Comparative study of intraoperative knee flexion with three different TKR designs

SummaryIntroductionSubstantial flexion after total knee arthroplasty (TKA) is required for certain categories of patients who wish to squat or kneel in their daily life. Many factors influence this postoperative flexion, including the prosthesis design. It is therefore valuable to in vivo analyze these factors on three knee prosthesis designs through a study of their intraoperative flexion.HypothesisThe posterior-stabilized (PS) knee prostheses provide better intraoperative flexion than the ultracongruent (UC) model. Of the currently available PS models, the high-flexion ones have better intraoperative flexion than standard models. Our main focus endpoint was the intraoperative flexion achieved, before soft-tissues closure, during TKA surgical procedure.Patients and methodsThis was a controlled study. Seventy-two osteoarthritic knees requiring TKA were included to compare three selected prosthesis models: the SAL ultracongruent and two PS models (the standard LPS and the LPS Flex). This was a single-operator study, with patients divided into three homogenous, comparable groups, in which intraoperative measurement of flexion was performed using computer-assisted navigation. Statistical analysis allowed comparison of the three models.ResultsIntraoperatively, after prosthesis implantation, before soft-tissues closure, the mean flexion of the LPS-Flex was 134° versus 124° for the SAL (p=0.0004); the mean flexion of the standard LPS model was 130° versus 124° for the SAL (p=0.14); the PS Flex model showed no significant difference (p=0.26) in flexion (134°) compared to the standard model (130°). The SAL ultracongruent model seemed to be a factor reducing the intraoperative flexion by 8° compared to the PS models (p<10−4).DiscussionIn this study, the PS designs (standard or Flex) provided better intraoperative flexion than the SAL ultracongruent design. However, the LPS Prosthesis did not demonstrate superiority over the standard LPS Prosthesis.Level of evidenceLevel III, low-power prospective study

Asymptotic normality of the Parzen-Rosenblatt density estimator for strongly mixing random fields

We prove the asymptotic normality of the kernel density estimator (introduced by Rosenblatt (1956) and Parzen (1962)) in the context of stationary strongly mixing random fields. Our approach is based on the Lindeberg's method rather than on Bernstein's small-block-large-block technique and coupling arguments widely used in previous works on nonparametric estimation for spatial processes. Our method allows us to consider only minimal conditions on the bandwidth parameter and provides a simple criterion on the (non-uniform) strong mixing coefficients which do not depend on the bandwith.Comment: 16 page

Activating words without language: beta and theta oscillations reflect lexical access and control processes during verbal and non-verbal object recognition tasks

The intention to name an object modulates neural responses during object recognition tasks. However, the nature of this modulation is still unclear. We established whether a core operation in language, i.e. lexical access, can be observed even when the task does not require language (size-judgment task), and whether response selection in verbal versus non-verbal semantic tasks relies on similar neuronal processes. We measured and compared neuronal oscillatory activities and behavioral responses to the same set of pictures of meaningful objects, while the type of task participants had to perform (picture-naming versus size-judgment) and the type of stimuli to measure lexical access (cognate versus non-cognate) were manipulated. Despite activation of words was facilitated when the task required explicit word-retrieval (picture-naming task), lexical access occurred even without the intention to name the object (non-verbal size-judgment task). Activation of words and response selection were accompanied by beta (25-35 Hz) desynchronization and theta (3-7 Hz) synchronization, respectively. These effects were observed in both picture-naming and size-judgment tasks, suggesting that words became activated via similar mechanisms, irrespective of whether the task involves language explicitly. This finding has important implications to understand the link between core linguistic operations and performance in verbal and non-verbal semantic tasks

Flame spread over solid fuel in low-speed concurrent flow

This research program is concerned with the effect of low speed flow on the spreading and extinction processes of flames over solid fuels. Primary attention is given to flame propagation in concurrent flow - the more hazardous situation from the point of view of fire safety

Nonlinear description of transversal motion in a laminar boundary layer with streaks

The nonlinear streamwise growth of a spanwise periodic array of steady streaks in a flat plate boundary layer is numerically computed using the well known Reduced Navier-Stokes formulation. It is found that the flow configuration changes substantially when the amplitude of the streaks grows and the nonlinear effects come into play. The transversal motion (in the wall normal-spanwise plane), which is normally not considered, becomes non-negligible in the nonlinear regime, and it strongly distorts the streamwise velocity profiles, which end up being quite different from those predicted by the linear theory. We analyze in detail the resulting flow patterns for the nonlinearly saturated streaks, and compare them with available experimental results

A systematic review of the use of an expertise-based randomised controlled trial design

Acknowledgements JAC held a Medical Research Council UK methodology (G1002292) fellowship, which supported this research. The Health Services Research Unit, Institute of Applied Health Sciences (University of Aberdeen), is core-funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates. Views express are those of the authors and do not necessarily reflect the views of the funders.Peer reviewedPublisher PD

Ptch2/Gas1 and Ptch1/Boc differentially regulate Hedgehog signalling in murine primordial germ cell migration.

Gas1 and Boc/Cdon act as co-receptors in the vertebrate Hedgehog signalling pathway, but the nature of their interaction with the primary Ptch1/2 receptors remains unclear. Here we demonstrate, using primordial germ cell migration in mouse as a developmental model, that specific hetero-complexes of Ptch2/Gas1 and Ptch1/Boc mediate the process of Smo de-repression with different kinetics, through distinct modes of Hedgehog ligand reception. Moreover, Ptch2-mediated Hedgehog signalling induces the phosphorylation of Creb and Src proteins in parallel to Gli induction, identifying a previously unknown Ptch2-specific signal pathway. We propose that although Ptch1 and Ptch2 functionally overlap in the sequestration of Smo, the spatiotemporal expression of Boc and Gas1 may determine the outcome of Hedgehog signalling through compartmentalisation and modulation of Smo-downstream signalling. Our study identifies the existence of a divergent Hedgehog signal pathway mediated by Ptch2 and provides a mechanism for differential interpretation of Hedgehog signalling in the germ cell niche

Theoretical Properties of Projection Based Multilayer Perceptrons with Functional Inputs

Many real world data are sampled functions. As shown by Functional Data Analysis (FDA) methods, spectra, time series, images, gesture recognition data, etc. can be processed more efficiently if their functional nature is taken into account during the data analysis process. This is done by extending standard data analysis methods so that they can apply to functional inputs. A general way to achieve this goal is to compute projections of the functional data onto a finite dimensional sub-space of the functional space. The coordinates of the data on a basis of this sub-space provide standard vector representations of the functions. The obtained vectors can be processed by any standard method. In our previous work, this general approach has been used to define projection based Multilayer Perceptrons (MLPs) with functional inputs. We study in this paper important theoretical properties of the proposed model. We show in particular that MLPs with functional inputs are universal approximators: they can approximate to arbitrary accuracy any continuous mapping from a compact sub-space of a functional space to R. Moreover, we provide a consistency result that shows that any mapping from a functional space to R can be learned thanks to examples by a projection based MLP: the generalization mean square error of the MLP decreases to the smallest possible mean square error on the data when the number of examples goes to infinity

Mesenchymal chondrosarcoma: prognostic factors and outcome in 113 patients. A European Musculoskeletal Oncology Society study

BACKGROUND: Mesenchymal chondrosarcoma (MCS) is a distinct, very rare sarcoma with little evidence supporting treatment recommendations. PATIENTS AND METHODS: Specialist centres collaborated to report prognostic factors and outcome for 113 patients. RESULTS: Median age was 30 years (range: 11-80), male/female ratio 1.1. Primary sites were extremities (40%), trunk (47%) and head and neck (13%), 41 arising primarily in soft tissue. Seventeen patients had metastases at diagnosis. Mean follow-up was 14.9 years (range: 1-34), median overall survival (OS) 17 years (95% confidence interval (CI): 10.3-28.6). Ninety-five of 96 patients with localised disease underwent surgery, 54 additionally received combination chemotherapy. Sixty-five of 95 patients are alive and 45 progression-free (5 local recurrence, 34 distant metastases, 11 combined). Median progression-free survival (PFS) and OS were 7 (95% CI: 3.03-10.96) and 20 (95% CI: 12.63-27.36) years respectively. Chemotherapy administration in patients with localised disease was associated with reduced risk of recurrence (P=0.046; hazard ratio (HR)=0.482 95% CI: 0.213-0.996) and death (P=0.004; HR=0.445 95% CI: 0.256-0.774). Clear resection margins predicted less frequent local recurrence (2% versus 27%; P=0.002). Primary site and origin did not influence survival. The absence of metastases at diagnosis was associated with a significantly better outcome (P<0.0001). Data on radiotherapy indications, dose and fractionation were insufficiently complete, to allow comment of its impact on outcomes. Median OS for patients with metastases at presentation was 3 years (95% CI: 0-4.25). CONCLUSIONS: Prognosis in MCS varies considerably. Metastatic disease at diagnosis has the strongest impact on survival. Complete resection and adjuvant chemotherapy should be considered as standard of care for localised disease

A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry

[EN] This document presents a comparison of demand forecasting methods, with the aim of improving demand forecasting and with it, the production planning system of Ecuadorian textile industry. These industries present problems in providing a reliable estimate of future demand due to recent changes in the Ecuadorian context. The impact on demand for textile products has been observed in variables such as sales prices and manufacturing costs, manufacturing gross domestic product and the unemployment rate. Being indicators that determine to a great extent, the quality and accuracy of the forecast, generating also, uncertainty scenarios. For this reason, the aim of this work is focused on the demand forecasting for textile products by comparing a set of classic methods such as ARIMA, STL Decomposition, Holt-Winters and machine learning, Artificial Neural Networks, Bayesian Networks, Random Forest, Support Vector Machine, taking into consideration all the above mentioned, as an essential input for the production planning and sales of the textile industries. And as a support, when developing strategies for demand management and medium-term decision making of this sector under study. Finally, the effectiveness of the methods is demonstrated by comparing them with different indicators that evaluate the forecast error, with the Multi-layer Neural Networks having the best results with the least error and the best performance.The authors are greatly grateful by the support given by the SDAS Research Group (https://sdas-group.com/).Lorente-Leyva, LL.; Alemany Díaz, MDM.; Peluffo-Ordóñez, DH.; Herrera-Granda, ID. (2021). 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