RpaA Regulates the Accumulation of Monomeric Photosystem I and PsbA under High Light Conditions in Synechocystis sp PCC 6803

The response regulator RpaA was examined by targeted mutagenesis under high light conditions in Synechocystis sp. PCC 6803. A significant reduction in chlorophyll fluorescence from photosystem I at 77 K was observed in the RpaA mutant cells under high light conditions. Interestingly, the chlorophyll fluorescence emission from the photosystem I trimers at 77 K are similar to that of the wild type, while the chlorophyll fluorescence from the photosystem I monomers was at a much lower level in the mutant than in the wild type under high light conditions. The RpaA inactivation resulted in a dramatic reduction in the monomeric photosystem I and the D1 protein but not the CP47 content. However, there is no significant difference in the transcript levels of psaA or psbA or other genes examined, most of which are involved in photosynthesis, pigment biosynthesis, or stress responses. Under high light conditions, the growth of the mutant was affected, and both the chlorophyll content and the whole-chain oxygen evolution capability of the mutant were found to be significantly lower than those of the wild type, respectively. We propose that RpaA regulates the accumulation of the monomeric photosystem I and the D1 protein under high light conditions. This is the first report demonstrating that inactivation of a stress response regulator has specifically reduced the monomeric photosystem I. It suggests that PS I monomers and PS I trimers can be regulated independently for acclimation of cells to high light stress.The response regulator RpaA was examined by targeted mutagenesis under high light conditions in Synechocystis sp. PCC 6803. A significant reduction in chlorophyll fluorescence from photosystem I at 77 K was observed in the RpaA mutant cells under high light conditions. Interestingly, the chlorophyll fluorescence emission from the photosystem I trimers at 77 K are similar to that of the wild type, while the chlorophyll fluorescence from the photosystem I monomers was at a much lower level in the mutant than in the wild type under high light conditions. The RpaA inactivation resulted in a dramatic reduction in the monomeric photosystem I and the D1 protein but not the CP47 content. However, there is no significant difference in the transcript levels of psaA or psbA or other genes examined, most of which are involved in photosynthesis, pigment biosynthesis, or stress responses. Under high light conditions, the growth of the mutant was affected, and both the chlorophyll content and the whole-chain oxygen evolution capability of the mutant were found to be significantly lower than those of the wild type, respectively. We propose that RpaA regulates the accumulation of the monomeric photosystem I and the D1 protein under high light conditions. This is the first report demonstrating that inactivation of a stress response regulator has specifically reduced the monomeric photosystem I. It suggests that PS I monomers and PS I trimers can be regulated independently for acclimation of cells to high light stress

The classification of skateboarding tricks: A support vector machine hyperparameter evaluation optimisation

The growing interest in skateboarding as a competitive sport requires new motion analysis approaches and innovative ways to portray athletes’ results as previous techniques in the identification of the tricks was often inadequate in providing accurate evaluation during competition. Therefore, there is a need to introduce an unprejudiced method of evaluation in skateboarding competitions. This paper presents the classification of five different skateboarding tricks (Ollie, Kickflip, Frontside 180, Pop Shove-it, and Nollie Frontside Shove-it) through the identification os significant frequency-domain signals collected via Inertial Measurement Unit (IMU) and the use of machine learning models. Onemale skateboarder (age: 23 years old) performed five different tricks repeatedly for several times. The time-domain data acquired from the IMU were converted to frequency-domain by employing Fast Fourier Transform (FFT) and a number of statistical features (mean, kurtosis, skewness, standard deviation, root mean square and peak-to-peak corresponding to x-y-z-axis of the IMU) were then extracted. Significant features were then identified from the Information Gain (IG) scoring. It was shown from the study that the Naïve Bayes (NB) classifier is able to acquire the highest classification accuracy of 100% on the test data compared to the other evaluated classifiers, namely Artificial Neural Network (ANN) and SupportVector Machine (SVM), by utilising the selected features, suggesting that the proposed methodology could provide an objective-based evaluation of the trick

Interleukin-1β sequesters hypoxia inducible factor 2α to the primary cilium.

BACKGROUND: The primary cilium coordinates signalling in development, health and disease. Previously we have shown that the cilium is essential for the anabolic response to loading and the inflammatory response to interleukin-1β (IL-1β). We have also shown the primary cilium elongates in response to IL-1β exposure. Both anabolic phenotype and inflammatory pathology are proposed to be dependent on hypoxia-inducible factor 2 alpha (HIF-2α). The present study tests the hypothesis that an association exists between the primary cilium and HIFs in inflammatory signalling. RESULTS: Here we show, in articular chondrocytes, that IL-1β-induces primary cilia elongation with alterations to cilia trafficking of arl13b. This elongation is associated with a transient increase in HIF-2α expression and accumulation in the primary cilium. Prolyl hydroxylase inhibition results in primary cilia elongation also associated with accumulation of HIF-2α in the ciliary base and axoneme. This recruitment and the associated cilia elongation is not inhibited by blockade of HIFα transcription activity or rescue of basal HIF-2α expression. Hypomorphic mutation to intraflagellar transport protein IFT88 results in limited ciliogenesis. This is associated with increased HIF-2α expression and inhibited response to prolyl hydroxylase inhibition. CONCLUSIONS: These findings suggest that ciliary sequestration of HIF-2α provides negative regulation of HIF-2α expression and potentially activity. This study indicates, for the first time, that the primary cilium regulates HIF signalling during inflammation

The classification of skateboarding tricks by means of support vector machine: An evaluation of significant time-domain features

This study aims to improve classification accuracy of different Support Vector Machine (SVM) models in classifying flat ground tricks namely Ollie, Kick-flip, Shove-it, Nollie and Frontside 180 through the identification of significant time-domain features. An amateur skateboarder (23 years of age ±5.0 years’ experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (IMU sensor fused) on a cemented ground. From the IMU data a total of 36 features were extracted through statistical measures. The significant features were identified through two feature selection methods, namely Pearson and Chi-Squared. The variation of the SVM models (kernel-based) was evaluated both on all features and selected features in classifying the skateboarding tricks. It was shown from the study that all classifiers improved significantly in terms of training accuracy, prediction speed, training time and test accuracy. The Cubic-based SVM and Quadratic-based SVM demonstrated a 100% accuracy on both the test and train dataset, however, the Cubic-based SVM model provided the fastest training time and prediction speed between the two models. It could be concluded that the proposed method is able to improve the classification of the skateboarding tricks well

The classification of skateboarding tricks : A transfer learning and machine learning approach

The skateboarding scene has arrived at new statures, particularly with its first appearance at the now delayed Tokyo Summer Olympic Games. Hence, attributable to the size of the game in such competitive games, progressed creative appraisal approaches have progressively increased due consideration by pertinent partners, particularly with the enthusiasm of a more goal-based assessment. This study purposes for classifying skateboarding tricks, specifically Frontside 180, Kickflip, Ollie, Nollie Front Shove-it, and Pop Shove-it over the integration of image processing, Trasnfer Learning (TL) to feature extraction enhanced with tradisional Machine Learning (ML) classifier. A male skateboarder performed five tricks every sort of trick consistently and the YI Action camera captured the movement by a range of 1.26 m. Then, the image dataset were features built and extricated by means of three TL models, and afterward in this manner arranged to utilize by k-Nearest Neighbor (k-NN) classifier. The perception via the initial experiments showed, the MobileNet, NASNetMobile, and NASNetLarge coupled with optimized k-NN classifiers attain a classification accuracy (CA) of 95%, 92% and 90%, respectively on the test dataset. Besides, the result evident from the robustness evaluation showed the MobileNet+k-NN pipeline is more robust as it could provide a decent average CA than other pipelines. It would be demonstrated that the suggested study could characterize the skateboard tricks sufficiently and could, over the long haul, uphold judges decided for giving progressively objective-based decision

The classification of skateboarding tricks via transfer learning pipelines

This study aims at classifying flat ground tricks, namely Ollie, Kickflip, Shove-it, Nollie and Frontside 180, through the identification of significant input image transformation on different transfer learning models with optimized Support Vector Machine (SVM) classifier. A total of six amateur skateboarders (20 ± 7 years of age with at least 5.0 years of experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (IMU sensor fused) on a cemented ground. From the IMU data, a total of six raw signals extracted. A total of two input image type, namely raw data (RAW) and Continous Wavelet Transform (CWT), as well as six transfer learning models from three different families along with gridsearched optimized SVM, were investigated towards its efficacy in classifying the skateboarding tricks. It was shown from the study that RAW and CWT input images on MobileNet, MobileNetV2 and ResNet101 transfer learning models demonstrated the best test accuracy at 100% on the test dataset. Nonetheless, by evaluating the computational time amongst the best models, it was established that the CWTMobileNet-Optimized SVM pipeline was found to be the best. It could be concluded that the proposed method is able to facilitate the judges as well as coaches in identifying skateboarding tricks executio

An evaluation of different input transformation for the classification of skateboarding tricks by means of transfer learning

This study aims to investigate the effect of different input images, namely raw data (RAW) and Continuous Wavelet Transform (CWT) towards the discriminating of street skateboarding tricks, i.e., Ollie, Kickflip, Shove-it, Nollie and Frontside 180 through a variety of transfer learning with optimised k-Nearest Neighbors (kNN) pipelines. Six amateur skateboarders participated in the study, executed the aforesaid tricks five times per trick on an instrumented skateboard where six time-domain signals were extracted prior it was transformed to RAW and CWT. It was shown from the study that the CWT-InceptionV3-optimised kNN pipeline could attain an average test and validation accuracy of 90%

The classification of skateboarding trick manoeuvres: A K-nearest neighbour approach

The evaluation of skateboarding tricks is commonly carried out subjectively through the prior experience of the panel of judges during skateboarding competitions. Hence, this technique evaluation is often impartial to a certain degree. This study aims at classifying flat ground tricks namely Ollie, Kickflip, Shove-it, Nollie and Frontside 180 through the use of Inertial Measurement Unit (IMU) and a class of machine learning model namely k-Nearest Neighbour (k-NN). An amateur skateboarder (23 years of age ± 5.0 years’ experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (IMU sensor fused) on a cemented ground. A number of features were extracted and engineered from the IMU data, i.e., mean, skewness, kurtosis, peak to peak, root mean square as well as standard deviation of the acceleration and angular velocities along the primary axes. A variation of k-NN algorithms were tested based on the number of neighbours, as well as the weight and the type of distance metric used. It was shown from the present preliminary investigation, that the k-NN model which employs k = 1 with an equal weight applied to the Euclidean distance metric yielded a classification accuracy of 85%. Therefore, it could be concluded that the proposed method is able to classify the skateboard tricks reasonably well and will in turn, assist the judges in providing more accurate evaluation of the tricks as opposed to the conventional-subjective based assessment that is applied at presen

Utilisation of an operative difficulty grading scale for laparoscopic cholecystectomy

Background A reliable system for grading operative difficulty of laparoscopic cholecystectomy would standardise description of findings and reporting of outcomes. The aim of this study was to validate a difficulty grading system (Nassar scale), testing its applicability and consistency in two large prospective datasets. Methods Patient and disease-related variables and 30-day outcomes were identified in two prospective cholecystectomy databases: the multi-centre prospective cohort of 8820 patients from the recent CholeS Study and the single-surgeon series containing 4089 patients. Operative data and patient outcomes were correlated with Nassar operative difficultly scale, using Kendall’s tau for dichotomous variables, or Jonckheere–Terpstra tests for continuous variables. A ROC curve analysis was performed, to quantify the predictive accuracy of the scale for each outcome, with continuous outcomes dichotomised, prior to analysis. Results A higher operative difficulty grade was consistently associated with worse outcomes for the patients in both the reference and CholeS cohorts. The median length of stay increased from 0 to 4 days, and the 30-day complication rate from 7.6 to 24.4% as the difficulty grade increased from 1 to 4/5 (both p < 0.001). In the CholeS cohort, a higher difficulty grade was found to be most strongly associated with conversion to open and 30-day mortality (AUROC = 0.903, 0.822, respectively). On multivariable analysis, the Nassar operative difficultly scale was found to be a significant independent predictor of operative duration, conversion to open surgery, 30-day complications and 30-day reintervention (all p < 0.001). Conclusion We have shown that an operative difficulty scale can standardise the description of operative findings by multiple grades of surgeons to facilitate audit, training assessment and research. It provides a tool for reporting operative findings, disease severity and technical difficulty and can be utilised in future research to reliably compare outcomes according to case mix and intra-operative difficulty