Simple verbal instructions are able to improve quality of movement during the lateral step-down test in healthy females.

INTRODUCTION: Altered movement patterns during weight-bearing activities have been associated with knee injuries and can be clinically assessed using the lateral step-down test (LSD). It is possible that verbal feedback can improve movement patterns, but it remains unknown whether verbal feedback can improve movement quality during the LSD. PURPOSE: To investigate whether verbal feedback can immediately improve visual movement quality and trunk, pelvis and lower limb kinematics in healthy females during the LSD. METHODS: 34 healthy females were assessed visually and with 3D kinematics while performing the LSD. Participants were divided into Good Movement Group (GG; n = 18) and Poor Movement Group (PG; n = 16) based on the LSD score. The feedback involved verbal instructions aimed at improving trunk, pelvis, hip and knee alignment during the test. Lower limb flexibility and strength were assessed for group comparisons and to investigate associations between all variables. Data analyses were performed using repeated-measures two-way ANOVAs and Spearman correlation tests. RESULTS: Feedback immediately improved movement quality, especially in participants of the GG [mean difference (MD) = 2.2 points; P < 0.001]. The PG showed greater pelvic drop (MD = 5.1°; P = 0.012), greater hip adduction (MD = 5.4°; P = 0.028) and less hip flexion (MD = 8.4°; P = 0.016) than the GG. Quality of movement had positive correlations with pelvic drop (r = 0.39; P = 0.02), hip adduction (r = 0.45; P = 0.01) and hip flexion (r = 0.49; P < 0.01) kinematics. CONCLUSIONS: Verbal feedback improved movement quality during the LSD in healthy females. Females with worst movement quality showed greater pelvic drop and hip adduction, which are often found in individuals with knee disorders.

Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control.

Process analytical technologies (PAT) applied to process monitoring and control generally provide multiple outputs that can come from different sensors or from different model outputs generated from a single multivariate sensor. This paper provides a contribution to current data fusion strategies for the combination of sensor and/or model outputs in the development of multivariate statistical process control (MSPC) models. Data fusion is explored through three real process examples combining output from multivariate models coming from the same sensor uniquely (in the near-infrared (NIR)-based end point detection of a two-stage polyester production process) or the combination of these outputs with other process variable sensors (using NIR-based model outputs and temperature values in the end point detection of a fluidized bed drying process and in the on-line control of a distillation process). The three examples studied show clearly the flexibility in the choice of model outputs (e.g. key properties prediction by multivariate calibration, process profiles issued from a multivariate resolution method) and the benefit of using MSPC models based on fused information including model outputs towards those based on raw single sensor outputs for both process control and diagnostic and interpretation of abnormal process situations. The data fusion strategy proposed is of general applicability for any analytical or bioanalytical process that produces several sensor and/or model outputs. Graphical abstract.

Process modeling and control applied to real-time monitoring of distillation processes by near-infrared spectroscopy.

A distillation device that acquires continuous and synchronized measurements of temperature, percentage of distilled fraction and NIR spectra has been designed for real-time monitoring of distillation processes. As a process model, synthetic commercial gasoline batches produced in Brazil, which contain mixtures of pure gasoline blended with ethanol have been analyzed. The information provided by this device, i.e., distillation curves and NIR spectra, has served as initial information for the proposal of new strategies of process modeling and multivariate statistical process control (MSPC). Process modeling based on PCA batch analysis provided global distillation trajectories, whereas multiset MCR-ALS analysis is proposed to obtain a component-wise characterization of the distillation evolution and distilled fractions. Distillation curves, NIR spectra or compressed NIR information under the form of PCA scores and MCR-ALS concentration profiles were tested as the seed information to build MSPC models. New on-line PCA-based MSPC approaches, some inspired on local rank exploratory methods for process analysis, are proposed and work as follows: a) MSPC based on individual process observation models, where multiple local PCA models are built considering the sole information in each observation point; b) Fixed Size Moving Window - MSPC, in which local PCA models are built considering a moving window of the current and few past observation points; and c) Evolving MSPC, where local PCA models are built with an increasing window of observations covering all points since the beginning of the process until the current observation. Performance of different approaches has been assessed in terms of sensitivity to fault detection and number of false alarms. The outcome of this work will be of general use to define strategies for on-line process monitoring and control and, in a more specific way, to improve quality control of petroleum derived fuels and other substances submitted to automatic distillation processes monitored by NIRS.

Experimental diabetes induces structural, inflammatory and vascular changes of Achilles tendons.

This study aims to demonstrate how the state of chronic hyperglycemia from experimental Diabetes Mellitus can influence the homeostatic imbalance of tendons and, consequently, lead to the characteristics of tendinopathy. Twenty animals were randomly divided into two experimental groups: control group, consisting of healthy rats and diabetic group constituted by rats induced to Diabetes Mellitus I. After twenty-four days of the induction of Diabetes type I, the Achilles tendon were removed for morphological evaluation, cellularity, number and cross-sectional area of blood vessel, immunohistochemistry for Collagen type I, VEGF and NF-κB nuclear localization sequence (NLS) and nitrate and nitrite level. The Achilles tendon thickness (µm/100g) of diabetic animals was significantly increased and, similarly, an increase was observed in the density of fibrocytes and mast cells in the tendons of the diabetic group. The average number of blood vessels per field, in peritendinous tissue, was statistically higher in the diabetic group 3.39 (2.98) vessels/field when compared to the control group 0.89 (1.68) vessels/field p = 0.001 and in the intratendinous region, it was observed that blood vessels were extremely rare in the control group 0.035 (0.18) vessels/field and were often present in the tendons of the diabetic group 0.89 (0.99) vessels/field. The immunohistochemistry analysis identified higher density of type 1 collagen and increased expression of VEGF as well as increased immunostaining for NFκB p50 NLS in the nucleus in Achilles tendon of the diabetic group when compared to the control group. Higher levels of nitrite/nitrate were observed in the experimental group induced to diabetes. We conclude that experimental DM induces notable structural, inflammatory and vascular changes in the Achilles tendon which are compatible with the process of chronic tendinopathy.

Licanolide, a new triterpene lactone from Licania tomentosa.

Licanolide (3beta-hydroxylupane-20,28-olide), a novel triterpene lactone with new stereochemical pattern, was isolated from fresh fruits of Licania tomentosa in addition to betulinic and palmitoleic acid. The structure elucidation was based on spectroscopic methods including two-dimensional NMR experiments (HSQC, HMBC and NOESY).