Data Alignment Research Articles

Is spinopelvic compensation associated with unstable gait?: Analysis using whole spine X-rays and a two-point accelerometer during gait in healthy adults

BackgroundPelvic incidence (PI)–lumbar lordosis (LL) mismatch has a significant destabilizing effect on the center of gravity sway in the static standing position. However, the association between spinopelvic alignment and balance during gait in healthy volunteers is poorly understood. Research questionThe degree of PI–LL mismatch and trunk anterior tilt in the static standing posture influences dynamic balance during gait. MethodsIn this study, 131 healthy volunteers were divided into two groups: harmonious group (PI − LL ≤ 10°; n = 91) and unharmonious group (PI − LL > 10°; n = 40). A two-point accelerometer system was used for gait analysis; accelerometers were attached to the pelvis and upper trunk to measure acceleration in the forward–backward, right–left, and vertical directions so that sagittal (front–back) deviation width, coronal (right–left) width, and vertical width and their ratios were calculated. Measurements were compared between the two groups, and correlations between alignment and accelerometer data were examined. ResultsThe harmonious group showed a negative correlation between pelvic sagittal width and PI – LL, pelvic tilt (PT), and sagittal vertical axis (SVA) (correlation coefficient ρ = −0.42, −0.38, and −0.4, respectively), and a positive correlation between sagittal ratio and PI − LL (ρ = 0.35). The unharmonious group showed a positive correlation between pelvic sagittal width and PI and PT (ρ = 0.43 and 0.33, respectively) and between sagittal ratio and SVA (ρ = 0.32). The unharmonious group showed a positive correlation between upper trunk sagittal width and PI − LL and PT (ρ = 0.38 and 0.36, respectively). SignificanceThe association between spinal alignment and gait parameters differs depending on the presence or absence of PI–LL mismatch. The degree of pelvic compensation and trunk anterior tilt during static standing were associated with unstable gait balance.

Multicentric intelligent cardiotocography signal interpretation using deep semi-supervised domain adaptation via minimax entropy and domain invariance

Background and ObjectiveObstetricians use Cardiotocography (CTG), which is the continuous recording of fetal heart rate and uterine contraction, to assess fetal health status. Deep learning models for intelligent fetal monitoring trained on extensively labeled and identically distributed CTG records have achieved excellent performance. However, creation of these training sets requires excessive time and specialist labor for the collection and annotation of CTG signals. Previous research has demonstrated that multicenter studies can improve model performance. However, models trained on cross-domain data may not generalize well to target domains due to variance in distribution among datasets. Hence, this paper conducted a multicenter study with Deep Semi-Supervised Domain Adaptation (DSSDA) for intelligent interpretation of antenatal CTG signals. This approach helps to align cross-domain distribution and transfer knowledge from a label-rich source domain to a label-scarce target domain. MethodsWe proposed a DSSDA framework that integrated Minimax Entropy and Domain Invariance (DSSDA-MMEDI) to reduce inter-domain gaps and thus achieve domain invariance. The networks were developed using GoogLeNet to extract features from CTG signals, with fully connected, softmax layers for classification. We designed a Dynamic Gradient-driven strategy based on Mutual Information (DGMI) to unify the losses from Minimax Entropy (MME), Domain Invariance (DI), and supervised cross-entropy during iterative learning. ResultsWe validated our DSSDA model on two datasets collected from collaborating healthcare institutions and mobile terminals as the source and target domains, which contained 16,355 and 3,351 CTG signals, respectively. Compared to the results achieved with deep learning networks without DSSDA, DSSDA-MMEDI significantly improved sensitivity and F1-score by over 6%. DSSDA-MMEDI also outperformed other state-of-the-art DSSDA approaches for CTG signal interpretation. Ablation studies were performed to determine the unique contribution of each component in our DSSDA mechanism. ConclusionsThe proposed DSSDA-MMEDI is feasible and effective for alignment of cross-domain data and automated interpretation of multicentric antenatal CTG signals with minimal annotation cost.

Predicting macroinvertebrate average score per taxon (ASPT) at water quality monitoring sites in Japanese rivers.

Biomonitoring with bioindicators such as river macroinvertebrates is fundamental for assessing the status of freshwater ecosystems. In Japan, water quality and biomonitoring surveys are conducted separately, leading to a lack of nationwide information on their relationships and the biological status of water quality monitoring (WQM) sites. To understand the biological status of WQM sites across Japan, we developed a multiple linear regression model to estimate the average score per taxon (ASPT) using river macroinvertebrate data surveyed at a total of 237 "aligned" sites based on the co-occurrence of biomonitoring and WQM sites. The resulting regression model with eight predictors, such as biological oxygen demand, the proportion of urban areas in the catchment, could predict ASPT with reasonable accuracy (e.g., an error of ±1 for 96% of the aligned data). Using this model, we estimated ASPT values at 2925 WQM sites in rivers nationwide, categorizing them into four levels of river environment quality: "very good" (29% of WQM sites), "good" (50%), "fairly good" (14%), and "not good" (8%). Furthermore, we observed statistically significant correlations (p < 0.05; 0.4 ≤ r ≤ 0.7) between ASPT and all eight macroinvertebrate metrics examined, such as mayfly and stonefly richness, providing ecological implications of changes in ASPT.

CONHyperKGE: Using Contrastive Learning in Hyperbolic Space for Knowledge Graph Embedding

The embedding of Knowledge Graphs (KGs) in hyperbolic space has recently received great attention in the field of deep learning because it can provide more accurate and concise representations of hierarchical structures compared to Euclidean spaces and complex spaces. Although hyperbolic space embeddings have shown significant improvements over Euclidean spaces and complex space embeddings in handling the task of KG embedding, they still face challenges related to the uneven distribution and insufficient alignment of high-dimensional sparse data. To address this issue, we propose the CONHyperKGE model, which leverages contrastive learning to optimize the embedding distribution in hyperbolic space. This approach enables better capture of hierarchical structures, improved handling of symmetry, and enhanced treatment of sparse matrices. Our proposed method is evaluated on four standard KG Embedding (KGE) datasets: WN18RR, FB15k-237, Kinship, and UMLS. After extensive experimental verification, our method has improved its performance on all four datasets. Notably, on the low-dimensional Kinship dataset, our method achieves an average Mean Reciprocal Rank (MRR) improvement of 2% over the original method, while on the high-dimensional WN18RR dataset, an average MRR improvement of 1% is observed compared to the original method.

Design and Optimization of a Wearable Underactuated Mechanism for Spinal Posture Measurement

Abstract This paper proposes a novel wearable device to monitor and record the posture and alignment of spine. The proposed device adopts an underactuated mechanism design which allows it to adapt to the multiple-degrees-of-freedom spinal posture with minimum weight and complexity. To ensure the validity of measurement and comfort of wearing, the mechanism parameters are determined first by considering a special posture and then are fine-tuned using an optimization algorithm so that uniform contact forces for several selected spinal postures can be achieved. Experiments demonstrate that the device can automatically maintain contact with the wearer’s back and offer real-time spinal posture and alignment data for medical diagnosis and treatment.

Mobilizing data during a crisis: Building rapid evidence pipelines using multi-institutional real world data

The COVID-19 pandemic generated tremendous interest in using real world data (RWD). Many consortia across the public and private sectors formed in 2020 with the goal of rapidly producing high-quality evidence from RWD to guide medical decision-making, public health priorities, and more. Experiences were gathered from five large consortia on rapid multi-institutional evidence generation during the COVID-19 pandemic. Insights have been compiled across five dimensions: consortium composition, governance structure and alignment of priorities, data sharing, data analysis, and evidence dissemination. The purpose of this piece is to offer guidance on building large-scale multi-institutional RWD analysis pipelines for future public health issues.The composition of each consortium was largely influenced by existing collaborations. A central set of priorities for evidence generation guided each consortium, however different approaches to governance emerged. Challenges surrounding limited access to clinical data due to various contributors were overcome in unique ways. While all consortia used different methods to construct and analyze patient cohorts ranging from centralized to federated approaches, all proved effective for generating meaningful real-world evidence. Actionable recommendations for clinical practice and public health agencies were made from translating insights from consortium analyses.Each consortium was successful in rapidly answering questions about COVID-19 diagnosis and treatment despite all taking slightly different approaches to data sharing and analysis. Leveraging RWD, leveraged in a manner that applies scientific rigor and transparency, can complement higher-level evidence and serve as an important adjunct to clinical trials to quickly guide policy and critical care, especially for a pandemic response.

Spatial-Related Sensors Matters: 3D Human Motion Reconstruction Assisted with Textual Semantics

Leveraging wearable devices for motion reconstruction has emerged as an economical and viable technique. Certain methodologies employ sparse Inertial Measurement Units (IMUs) on the human body and harness data-driven strategies to model human poses. However, the reconstruction of motion based solely on sparse IMU data is inherently fraught with ambiguity, a consequence of numerous identical IMU readings corresponding to different poses. In this paper, we explore the spatial importance of sparse sensors, supervised by text that describes specific actions. Specifically, uncertainty is introduced to derive weighted features for each IMU. We also design a Hierarchical Temporal Transformer (HTT) and apply contrastive learning to achieve precise temporal and feature alignment of sensor data with textual semantics. Experimental results demonstrate our proposed approach achieves significant improvements in multiple metrics compared to existing methods. Notably, with textual supervision, our method not only differentiates between ambiguous actions such as sitting and standing but also produces more precise and natural motion.

Augmented Commonsense Knowledge for Remote Object Grounding

The vision-and-language navigation (VLN) task necessitates an agent to perceive the surroundings, follow natural language instructions, and act in photo-realistic unseen environments. Most of the existing methods employ the entire image or object features to represent navigable viewpoints. However, these representations are insufficient for proper action prediction, especially for the REVERIE task, which uses concise high-level instructions, such as “Bring me the blue cushion in the master bedroom”. To address enhancing representation, we propose an augmented commonsense knowledge model (ACK) to leverage commonsense information as a spatio-temporal knowledge graph for improving agent navigation. Specifically, the proposed approach involves constructing a knowledge base by retrieving commonsense information from ConceptNet, followed by a refinement module to remove noisy and irrelevant knowledge. We further present ACK which consists of knowledge graph-aware cross-modal and concept aggregation modules to enhance visual representation and visual-textual data alignment by integrating visible objects, commonsense knowledge, and concept history, which includes object and knowledge temporal information. Moreover, we add a new pipeline for the commonsense-based decision-making process which leads to more accurate local action prediction. Experimental results demonstrate our proposed model noticeably outperforms the baseline and archives the state-of-the-art on the REVERIE benchmark. The source code is available at https://github.com/Bahram-Mohammadi/ACK.

Abstract 7030: Reference materials for analysis of DNA methylation in cell-free circulating tumor DNA

Abstract Aberrant DNA methylation is associated with many cancers including breast, liver, bone, and colon and has been identified as a biomarker for early cancer screening. Liquid biopsies are beginning to screen for DNA methylation in cancer-derived DNA, but accurate assessment of methylation is not trivial, and methods like bisulfite sequencing can result in biased data. Reference materials for establishing the analytical validity of measurements of CpG methylation are not widely available. In order to prepare such reference materials, genomic DNA (gDNA) was extracted from the well characterized human reference cell line GM24385 and fragmented to a circulating tumor DNA (ctDNA)-like size distribution. This DNA was processed further to prepare CpG-methylated and unmethylated versions. The size distribution was determined by using the Bioanalyzer High Sensitivity DNA Kit (Agilent, Santa Clara, CA). Methylation status in the reference materials was confirmed by both digital PCR (dPCR) and NGS based methods. A digital PCR method was developed to analyze heterozygous single nucleotide polymorphisms (SNPs) using the Bio-Rad QX200™ Droplet Digital PCR (ddPCR) System. To determine methylation levels by NGS, the NEBNext™ Enzymatic Methyl-seq Kit was used (EM-seq). Paired-end sequencing of the libraries was performed on a NextSeq™ 2000 (Illumina, San Diego, CA). Data alignment to hg19 was done with the bwa-meth (Pedersen, BS., et al.) aligner, and assessment of methylation in the aligned data was done with MethylDackel (Ryan, D.). Each reference material had an average peak size of ~160 bp in length, similar to patient cfDNA. By dPCR, the unmethylated reference material had an average of 0.73% DNA methylation, while the methylated reference material had an average of 100.10% DNA methylation. Values can be above 100% by dPCR due to unequal representation of both alleles. By NGS, the unmethylated reference material had an average of 0.93 % CpG methylation, which was similar to the background of 1.13 % and 1.31 % for CHG and CHH methylation, respectively. The methylated reference material had an average of 93.95 % CpG methylation, with a background of 0.69 % and 0.74 % for CHG and CHH methylation, respectively. Because EM-seq relies on enzymatic protection and conversion steps, it may be that these reactions did not allow for full protection and full conversion, which led to values above 0 % for unmethylated and below 100 % for methylated materials. We have developed reference materials for the assessment of DNA methylation in ctDNA. One unmethylated and one methylated reference material was created. These reference materials can be mixed together to the level of methylation required to help aid in assay design, optimization, and validation. Citation Format: Alysha Higgs, Angelique Ryan, Jayanthi Ramprakash, Dana Ruminski Lowe, Yves Konigshofer, Catherine Huang, Andrew Anfora, Russell Garlick, Bharathi Anekella. Reference materials for analysis of DNA methylation in cell-free circulating tumor DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7030.

Tomo Live: an on-the-fly reconstruction pipeline to judge data quality for cryo-electron tomography workflows.

Data acquisition and processing for cryo-electron tomography can be a significant bottleneck for users. To simplify and streamline the cryo-ET workflow, Tomo Live, an on-the-fly solution that automates the alignment and reconstruction of tilt-series data, enabling real-time data-quality assessment, has been developed. Through the integration of Tomo Live into the data-acquisition workflow for cryo-ET, motion correction is performed directly after each of the acquired tilt angles. Immediately after the tilt-series acquisition has completed, an unattended tilt-series alignment and reconstruction into a 3D volume is performed. The results are displayed in real time in a dedicated remote web platform that runs on the microscope hardware. Through this web platform, users can review the acquired data (aligned stack and 3D volume) and several quality metrics that are obtained during the alignment and reconstruction process. These quality metrics can be used for fast feedback for subsequent acquisitions to save time. Parameters such as Alignment Accuracy, Deleted Tilts and Tilt Axis Correction Angle are visualized as graphs and can be used as filters to export only the best tomograms (raw data, reconstruction and intermediate data) for further processing. Here, the Tomo Live algorithms and workflow are described and representative results on several biological samples are presented. The Tomo Live workflow is accessible to both expert and non-expert users, making it a valuable tool for the continued advancement of structural biology, cell biology and histology.

Multiplex PCR method for MinION sequencing of Bagaza virus isolated from wild caught mosquitoes in South Africa

Bagaza virus (BAGV) is a mosquito-borne orthoflavivirus known to occur in regions of southern Europe, Africa, India and the Middle East. The virus has been associated with neurological disease and fatalities in various wild bird species. Association with human disease is not confirmed although limited serological evidence has suggested human infection. Surveillance programs for screening mosquitoes for evidence of arbovirus infection play an important role in providing information regarding the circulation and spread of viruses in specific regions. BAGV was detected in a mosquito pool during surveillance of mosquitoes collected in central South Africa between November 2019 and March 2023. Homogenized mosquito pools were screened for flaviviral RNA using conventional RT-PCR and virus isolation was attempted on positive samples. BAGV was detected and subsequently isolated using cell culture. A multiplex tiling PCR method for targeted enrichment using a PCR based or amplicon sequencing approach of the complete genome of BAGV was developed and optimized. Primers were designed using alignment of complete genome sequence data retrieved from GenBank to identify suitable primer sites that would generate overlapping fragments spanning the complete genome. Six forward primers and eight reverse primers were identified that target the complete genome and amplified nine overlapping fragments, that ranged in length from 1954 to 2039 with an overlap ranging from 71 to 711 base pairs. The design strategy included multiple forward and reverse primer pairs for the 5’ and 3’ ends. Phylogenetic analysis with other isolates was performed and BAGV isolate VBD 74/23/3 was shown to share high similarity with previous BAGV isolates from all regions, with genetic distance ranging from 0.026 to 0.083. VBD 74/23/3 was most closely related to previous isolates from southern Africa, ZRU96/16/2 isolated from a post-mortem sample from a pheasant in 2016 and MP-314-NA-2018 isolated from mosquitoes in northwestern Namibia with genetic distance 0.0085 and 0.016 respectively. Currently there is limited complete genome sequence data available for many of the arboviruses circulating in Africa. The multiplex tiling method provided a simple and cost-effective method for obtaining complete genome sequence. This method can be readily applied to other viruses using sequence data from publicly available databases and would have important application facilitating genomic surveillance of arboviruses in low resource countries.

Active domain adaptation with mining diverse knowledge: An updated class consensus dictionary approach

Domain adaptation (DA) has recently emerged as an effective paradigm for training the target model with labeled source knowledge. When knowledge transfer in DA encounters the bottleneck, one effective crack way is to introduce the labeled data to guide the DA process. Along this line, many active learning-based DA approaches are emerging to improve the quality of samples selection at the decision border. However, these methods have not preserved and exploited cross-domain common knowledge. In this work, we propose active domain adaptation with mining diverse knowledge: an updated class consensus dictionary approach (UCCDA). Specifically, we firstly initialize the class consensus dictionary by the source prior knowledge. Then, we choose high-confident target pseudo samples through self-training, while assigning labels to those low-confident via oracle annotation. In addition, we design the class consensus dictionary to guide the alignment between the source and target domains, instead of traditional direct cross-domain data alignment. Remarkably, to prevent error accumulation during the consensus dictionary learning, we specially design the anti-forgetting mask matrix to randomly restore the original knowledge. Finally, abundant experiments demonstrate that UCCDA outperforms the related state-of-the-art approaches.

A novel federated multi-view clustering method for unaligned and incomplete data fusion

Recently, federated multi-view clustering (FedMVC) has emerged as a powerful tool to uncover complementary cluster structures across distributed clients, gaining significant attention in the realm of data fusion. While FedMVC methods have adeptly addressed the challenges of feature heterogeneity among various clients, achieving notable success in controlled environments. Their applicability often hinges on the assumptions of strict alignment and data completeness across multi-view clients. These assumptions, unfortunately, are not always consistent with real-world conditions. Specifically, practical applications often come with (1) unaligned multi-view data and (2) missing data. Current FedMVC methods struggle to effectively address these challenges. To bridge this gap, this paper presents FCUIF, a novel method that eliminates the need for data alignment and completeness assumptions. To tackle unaligned data, FCUIF leverages both sample commonality and view versatility to adaptively generate alignment matrices, ensuring effective cross-view alignment. For the challenge of missing data, FCUIF uses an unsupervised technique to evaluate and refine imputation quality, efficiently handling various scenarios of incomplete multi-view data. Our extensive experiments using four public datasets demonstrate FCUIF’s superior performance when dealing with unaligned and incomplete multi-view data. The code is available at https://github.com/5Martina5/FCUIF.

Direct infusion–multiple reaction monitoring cubed (DI–MRM3) enables widely targeted bi-omics of Colla Corii Asini (Chinese name: Ejiao)

Food authenticity is extremely important and widely targeted bi-omics is a promising pipeline attributing to incorporating metabolomics and peptidomics. Colla Corii Asini (CCA, Ejiao) is one of the most popular tonic edible materials, with counterfeit and adulterated products being widespread. An attempt was devoted to develop a high-throughput and reliable DI–MRM3 program facilitating widely targeted bi-omics of CCA. Firstly, predictive MRM program captured metabolites and peptides in trypsin-digestive gelatins. After data alignment and structure annotation, primary parameters such as Q1 → Q3 → QLIT, CE, and EE were optimized for all 17 metabolites and 34 peptides by online ER-MS. Though a single run merely consumed 6.5 min, great selectivity was reached for each analyte. Statistical results showed that nine peptides contributed to distinguish CCA from other gelatins. After cross-validation with LC–MRM, DI–MRM3 was justified to be reproducible and high-throughput for widely targeted bi-omics of CCA, suggesting a meaningful tool for food authenticity.

Multimodal fusion of deep neural networks for transmission line flashover classification

ABSTRACT The power system plays a vital role in modern society. However, the occurrence of transmission line flashover faults seriously threatens the safety and stability of the power system. In this paper, a deep neural network flashover classification model based on multimodal attention aggregation is proposed. By using the model, the problems of insufficient feature extraction capability of the unimodal flashover data model and easy loss of key feature information in the process of audiovisual multimodal flashover data alignment are solved. Experiments on the arc- flashover fault dataset show that the flashover fault detection model proposed in this paper can be used stably and effectively for flashover fault detection.

A Novel Multimodal Fusion Framework Based on Point Cloud Registration for Near-Field 3D SAR Perception

This study introduces a pioneering multimodal fusion framework to enhance near-field 3D Synthetic Aperture Radar (SAR) imaging, crucial for applications like radar cross-section measurement and concealed object detection. Traditional near-field 3D SAR imaging struggles with issues like target–background confusion due to clutter and multipath interference, shape distortion from high sidelobes, and lack of color and texture information, all of which impede effective target recognition and scattering diagnosis. The proposed approach presents the first known application of multimodal fusion in near-field 3D SAR imaging, integrating LiDAR and optical camera data to overcome its inherent limitations. The framework comprises data preprocessing, point cloud registration, and data fusion, where registration between multi-sensor data is the core of effective integration. Recognizing the inadequacy of traditional registration methods in handling varying data formats, noise, and resolution differences, particularly between near-field 3D SAR and other sensors, this work introduces a novel three-stage registration process to effectively address these challenges. First, the approach designs a structure–intensity-constrained centroid distance detector, enabling key point extraction that reduces heterogeneity and accelerates the process. Second, a sample consensus initial alignment algorithm with SHOT features and geometric relationship constraints is proposed for enhanced coarse registration. Finally, the fine registration phase employs adaptive thresholding in the iterative closest point algorithm for precise and efficient data alignment. Both visual and quantitative analyses of measured data demonstrate the effectiveness of our method. The experimental results show significant improvements in registration accuracy and efficiency, laying the groundwork for future multimodal fusion advancements in near-field 3D SAR imaging.

Multi-Level Hazard Detection Using a UAV-Mounted Multi-Sensor for Levee Inspection

This paper introduces a developed multi-sensor integrated system comprising a thermal infrared camera, an RGB camera, and a LiDAR sensor, mounted on a lightweight unmanned aerial vehicle (UAV). This system is applied to the inspection tasks of levee engineering, enabling the real-time, rapid, all-day, all-round, and non-contact acquisition of multi-source data for levee structures and their surrounding environments. Our aim is to address the inefficiencies, high costs, limited data diversity, and potential safety hazards associated with traditional methods, particularly concerning the structural safety of dam bodies. In the preprocessing stage of multi-source data, techniques such as thermal infrared data enhancement and multi-source data alignment are employed to enhance data quality and consistency. Subsequently, a multi-level approach to detecting and screening suspected risk areas is implemented, facilitating the rapid localization of potential hazard zones and assisting in assessing the urgency of addressing these concerns. The reliability of the developed multi-sensor equipment and the multi-level suspected hazard detection algorithm is validated through on-site levee engineering inspections conducted during flood disasters. The application reliably detects and locates suspected hazards, significantly reducing the time and resource costs associated with levee inspections. Moreover, it mitigates safety risks for personnel engaged in levee inspections. Therefore, this method provides reliable data support and technical services for levee inspection, hazard identification, flood control, and disaster reduction.

What Is the Correlation between Coronal Plane Alignment Measured on Pre- and Postoperative Weight-bearing Radiographs and Intraoperative Navigation When Stress Is Applied to the Knee?

This study examines the correlation between the weight-bearing (WB) long leg radiograph (LLR)-derived hip-knee-ankle angle (HKAA) and intraoperative supine computer-assisted surgery (CAS)-derived HKAA measurements at the beginning and end of total knee arthroplasty (TKA). The primary aim of the study was to determine if WB alignment could be mimicked or inferred based on intraoperative alignment findings. We conducted a prospective analysis from a cohort of 129 TKAs undergoing a CAS TKA at a single center by a single surgeon. The HKAA was recorded using the CAS navigation system immediately postregistration of navigation data and after implantation of the prosthesis. The intraoperative HKAA was recorded in both the supine "resting" position of the knee and also while the knee was manipulated in an effort to replicate the patient's WB alignment. These measurements were compared with the HKAA recorded on pre- and postoperative WB LLRs. There was a strong correlation between the preoperative WB LLR HKAA and the intraoperative preimplant CAS-derived stressed HKAA (R = 0.946). However, there was no correlation between the postoperative WB LLR HKAA and the postimplant insertion HKAA as measured intraoperatively via CAS for either a "resting" or "stressed" position of the operated knee (R = 0.165 and R = 0.041, respectively). Thus, the interpretation of intraoperative alignment data is potentially problematic. Despite technological advances in the development and utilization of computer navigation and robotics in arthroplasty to help obtain the optimal alignment, it would seem apparent from our study that this alignment does not correlate to upright stance postoperatively. Surgeons should apply caution to the strength of assumptions they place on intraoperative coronal plane alignment findings.

The effect of aging on vowel production in Western Canadian English

Aging influences many aspects of speech production. For example, a speaker’s fundamental frequency or formant space has been reported to change as they age. However, this research has largely focused on careful or prepared speech, with a few notable exceptions in the longitudinal research literature. There is limited knowledge of the relationship between aging and vowel reduction in spontaneously produced speech. It is important to investigate how spontaneous everyday speech is influenced by the aging process. In the present investigation, we work with a corpus of Western Canadian English containing spontaneous conversational speech produced by 18 younger (17–31) and 13 older speakers (64–79). The data was force-aligned and then acoustic measures were extracted from the aligned data. In this presentation, we report on the differences in formant space for stressed and unstressed vowels. The results of this study will be used to better inform our understanding of the role of vowel centralization and aging in spontaneous speech. They will further support the understanding of reduction in spontaneous speech.

Data Alignment in the Fast-Moving Consumer Goods Retail Sector

Purpose: The Fast-Moving Consumer Goods (FMCG) industry faces a significant challenge in data alignment due to the diverse range of barcode types utilized by different data sources.&#x0D; Methodology: Barcodes and Universal Product Codes (UPCs) have become pivotal solutions for harmonizing large-scale FMCG product data. This paper explores and analyzes challenges in data aligning within the retail sector. &#x0D; Findings : The paper proposes solutions to enhance product data synchronization, contributing valuable insights to foster a more data-driven and efficient retail ecosystem. The barcode/UPC integration not only aligns data for current analytics needs but also holds the potential for significantly enhancing future capabilities, especially in predictive modeling, trend forecasting, and strategic research on new product introductions.&#x0D; Unique Contribution to Theory, Policy and Practice: As the retail landscape evolves, barcode and UPC integration becomes a cornerstone for retailers seeking to stay ahead of industry trends and harness the power of advanced analytics to drive success.