The effects of sex hormones during the menstrual cycle on knee kinematics

The effects of the menstrual cycle and sex hormones on knee kinematics remain unclear. The purpose of the study was to investigate the effects of the menstrual cycle and serum sex hormone concentrations on knee kinematic parameters of the 90°cutting in female college soccer athletes. Three female college soccer teams (53 subjects) participated in the study. During the first menstrual cycle, a three-step method was used to exclude subjects with anovulatory and luteal phase–deficient (LPD) (12 subjects). The subjects’ menstrual cycle was divided into the menstrual phase, late-follicular phase, ovulatory phase, and mid-luteal phase (group 1, 2, 3, 4). In each phase of the second menstrual cycle, we used a portable motion analysis system to enter the teams and tested the sex hormones concentrations and knee kinematics parameters in three universities in turn. We found that subjects had a lower maximum knee valgus in group 4 compared with other groups. This meant that subjects had a lower biomechanical risk of non-contact anterior cruciate ligament (ACL) injury in the mid-luteal phase. There was no significant correlation between serum estrogen, progesterone concentration, and knee kinematic parameters. This meant that sex hormones did not have a protective effect. Future studies need to incorporate more factors (such as neuromuscular control, etc.) to investigate

Localized atmospheric density prediction method based on NARX neural network

Errors of orbit determination and prediction for low earth orbit (LEO) satellites mainly arise from the lack of accuracy in existing atmospheric density models. The lack of observation methods and insufficient understanding of physical mechanism of the upper atmosphere have brought difficulties to the modelling of atmospheric density. Two line element (TLE) was used to calibrate the MSIS atmospheric model, aiming at getting a localized density model along the orbit. Then a predictor was built based on the nonlinear autoregressive neural network with exogenous inputs (NARX). It uses calibrated MISIS model and a set of proxies of solar and geomagnetic activities to predict localized density values along the future orbit of a satellite. This model was applied for different types of satellite orbits and tested for different prediction windows. Comparison with the predictor based on the MSIS model shows a decrease in the mean error of the proposed model, which throws new light on improving the accuracy of LEO satellites’ short-time prediction

Substrate specificities of NfBGL1.

a<p> Values are means ± S.D. (<i>n</i> = 3); –, no activity detected.</p><p>Substrate specificities of NfBGL1.</p

Property comparison of NfBGL1 from <i>N. fischeri</i> P1 and its fungal counterparts. ^a

a<p> The substrate used for enzyme characterization is <i>p</i>NPG.</p><p>Property comparison of NfBGL1 from <i>N. fischeri</i> P1 and its fungal counterparts. ^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106785#nt101" target="_blank">a</a>}</p

Conversion of soybean isoflavone glycosides into free isoflavones by NfBGL1 and Novozyme 188. ^a

a<p> The reaction system without enzyme addition was treated as the control; the data are shown as mean ± S.D. (<i>n</i> = 3).</p><p>Conversion of soybean isoflavone glycosides into free isoflavones by NfBGL1 and Novozyme 188. ^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106785#nt103" target="_blank">a</a>}</p

Purification and high-cell-density fermentation of recombinant NfBGL1.

<p>(A) SDS-PAGE analysis of purified recombinant NfBGL1. Lanes: 1, the molecular mass standards; 2, the purified recombinant of NfBGL1. (B) Time course of NfBGL1 production in a 3.7-l fermenter. Each value in the panel represents the means ± S.D. (<i>n</i> = 3).</p

The homology-modeled NfBGL1 with AaBGL1 from <i>A. aculeatus</i> (4IIB, 44.1% identity) as the template.

<p>(A) Putative structure of NfBGL1. The catalytic residues D235 and E447 are indicated. (B) Putative interactions between the key residues of NfBGL1 and substrate cellobiose.</p

Characterization of purified recombinant NfBGL1.

<p>(A) Effect of pH on β-glucosidase activity. The enzyme assay was performed at 80°C for 10 min. (B) pH stability. The enzyme was pre-incubated without substrate at 37°C for 60 min, and then subjected to residual activity assay under standard conditions (pH 5.0, 80°C, 10 min). (C) Effect of temperature on β-glucosidase activity determined at pH 5.0 for 10 min. (D) Thermostability. The residual enzyme activities were measured under standard conditions after pre-incubation of the enzyme without substrate in McIlvaine buffer (pH 5.0) for various periods. Each value in the panel represents the means ± S.D. (<i>n</i> = 3).</p

Multiple alignment of the deduced amino acid sequence of mature NfBGL1 (XP_001261562) with other fungal counterparts from <i>T. reesei</i> (TrBgl1, 1713235A), and <i>A. aculeatus</i> (AaBGL1, 4IIB).

<p>Identical and similar amino acids are indicated by solid black and gray boxes, respectively. The putative catalytic residues, D235 and E447, are indicated with asterisks. The residues probably related to subsite −1 (W236) and +1 (W36, N261, and Y449) are indicated with white and black triangles, respectively. The residues probably forming hydrogen bonds with substrate, D60, R124, K147 and H148, are indicated with black dots.</p