Similarity Threshold Research Articles

An adaptive remaining useful life prediction model for aeroengine based on multi-angle similarity

Similarity-based aeroengine Remaining Useful Life (RUL) prediction methods have long been limited by similarity evaluation rules. Therefore, this article proposes an advanced multi-angle engine similarity evaluation criterion based on the similarity of degradation mechanisms and process data. Firstly, a convolutional autoencoder is used to calculate the Health Index (HI) of the engines. Then, the encoder part is used to ensure that the similarity calculation is performed under the same fault pattern. Dynamic time warping based on sequence length correction is used to compute the global similarity between different HIs, which overcomes the limitations of different length. Finally, Gaussian process regression is used to predict the remaining useful life, and the Akaike information criterion is used to select the similarity threshold of the training data adaptively. This method ensures that the prediction model fully learns the fault evolution mechanism and degradation data characteristics, greatly improving the RUL prediction accuracy.

Deep semi-supervised clustering based on pairwise constraints and sample similarity

Semi-supervised clustering methods enhance the performance of completely unsupervised clustering tasks by incorporating pairwise relationship information from a subset of samples. However, previous methods based on pairwise constraints have struggled to effectively leverage available prior knowledge and fully exploit the similarity relationships between samples. To address these issues, this paper proposes an advanced approach called Deep Semi-Supervised Clustering based on Pairwise Constraints and Sample Similarity (DSCPS). Specifically, DSCPS consists of two training stages: the coarse clustering stage and the fine clustering stage. In the coarse clustering stage, DSCPS assigns probabilities to each sample indicating its belongingness to each cluster by computing the distance to the cluster centers. In the fine clustering stage, DSCPS introduces a predictor network in the clustering space to predict the class labels of each sample. Simultaneously, the thresholds for similarity and dissimilarity between samples are determined based on pairwise constraints. Finally, the encoder and prediction network are further optimized using sample similarity relationships and pairwise constraints as loss conditions, resulting in the final clustering results. Extensive experiments demonstrate that DSCPS outperforms state-of-the-art methods, achieving the highest performance.

SOINN Intrusion Detection Model Based on Three-Way Attribute Reduction

With a large number of intrusion detection datasets and high feature dimensionality, the emergent nature of new attack types makes it impossible to collect network traffic data all at once. The modified three-way attribute reduction method is combined with a Self-Organizing Incremental learning Neural Network (SOINN) algorithm to propose a self-organizing incremental neural network intrusion detection model based on three-way attribute reduction. Attribute importance is used to perform attribute reduction, and the data after attribute reduction are fed into a self-organized incremental learning neural network algorithm, which generalizes the topology of the original data through self-organized competitive learning. When the streaming data are transferred into the model, the inter-class insertion or node fusion operation is performed by comparing the inter-node distance and similarity threshold to achieve incremental learning of the model streaming data. The inter-node distance value is introduced into the weight update formulation to replace the traditional learning rate and to optimize the topological structure adjustment operation. The experimental results show that T-SOINN achieves high precision and recall when processing intrusion detection data.

On tuning parameters guiding similarity computations in a data deduplication pipeline for customers records: Experience from a R&D project

Data stored in information systems are often erroneous. Duplicate data are one of the typical error type. To discover and handle duplicates, the so-called deduplication methods are applied. They are complex and time costly algorithms. In data deduplication, pairs of records are compared and their similarities are computed. For a given deduplication problem, challenging tasks are: (1) to decide which similarity measures are the most adequate to given attributes being compared and (2) defining the importance of attributes being compared, and (3) defining adequate similarity thresholds between similar and not similar pairs of records. In this paper, we summarize our experience gained from a real R&D project run for a large financial institution. In particular, we answer the following three research questions: (1) what are the adequate similarity measures for comparing attributes of text data types, (2) what are the adequate weights of attributes in the procedure of comparing pairs of records, and (3) what are the similarity thresholds between classes: duplicates, probably duplicates, non-duplicates? The answers to the questions are based on the experimental evaluation of 54 similarity measures for text values. The measures were compared on five different real data sets of different data characteristic. The similarity measures were assessed based on: (1) similarity values they produced for given values being compared and (2) their execution time. Furthermore, we present our method, based on mathematical programming, for computing weights of attributes and similarity thresholds for records being compared. The experimental evaluation of the method and its assessment by experts from the financial institution proved that it is adequate to the deduplication problem at hand. The whole data deduplication pipeline that we have developed has been deployed in the financial institution and is run in their production system, processing batches of over 20 million of customer records.

Simulating auditory nerve fiber response following micro-electrode stimulation

Abstract The cochlear implant was the first effective and is still the most common neuroprosthetic device which is employed for people with severe to profound hearing loss. To restore auditory perception, an array of micro-electrodes that deliver electrical pulses to the auditory nerve is surgically implanted into the lower cochlea duct, the scala tympani (ST). However, implantation into the upper cavity, the scala vestibuli (SV) has been tested due to severe anatomical obstruction or ossification of the ST. Clinical results revealed similar performance and thresholds for SV and ST cochlear implant users. We present a simulation study of auditory nerve fibre response to monophasic stimulation of both polarities. Excitation profiles are compared for microelectrodes placed in the SV and equivalent positions in the ST. In total, 7 different electrode positions for 4 different fibres have been investigated in a homogenous 2D model. Results for the intact fibres predict generally higher anodic thresholds in comparison to cathodic stimulation at the same electrode position and mostly lower thresholds for the SV electrodes in comparison to their ST counterparts. In contrast, anodic thresholds are mostly lower than cathodic thresholds for the degenerated fibres. Furthermore, due to the increased electrode-fibre distance for degenerate fibres which have completely lost the dendrite, SV stimulation is less beneficial. However, for basal fibres and the clinically relevant mid scala placement of the electrode the typically high thresholds remain similar for ST and SV positions.

Inflation and Attention Thresholds

Abstract One of the dangers of high inflation is that it can cause individuals to pay close attention to it. This internalization of inflation can lead to an accelerationist regime, making inflation harder to control. We empirically assess the relationship between attention and inflation for 38 countries. Our measures of attention are constructed from internet search behavior or the popularity of inflation mentions on Twitter. We find evidence that attention thresholds exist for most of the countries and that they typically occur between 2-4% inflation. We also document interesting variability across countries and find similar thresholds in newspaper coverage of inflation.

1449. Evaluation of Cloud-Based Automated Analytic Pipelines for Whole Genome Sequencing Analysis of Multidrug-Resistant Gram-Negative Organism Outbreaks

Abstract Background Clinical implementation of whole genome sequencing (WGS) has been identified as the optimal approach to identify transmission of multidrug-resistant (MDR) organisms in the healthcare setting. Although WGS capabilities are becoming more common in clinical laboratories, analysis and interpretation of results is still a barrier to implementation. The purpose of this study was to evaluate cloud-based automated analytic pipelines to aid with WGS analysis of MDR gram-negative organisms as part of outbreak investigations. Methods A convenience set of MDR Enterobacterales (n: 142), Acinetobacter baumannii complex (n: 14) and Pseudomonas aeruginosa (n:29) isolates were WGS on Illumina sequencing platforms. The FASTQ files were uploaded to AREScloud (Ares Genetics, an OpGen group company) and to EPISEQ CS (bioMérieux) for whole genome or core genome multi-locus sequence typing (wgMLST/cgMLST) with dendrogram creation. Results were compared to traditional 7 gene MLST schema. Ease-of-use and additional tools were compared between pipelines. Results AREScloud and EPISEQ CS generated similar dendrograms for all organisms evaluated (examples provided in Figures 1 & 2). EPISEQ CS provided preset similarity thresholds based on percent relatedness to define clusters simplifying outbreak analysis, whereas AREScloud allowed users to define allelic difference cutoffs. Although the wgMLST/cgMLST clusters aligned with traditional MLST types, not all isolates assigned to a similar MLST were identified as part of a related cluster demonstrating increased resolution of wgMLST and cgMLST phylogenetic assessments. Table 1 compares the features between the two pipelines. Dendrogram generated for Escherichia coli (EPISEQ CS) Dendrogram generated for Escherichia coli (AREScloud) Comparison of features between EPISEQ CS and AREScloud for phylogenetic analysis Conclusion Both cloud-based tools perform comparably for assessing phylogenetic relatedness compared to traditional MLST. Preset similarity thresholds available with EPISEQ CS provide ease-of-use for cluster assessment. Additional features, such as resistome and virulome analysis, are available by both pipelines. Predictive WGS-AST is an additional asset available with AREScloud. Disclosures Karen C. Carroll, MD, Abbott Diagnostics: Board Member|Co-Diagnostics, Inc.: Board Member|Great Basin: Grant/Research Support|Meridian Diagnostics: Grant/Research Support|Pattern Diagnostics: Advisor/Consultant|Pattern Diagnostics: Stocks/Bonds|Qiagen, Inc.: Grant/Research Support|Scanogen: Advisor/Consultant|Scanogen: Grant/Research Support Patricia J. Simner, PhD, Affinity Biosensors: Grant/Research Support|BD Diagnostics: Advisor/Consultant|BD Diagnostics: Grant/Research Support|Entasis: Advisor/Consultant|GeneCapture: Stocks/Bonds|Merck: Advisor/Consultant|OpGen Inc: Board Member|OpGen Inc: Grant/Research Support|OpGen Inc: Honoraria|Qiagen Sciences Inc: Advisor/Consultant|Qiagen Sciences Inc: Grant/Research Support|Shionogi Inc: Advisor/Consultant|T2 Biosystems: Grant/Research Support

Feature-Based Place Recognition Using Forward-Looking Sonar

Some structures in the harbour environment need to be inspected regularly. However, these scenarios present a major challenge for the accurate estimation of a vehicle’s position and subsequent recognition of similar images. In these scenarios, visibility can be poor, making place recognition a difficult task as the visual appearance of a local feature can be compromised. Under these operating conditions, imaging sonars are a promising solution. The quality of the captured images is affected by some factors but they do not suffer from haze, which is an advantage. Therefore, a purely acoustic approach for unsupervised recognition of similar images based on forward-looking sonar (FLS) data is proposed to solve the perception problems in harbour facilities. To simplify the variation of environment parameters and sensor configurations, and given the need for online data for these applications, a harbour scenario was recreated using the Stonefish simulator. Therefore, experiments were conducted with preconfigured user trajectories to simulate inspections in the vicinity of structures. The place recognition approach performs better than the results obtained from optical images. The proposed method provides a good compromise in terms of distinctiveness, achieving 87.5% recall considering appropriate constraints and assumptions for this task given its impact on navigation success. That is, it is based on a similarity threshold of 0.3 and 12 consistent features to consider only effective loops. The behaviour of FLS is the same regardless of the environment conditions and thus this work opens new horizons for the use of these sensors as a great aid for underwater perception, namely, to avoid degradation of navigation performance in muddy conditions.

A singular shark bitter taste receptor provides insights into the evolution of bitter taste perception.

Many animal and plant species synthesize toxic compounds as deterrent; thus, detection of these compounds is of vital importance to avoid their ingestion. Often, such compounds are recognized by taste 2 receptors that mediate bitter taste in humans. Until now, bitter taste receptors have only been found in bony vertebrates, where they occur as a large family already in coelacanth, a "living fossil" and the earliest-diverging extant lobe-finned fish. Here, we have revisited the evolutionary origin of taste 2 receptors (T2Rs) making use of a multitude of recently available cartilaginous fish genomes. We have identified a singular T2R in 12 cartilaginous fish species (9 sharks, 1 sawfish, and 2 skates), which represents a sister clade to all bony fish T2Rs. We have examined its ligands for two shark species, a catshark and a bamboo shark. The ligand repertoire of bamboo shark represents a subset of that of the catshark, with roughly similar thresholds. Amarogentin, one of the most bitter natural substances for humans, also elicited the highest signal amplitudes with both shark receptors. Other subsets of ligands are shared with basal bony fish T2Rs indicating an astonishing degree of functional conservation over nearly 500 mya of separate evolution. Both shark receptors respond to endogenous steroids as well as xenobiotic compounds, whereas separate receptors exist for xenobiotics both in early- and late-derived bony vertebrates (coelacanth, zebrafish, and human), consistent with the shark T2R reflecting the original ligand repertoire of the ancestral bitter taste receptor at the evolutionary origin of this family.

Structure-property relationships in the electrical conductivity of multilayer graphene sheet/epoxy nanocomposites

The relationship between the physicochemical properties of multilayer graphene sheets (GSs), their resulting state of dispersion, and the effective electrical conductivity of GS/epoxy nanocomposites is investigated. To construct such relationship, four types of GSs with dissimilar properties were physicochemically, structurally, and morphologically characterized, and correlated with the direct current and alternating current (AC) electrical responses of their polymeric nanocomposites. It was found that the most influential parameters that affect the electrical conductivity of the nanocomposites are the size and density of agglomerates formed at the mesoscale. Both parameters, in turn, are governed by the physicochemical properties of the individual GSs. At the nanostructure level, the lateral size of the GS arises as the most influential parameter, with larger GSs yielding higher conductivities and lower percolation thresholds. The carbon/oxygen ratio of the GSs also showed moderate influence. The nanocomposites showed a resistive (R)-capacitive (C) electrical response in AC, which fits to a two-element parallel RC model. Both, the power law and general effective media (GEM) models predict similar percolation thresholds, but the GEM model is more general and adequately reproduces the whole electrical curve for all filler concentrations.

Abstract 17052: Are Sex-Specific Thresholds Needed for Donor-Derived Cell-Free DNA in Rejection Surveillance? An Analysis From the Genomic Research Alliance for Transplantation

Introduction: Non-invasive methods for surveillance of rejection are increasingly used among heart transplant (HT) recipients, including donor-derived cell-free DNA (dd-cfDNA). The purpose of the current study was to examine patterns of dd-cfDNA seen in quiescence and acute rejection in males and females. Hypothesis: As other cardiac biomarkers differ by sex, we hypothesized that there may be sex-specific differences in performance for detection of acute rejection. Methods: Patients previously enrolled in GRAfT who were ≥18 years at the time of HT were included. Rejection was defined by endomyocardial biopsy with ISHLT acute cellular rejection (ACR) grade ≥2R and/or antibody-mediated rejection ≥ pAMR 1. Median dd-cfDNA levels were compared between sexes during quiescence and rejection. The performance of dd-cfDNA by sex at or after Day 28 was assessed using AUROC. Allograft injury was defined as dd-cfDNA ≥0.25%. Results: After exclusions, 148 patients were included with 1109 dd-cfDNA measurements. Baseline characteristics including demographics and comorbidities were not significantly different between sexes. During quiescence, there were no significant sex differences in median dd-cfDNA (0.038% [IQR 0.001-0.167] in females vs 0.029% [IQR 0.001-0.125] in males, p = 0.21). There were no significant sex differences in median dd-cfDNA for ACR (0.33% [0.210-0.356] in females vs 0.32% [0.210-1.103] in males, p = 0.57). Overall, median dd-cfDNA levels were higher in AMR than ACR but did not significantly differ by sex (0.50% [IQR 0.180-0.820] in females vs 0.63% [IQR 0.316-1.945] in males, p = 0.51). Elevated dd-cfDNA detected ACR/AMR with an AUROC of 0.83 in females and 0.89 in males (Figure). Conclusions: There were no significant sex differences between dd-cfDNA levels during quiescence and rejection. Performance characteristics were similar, suggesting that similar thresholds can be used in men and women for rejection surveillance.

The Optimization of the ARP Poisoning Attack Detection Model Using a Similar Approach Based on NetFlow Analysis

Information security and threats are a concern in the cyber era. Attacks can be malicious activities. One of them is known as ARP poisoning attack activity, which attacks by falsifying a computer's identity through illegal access to retrieve confidential information in a target computer. Besides, it has also caused service deadlocks in the network. Previous studies have been introduced for the ARP Attack Detection model using rule-based and mining-based. Still, they cannot show optimal detection performance and obtain high false positive results. This paper proposed a detection model for ARP poisoning attacks using a similarity measurement approach adopting cosine similarity. The goal is to obtain measurements of host activities similar to ARP poisoning attacks. The experiment results showed that the model got an accuracy of 97.25%, recall of 96.43%, and precision of 81% with a similarity threshold value of 0.488. Comparison results with previous studies showed higher detection accuracy than previous studies and some classification methods. It shows that the model can improve intrusion detection performance and facilitate network administrators to analyze ARP poisoning attacks in computer networks.

Knowledge-based multiple point statistics for soil stratigraphy simulation

An accurate representation of subsurface stratigraphy in the form of geotechnical profiles is a prerequisite for optimal underground design and safe construction. This paper proposes a novel knowledge-based multiple point statistics (KMPS) method to simulate the heterogeneities and spatial trends of subsurface strata and to generate geotechnical profiles. This approach incorporates domain knowledge in the form of cone penetration test and standard penetration test data as well as results from laboratory testing to increase simulation accuracy. A novel varying search template size and similarity threshold is adopted to alleviate the computational cost of the simulations. Ablation analyses are used to quantify the improvement of the proposed KMPS algorithm over existing baseline multiple point statistics algorithms. By way of example, the proposed approach is applied to real-world exploration data from the Suzhou No.6 metroline project. The results show that the computational cost of KMPS is effectively minimized whilst the simulation accuracy can be improved by up to 13.3%. The algorithm is further validated by extending the simulation to a test image. With sparse exploration data, the KMPS method is shown to effectively and efficiently predict soil profiles based on prior experience embodied by a training image, thus enabling a reduction in the number of field exploration boreholes.

Non-stomatal processes are responsible for the decrease in gross primary production of a potato crop during edaphic drought

Soil water stress is one of the main constraints on agrosystem functioning, causing a reduction in gross primary production (GPP). It is a key factor for the selection of drought-tolerant plant varieties and the adaptation of irrigation management strategies. Therefore, identifying the physiological factors limiting GPP is crucial. Land surface models commonly implement a beta (β) stress function to reproduce the effects of drought on GPP. It is still unclear whether GPP limitations originate from a direct stomatal response (SOL) or from other non-stomatal causes (NSOL). Moreover, the shape and thresholds of the β function are a major cause of uncertainty in LSMs. This study investigated the effects of edaphic drought on GPP limitations of a potato crop by using eddy covariance data from the Lonzée ICOS station (BE-Lon) in Belgium for four years (2006, 2010, 2014 and 2018) and by calibrating the β function on NSOL and SOL.The decrease in the relative extractable water in the soil (REW) induced a decline in the apparent maximum carboxylation rate (NSOL) from similar REW thresholds for all growing seasons of potato crops. A sensitivity analysis showed that the non-inclusion of REW decrease on NSOL in the modeling of GPP led to important overestimations of carbon sequestration from a threshold corresponding to 46 % of the maximum available soil water. The stomatal sensitivity to photosynthesis (SOL) remained constant or even increased in 2010 and 2018. The carbon and water fluxes were decoupled when REW decreased, exhibiting a strict control of NSOL on hourly dynamics of GPP. This study provides REW thresholds to identify drought stress episodes and to help for designing irrigation management strategies. Our results advocate for a better representation of the influence of drought on photosynthesis processes of potato crops to improve the accuracy of model predictions during drought.

Diversity of Fish Species in relation to Climatological Fluctuations in a Coastal River of Bangladesh

In the Sandha river of Bangladesh, we investigated the temporal and geographical fluctuation in species of fish composition and diversity. The extent of our understanding of the fish variety in this river varies greatly on both a temporal and a geographical scale. From July 2021 to June 2022, fish specimens were gathered from five stations using various conventional fishing methods. During the study period, 5118 individuals from 67 species were collected which include 8 orders, 24 families, and 54 genera. Cypriniformes made up the majority of the order (32.84%), whereas Beloniformes and Tetraodontiformes made up the least number of species (1.49% each). Six species were listed as endangered (8.96%), seven as vulnerable (10.45%), nine as near threatened (13.43%), forty-three as least concern (64.18%), one with data deficient (1.49%), and one as not evaluated (1.49%) in Bangladesh. In order to quantify the temporal and geographical changes in community composition, diversity indices were calculated and put to use. Three (temperature, humidity, and rainfall) out of the four environmental factors (temperature, humidity, rainfall, and photoperiod) had a big impact on how species were distributed. At a similarity level of 79% and 75.5% separation, 2 substantial clusters were seen in the case of stations and 2 large clusters were detected in the case of months, respectively. However, at a similarity threshold of 20% separation, three different groups of fish species were found. Our research offers the most recent status data on fish distribution in the Sandha river. The knowledge acquired from this research is crucial for creating protection and management plans that will promote the long-term viability of fishery resources in the Sandha river and its nearby coastal tributaries.

A hybrid genetic algorithm for the min–max Multiple Traveling Salesman Problem

This paper proposes a hybrid genetic algorithm for solving the Multiple Traveling Salesman Problem (mTSP) to minimize the length of the longest tour. The genetic algorithm utilizes a TSP sequence as the representation of each individual, and a dynamic programming algorithm is employed to evaluate the individual and find the optimal mTSP solution for the given sequence of cities. A novel crossover operator is designed to combine similar tours from two parents and offers great diversity for the population. For some of the generated offspring, we detect and remove intersections between tours to obtain a solution with no intersections. This is particularly useful for the min–max mTSP. The generated offspring are also improved by a self-adaptive random local search and a thorough neighborhood search. Our algorithm outperforms all existing algorithms on average, with similar cutoff time thresholds, when tested against multiple benchmark sets found in the literature. Additionally, we improve the best-known solutions for 21 out of 89 instances on four benchmark sets.

Towards a scientific community consensus on designating Vulnerable Marine Ecosystems from imagery.

Management of deep-sea fisheries in areas beyond national jurisdiction by Regional Fisheries Management Organizations/Arrangements (RFMO/As) requires identification of areas with Vulnerable Marine Ecosystems (VMEs). Currently, fisheries data, including trawl and longline bycatch data, are used by many RFMO/As to inform the identification of VMEs. However, the collection of such data creates impacts and there is a need to collect non-invasive data for VME identification and monitoring purposes. Imagery data from scientific surveys satisfies this requirement, but there currently is no established framework for identifying VMEs from images. Thus, the goal of this study was to bring together a large international team to determine current VME assessment protocols and establish preliminary global consensus guidelines for identifying VMEs from images. An initial assessment showed a lack of consistency among RFMO/A regions regarding what is considered a VME indicator taxon, and hence variability in how VMEs might be defined. In certain cases, experts agreed that a VME could be identified from a single image, most often in areas of scleractinian reefs, dense octocoral gardens, multiple VME species' co-occurrence, and chemosynthetic ecosystems. A decision flow chart is presented that gives practical interpretation of the FAO criteria for single images. To further evaluate steps of the flow chart related to density, data were compiled to assess whether scientists perceived similar density thresholds across regions. The range of observed densities and the density values considered to be VMEs varied considerably by taxon, but in many cases, there was a statistical difference in what experts considered to be a VME compared to images not considered a VME. Further work is required to develop an areal extent index, to include a measure of confidence, and to increase our understanding of what levels of density and diversity correspond to key ecosystem functions for VME indicator taxa. Based on our results, the following recommendations are made: 1. There is a need to establish a global consensus on which taxa are VME indicators. 2. RFMO/As should consider adopting guidelines that use imagery surveys as an alternative (or complement) to using bycatch and trawl surveys for designating VMEs. 3. Imagery surveys should also be included in Impact Assessments. And 4. All industries that impact the seafloor, not just fisheries, should use imagery surveys to detect and identify VMEs.

Time series clustering based on normal cloud model and complex network

When data mining research is conducted, it is difficult to obtain precise domain knowledge to set a similarity threshold. Furthermore, noise and missing values are inevitable. Missing values and noise without pre-processing are challenges for many algorithms. A time-series clustering method is proposed based on the normal cloud model and complex networks. Matrix profile similarity measurement, normal cloud model generation and filtering, cloud model expectation curve weighting, degree centrality reweighting, and community discovery in complex networks are the five stages of the proposed clustering algorithm. Local features are considered, and the effects of missing values are reduced when performing similarity measurements. The normal cloud model can be used to set thresholds adaptively. The Louvain algorithm accomplishes the clustering task in complex networks without specifying the clusters. Experiments are conducted on 94 datasets and are compared with 8 clustering methods in the UCR time-series clustering benchmark study. Experimental results indicate that the proposed method can perform well on many datasets.

Defining Clinically Meaningful Cut Points for Leg Power Impairment Using Physical Performance in Older Adults: A Secondary Analysis From Boston RISE

ObjectiveTo identify clinically meaningful thresholds of leg power impairment identified by the stair climb power test (SCPT). DesignCross-sectional analysis using the baseline data from an observational cohort study. SettingThe Boston Rehabilitative Impairment Study of the Elderly. ParticipantsCommunity-dwelling older adults (N=413). Main Outcome MeasuresSCPT and the Short Physical Performance Battery (SPPB). ResultsUsing the receiver operating characteristic curves and Youden's J statistics, the optimal threshold for the SCPT associated with mobility limitation as defined by an SPPB score ≤9 was 3.07 Watts/kg for men with a sensitivity of 74%, a specificity of 73% and, an area under the curve (AUC) value of 0.78. For women, the optimal threshold was 2.59 Watts/kg with a sensitivity of 83%, a specificity of 69%, and an AUC value of 0.81. The classification and regression tree sensitivity analysis demonstrated similar thresholds, 2.88 Watts/kg and 2.53 Watts/kg for men and women, respectively. ConclusionsThe study identified clinically meaningful thresholds of impairment for the SCPT for mobility limited older primary care patients. These thresholds may be used to inform rehabilitation care to improve functional mobility of older adults and should be validated in larger more representative clinical trials.

A multilayered graph-based framework to explore behavioural phenomena in social media conversations

ObjectiveSocial media is part of current health communications. This research aims to delve into the effects of social contagion, biased assimilation, and homophily in building and changing health opinions on social media. Materials and methodsConversations about COVID-19 vaccination on English and Spanish Twitter are the case studies. A new multilayered graph-based framework supports the integrated analysis of content similarity within and across posts, users, and conversations to interpret contrasting and confluent user stances. Deep learning models are applied to infer stance. Graph centrality and homophily scores support the interpretation of information reproduction. ResultsThe results show that semantically related English posts tend to present a similar stance about COVID-19 vaccination (rstance = 0.51) whereas Spanish posts are more heterophilic (rstance = 0.38). Neither case showed evidence of homophily regarding user influence or vaccine hashtags. Graph filters for Pfizer and Astrazeneca with a similarity threshold of 0.85 show stance homophily in English scenarios (i.e. rstance = 0.45 and rstance = 0.58, respectively) and small homophily in Spanish scenarios (i.e. r = 0.12 and r = 0.3, respectively). Highly connected users are a minority and are not socially influential. Spanish conversations showed stance homophily, i.e. most of the connected conversations promote vaccination (rstance = 0.42), whereas English conversations are more likely to offer contrasting stances. ConclusionThe methodology proposed for quantifying the impact of natural and intentional social behaviours in health information reproduction can be applied to any of the main social platforms and any given topic of conversation. Its effectiveness was demonstrated by two case studies describing English and Spanish demographic and sociocultural scenarios.