysed variables. However, we do not aim to interpret the obtained results, merely draw attention to interesting patterns, that may be worth exploring in detail. Data used here are available upon request for further analyses.
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Jeon, Gyuhyeon, and Juyong Park. "Impact of biased scores on ranking in bipartite competition networks and inference of modular structure via generalized modularity." EPL (Europhysics Letters) 117, no. 4 (February 1, 2017): 48005. http://dx.doi.org/10.1209/0295-5075/117/48005.
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Ma, Qing, Yanqing Tang, Fei Wang, Xuhong Liao, Xiaowei Jiang, Shengnan Wei, Andrea Mechelli, Yong He, and Mingrui Xia. "Transdiagnostic Dysfunctions in Brain Modules Across Patients with Schizophrenia, Bipolar Disorder, and Major Depressive Disorder: A Connectome-Based Study." Schizophrenia Bulletin 46, no. 3 (November 22, 2019): 699–712. http://dx.doi.org/10.1093/schbul/sbz111.
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Abstract Psychiatric disorders, including schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD), share clinical and neurobiological features. Because previous investigations of functional dysconnectivity have mainly focused on single disorders, the transdiagnostic alterations in the functional connectome architecture of the brain remain poorly understood. We collected resting-state functional magnetic resonance imaging data from 512 participants, including 121 with SCZ, 100 with BD, 108 with MDD, and 183 healthy controls. Individual functional brain connectomes were constructed in a voxelwise manner, and the modular architectures were examined at different scales, including (1) global modularity, (2) module-specific segregation and intra- and intermodular connections, and (3) nodal participation coefficients. The correlation of these modular measures with clinical scores was also examined. We reliably identify common alterations in modular organization in patients compared to controls, including (1) lower global modularity; (2) lower modular segregation in the frontoparietal, subcortical, visual, and sensorimotor modules driven by more intermodular connections; and (3) higher participation coefficients in several network connectors (the dorsolateral prefrontal cortex and angular gyrus) and the thalamus. Furthermore, the alterations in the SCZ group are more widespread than those of the BD and MDD groups and involve more intermodular connections, lower modular segregation and higher connector integrity. These alterations in modular organization significantly correlate with clinical scores in patients. This study demonstrates common hyper-integrated modular architectures of functional brain networks among patients with SCZ, BD, and MDD. These findings reveal a transdiagnostic mechanism of network dysfunction across psychiatric disorders from a connectomic perspective.
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Shen, Yao, and Michael Batty. "Delineating the perceived functional regions of London from commuting flows." Environment and Planning A: Economy and Space 51, no. 3 (July 12, 2018): 547–50. http://dx.doi.org/10.1177/0308518x18786253.
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Well-connected urban areas defined by different types of urban flow define the boundaries reflecting the relatedness of places in terms of their functionality. Yet, attempts to define cities and their communities based on aggregated data normally neglect the inherent differences between different groups of people. Based on the disaggregated flow data, this study detects community structures in the London Metropolitan Area perceived by different occupations by using the multi-level modularity optimisation algorithm. The results show the difference between our perceptions of different functional regions across occupations. The higher managerial groups have a more global sense than the lesser managerial occupations who have more segmented and local perceptions regarding their functional regions. This is well illustrated by the shift of the documented modularity scores across groups. Although the transport network and various natural boundaries do play a part in the locational patterns of the derived communities, it is found that the relative self-containment of functional regions is interpreted differently by different occupations. This kind of representation is of great value in advancing our knowledge regarding how different places are perceived by different occupations with all the implications for future commuting that future planning for housing and employment will bring.
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Kozulin, Alex, and Tziona Levi. "EFL Learning Potential: General or Modular?" Journal of Cognitive Education and Psychology 17, no. 1 (December 2018): 16–27. http://dx.doi.org/10.1891/1945-8959.17.1.16.
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Little attention has been paid to the question about generality versus modularity of the learning potential (LP). The main research question of our study was: Is the students’ LP established with the help of a dynamic assessment of their English as a foreign language (EFL) oral proficiency general enough to predict their subsequent EFL reading and writing scores? Eighty students (38 boys, 42 girls) received a dynamic assessment of their EFL oral proficiency in a pretest – mediation – posttest format. Six months later they took a standard EFL reading comprehension and writing exam. The results indicate that the correlations between oral LP scores and both reading (r = .42) and writing (r = .45) are significant and much stronger than the correlations with the static oral pretest. Oral LP appears to be general enough to predict students’ subsequent reading and writing achievements.
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Łazarz, Marcin. "An extension of F. Šik’s theorem on modular lattices." Mathematica Slovaca 68, no. 6 (December 19, 2018): 1321–26. http://dx.doi.org/10.1515/ms-2017-0182.
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AbstractJ. Jakubík noted in [JAKUBÍK, J.:Modular Lattice of Locally Finite Length, Acta Sci. Math.37(1975), 79–82] that F. Šik in the unpublished manuscript proved that in the class of upper semimodular lattices of locally finite length, modularity is equivalent to the lack of cover-preserving sublattices isomorphic toS7. In the present paper we extend the scope of Šik’s theorem to the class of upper semimodular, upper continuous and strongly atomic lattices. Moreover, we show that corresponding result of Jakubík from [JAKUBÍK, J.:Modular Lattice of Locally Finite Length, Acta Sci. Math.37(1975), 79–82] cannot be strengthened is analogous way.
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Wilsterman, Kathryn, Mattina M. Alonge, Darcy K. Ernst, Cody Limber, Lisa A. Treidel, and George E. Bentley. "Flexibility in an emergency life-history stage: acute food deprivation prevents sickness behaviour but not the immune response." Proceedings of the Royal Society B: Biological Sciences 287, no. 1929 (June 17, 2020): 20200842. http://dx.doi.org/10.1098/rspb.2020.0842.
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The emergency life-history stage (ELHS) can be divided into two subcategories that describe distinct, coordinated responses to disease- or non-disease-related physiological challenges. Whether an individual can simultaneously express aspects of both subcategories when faced with multiple challenges is poorly understood. Emergency life-history theory suggests that disease- and non-disease-related responses are coordinated at the level of the whole organism and therefore cannot be expressed simultaneously. However, the reactive scope and physiological regulatory network models suggest that traits can be independently regulated, allowing for components of both disease- and non-disease-related responses to be simultaneously expressed within a single organism. To test these ideas experimentally, we subjected female zebra finches to food deprivation, an immune challenge, both, or neither, and measured a suite of behavioural and physiological traits involved in the ELHS. We examined whether the trait values expressed by birds experiencing simultaneous challenges resembled trait values of birds experiencing a single challenge or if birds could express a mixture of trait values concurrently. We find that birds can respond to simultaneous challenges by regulating components of the behavioural and immune responses independently of one another. Modularity within these physio-behavioural networks adds additional dimensions to how we evaluate the intensity or quality of an ELHS. Whether modularity provides fitness advantages or costs in nature remains to be determined.