An OregonGreen488-labelled d-amino acid for visualizing peptidoglycan by super-resolution STED nanoscopy.

Fluorescent d-amino acids (FDAAs) are molecular probes that are widely used for labelling the peptidoglycan layer of bacteria. When added to growing cells they are incorporated into the stem peptide by a transpeptidase reaction, allowing the timing and localization of peptidoglycan synthesis to be determined by fluorescence microscopy. Herein we describe the chemical synthesis of an OregonGreen488-labelled FDAA (OGDA). We also demonstrate that OGDA can be efficiently incorporated into the PG of Gram-positive and some Gram-negative bacteria, and imaged by super-resolution stimulated emission depletion (STED) nanoscopy at a resolution well below 100 nm.

Automatic Detection of Epileptic Seizures in EEG Using Sparse CSP and Fisher Linear Discrimination Analysis Algorithm.

In order to realize the automatic epileptic seizure detection, feature extraction and classification of electroencephalogram (EEG) signals are performed on the interictal, the pre-ictal, and the ictal status of epilepsy patients. There is no effective strategy for selecting the number of channels and spatial filters in feature extraction of multichannel EEG data. Therefore, this paper combined sparse idea and greedy search algorithm to improve the feature extraction of common space pattern. The feature extraction can effectively overcome the repeating selection problem of feature pattern in the eigenvector space by the traditional method. Then we used the Fisher linear discriminant analysis to realize the classification. The results show that the proposed method can get high classification accuracy using fewer data. For 10 subjects, the averaged accuracy of epilepsy detection is more than 99%. So, the detection of an epileptic seizure based on sparse features using Fisher linear discriminant analysis classifiers is more suitable for a reliable, automatic epileptic seizure detection system to enhance the patient's care and the quality of life.

An idle state-detecting method based on transient visual evoked potentials for an asynchronous ERP-based BCI.

BACKGROUND: An asynchronous brain-computer interface (BCI) allows subject to freely switch between the working state and the idle state, improving the subject's comfort. However, using only the event-related potential (ERP) to detect these two states is difficult because of the small amplitude of the ERP. METHOD: Our previous study finds that an odd-ball paradigm could evoke transient visual evoked potentials (TSVEPs) simultaneously with ERPs. This study adopts the TSVEP and the ERP to detect the idle state in the design of an asynchronous TSVEP-ERP-based BCI (T-E BCI). The T-E BCI extracts time and frequency features from brain signals and uses a novel probability-based fisher linear discriminant analysis (P-FLDA) to combine the classification results of the ERP and the TSVEP. RESULT: Ten subjects perform visual speller and video watching experiments, and their brain signals are measured under the working and idle states. The main results show that the T-E BCI achieves a higher accuracy than the ERP-based BCI when judging the subject's intentions and the two states. The P-FLDA performs better than the FLDA in combining the classification results. CONCLUSIONS: The study demonstrates that adding the TSVEP can substantially reduce the number of wrongly detected trials. The T-E BCI provides a new way of designing an asynchronous BCI without adding any additional visual stimuli, which makes the BCI more practical.

Investigation of the mirrored-word reading paradigm for BCI implementation.

Brain-computer interface (BCI) applications such as keyboard control and vehicular navigation present significant assistive merit for disabled individuals. However, there are limitations associated with BCI paradigms which restrict a wider adoption of BCI technology. For example, rapid serial visual presentation (RSVP) paradigms can induce seizures in photosensitive epileptic subjects. This paper evaluates the novel mirrored-word reading paradigm (MWRP) for BCI implementation using an offline experimental study. The offline study obtained an average single-trial classification accuracy of 74.10%. The results also demonstrate that the use of multiple trials for classification can increase the accuracy as is common with BCIs. The developed MWRP-based BCI also utilized a low presentation frequency which averts the possibility of paradigm induced photosensitivity. However, there are multiple avenues for future work. The MWRP can be implemented in the online format for real-time device control. For example, a vehicular application platform can be used where the word orientation represents directions for travel. The MWRP can also be investigated across a wider range of stimulus presentation parameters such as timing, color and stimulus size. Such studies can be used to suggest further improvements to the paradigm which can enhance its applicability for online device control.

Discriminating growth stages of an endangered Mediterranean relict plant (Ammopiptanthus mongolicus) in the arid Northwest China using hyperspectral measurements.

Ammopiptanthus mongolicus, the only drought-resistant, leguminous, evergreen shrub in the desert region of China, is endangered due to climate change and its growth stages urgently need to be non-destructively detected. Although many spectral indexes have been proposed for characterizing vegetation, the relationships are often inconsistent, making it challenging to characterize the status of vegetation across all growth stages. This study investigated the Spectral Features of the endangered desert plant A. mongolicus at different growth stages, and extracted the identified Spectral Features for the establishment of detection and discrimination models using Partial Least Square Regression (PLSR) and Fisher Linear Discriminate Analysis (FLDA), respectively. The results showed spectral reflectance of A. mongolicus differed across different growth stages and it generally increased with the degree of senescence. Poor performance was found in the single factor model, with RMSE ranging from 20.34 to 27.39 or Overall Accuracy of 60% in the validation datasets. The multivariate PLSR model, based on Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), Physiological Reflectance Index (PhRI) and Plant Senescence Reflectance Index (PSRI), turned out to be accurate in detecting the growth stages, with R2 of 0.89 and RMSE of 12.46, and the performance of the multivariate FLDA model based on 14 Spectral Features was acceptable, with an Overall Accuracy of 89% in the validation datasets. This research provides useful insights for timely and non-destructively discriminating different growth stages by using multivariate PLSR and FLDA analysis.

Fetal size and growth velocity in chronic hypertension.

OBJECTIVE: To investigate longitudinal fetal growth and growth velocity for commonly measured biometric parameters in women with chronic hypertension. METHODS: Two centre retrospective European study of women with chronic hypertension ascertained at pregnancy booking. Ultrasound measurements of head circumference (HC), abdominal circumference (AC) and femur length (FL) were used to derive longitudinal fetal growth charts derived using functional linear discriminant analysis (FLDA). These were compared to existing cross sectional and longitudinal charts, as was birthweight. RESULTS: 282 women with a median of 3 third trimester ultrasound examinations were included. Gestation at delivery was 37.5weeks (SD 2.68), birthweight 3049g (SD 785). Birthweight <10th percentile found in 15.6% deliveries, >90th percentile 20.2%. Fetal size curves derived from women with chronic hypertension were no different to cross sectional and longitudinal charts for a normal population. Compared to a standard longitudinal biometry chart, growth velocity (mm/day) in chronic hypertension was higher for AC and FL at 30-32weeks (AC 1.447vs 1.357 p<0.05; FL 0.296vs 0.269 p<0.01) and 34-36weeks (AC 1.325vs 1.140 p<0.01; FL 0.248vs 0.198 p<0.01). CONCLUSIONS: In women with chronic hypertension there is an excess of both SGA and LGA babies compared to population standards. Growth velocity of the AC and FL was greater after 30weeks compared to a normal population.

Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier.

Improving the performance of computer-aided detection (CAD) system for pulmonary nodules is still an important issue for its future clinical applications. This study aims to develop a new CAD scheme for pulmonary nodule detection based on dynamic self-adaptive template matching and Fisher linear discriminant analysis (FLDA) classifier. We first segment and repair lung volume by using OTSU algorithm and three-dimensional (3D) region growing. Next, the suspicious regions of interest (ROIs) are extracted and filtered by applying 3D dot filtering and thresholding method. Then, pulmonary nodule candidates are roughly detected with 3D dynamic self-adaptive template matching. Finally, we optimally select 11 image features and apply FLDA classifier to reduce false positive detections. The performance of the new method is validated by comparing with other methods through experiments using two groups of public datasets from Lung Image Database Consortium (LIDC) and ANODE09. By a 10-fold cross-validation experiment, the new CAD scheme finally has achieved a sensitivity of 90.24% and a false-positive (FP) of 4.54 FP/scan on average for the former dataset, and a sensitivity of 84.1% with 5.59 FP/scan for the latter. By comparing with other previously reported CAD schemes tested on the same datasets, the study proves that this new scheme can yield higher and more robust results in detecting pulmonary nodules.