Reliability and performance of the IRRAflow® system for intracranial lavage and evacuation of hematomas-A technical note.

BACKGROUND: Intraventricular hemorrhage (IVH) is a severe condition with poor outcomes and high mortality. IRRAflow® (IRRAS AB) is a new technology introduced to accelerate IVH clearance by minimally invasive wash-out. The IRRAflow® system performs active and controlled intracranial irrigation and aspiration with physiological saline, while simultaneously monitoring and maintaining a stable intracranial pressure (ICP). We addressed important aspects of the device implementation and intracranial lavage. METHOD: To allow versatile investigation of multiple device parameters, we designed an ex vivo lab setup. We evaluated 1) compatibility between the IRRAflow® catheter and the Silverline f10 bolt (Spiegelberg), 2) the physiological and hydrodynamic effects of varying the IRRAflow® settings, 3) the accuracy of the IRRAflow® injection volumes, and 4) the reliability of the internal ICP monitor of the IRRAflow®. RESULTS: The IRRAflow® catheter was not compatible with Silverline bolt fixation, which was associated with leakage and obstruction. Design space exploration of IRRAflow® settings revealed that appropriate settings included irrigation rate 20 ml/h with a drainage bag height at 0 cm, irrigation rate 90 ml/h with a drainage bag height at 19 cm and irrigation rate 180 ml/h with a drainage bag height at 29 cm. We found the injection volume performed by the IRRAflow® to be stable and reliable, while the internal ICP monitor was compromised in several ways. We observed a significant mean drift difference of 3.16 mmHg (variance 0.4, p = 0.05) over a 24-hour test period with a mean 24-hour drift of 3.66 mmHg (variance 0.28) in the pressures measured by the IRRAflow® compared to 0.5 mmHg (variance 1.12) in the Raumedic measured pressures. CONCLUSION: Bolting of the IRRAflow® catheter using the Medtronic Silverline® bolt is not recommendable. Increased irrigation rates are recommendable followed by a decrease in drainage bag level. ICP measurement using the IRRAflow® device was unreliable and should be accompanied by a control ICP monitor device in clinical settings.

Effect of nano-silicon on the regulation of ascorbate-glutathione contents, antioxidant defense system and growth of copper stressed wheat (Triticum aestivum L.) seedlings.

Copper (Cu2+) toxicity can inhibit plant growth and development. It has been shown that silicon (Si) can relieve Cu2+ stress. However, it is unclear how Si-nanoparticles (SiNPs) relieve Cu2+ stress in wheat seedlings. Therefore, the current study was conducted by setting up four treatments: CK, SiNP: (2.5 mM), Cu2+: (500 µM), and SiNP+Cu2+: (2.5 mM SiNP+500 µM Cu2+) to explore whether SiNPs can alleviate Cu2+ toxicity in wheat seedlings. The results showed that Cu2+ stress hampered root and shoot growth and accumulated high Cu2+ concentrations in roots (45.35 mg/kg) and shoots (25.70 mg/kg) of wheat as compared to control treatment. Moreover, Cu2+ treatment inhibited photosynthetic traits and chlorophyll contents as well as disturbed the antioxidant defense system by accumulating malondialdehyde (MDA) and hydrogen peroxidase (H2O2) contents. However, SiNPs treatment increased root length and shoot height by 15.1% and 22%, respectively, under Cu2+ toxicity. Moreover, SiNPs application decreased MDA and H2O2 contents by 31.25% and 19.25%, respectively. SiNPs increased non-enzymatic compounds such as ascorbic acid-glutathione (AsA-GSH) and enhanced superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbic peroxidase (APX) activities by 77.5%, 141.7%, 68%, and 80%, respectively. Furthermore, SiNPs decreased Cu2+ concentrations in shoots by 26.2%, as compared to Cu2+ treatment alone. The results concluded that SiNPs could alleviate Cu2+ stress in wheat seedlings. The present investigation may help to increase wheat production in Cu2+ contaminated soils.

Multiscale image denoising using goodness-of-fit test based on EDF statistics.

Two novel image denoising algorithms are proposed which employ goodness of fit (GoF) test at multiple image scales. Proposed methods operate by employing the GoF tests locally on the wavelet coefficients of a noisy image obtained via discrete wavelet transform (DWT) and the dual tree complex wavelet transform (DT-CWT) respectively. We next formulate image denoising as a binary hypothesis testing problem with the null hypothesis indicating the presence of noise and the alternate hypothesis representing the presence of desired signal only. The decision that a given wavelet coefficient corresponds to the null hypothesis or the alternate hypothesis involves the GoF testing based on empirical distribution function (EDF), applied locally on the noisy wavelet coefficients. The performance of the proposed methods is validated by comparing them against the state of the art image denoising methods.

A Strategy for Classification of "Vaginal vs. Cesarean Section" Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings.

We propose objective and robust measures for the purpose of classification of "vaginal vs. cesarean section" delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics.

A Multiscale Denoising Framework Using Detection Theory with Application to Images from CMOS/CCD Sensors.

Output from imaging sensors based on CMOS and CCD devices is prone to noise due to inherent electronic fluctuations and low photon count. The resulting noise in the acquired image could be effectively modelled as signal-dependent Poisson noise or as a mixture of Poisson and Gaussian noise. To that end, we propose a generalized framework based on detection theory and hypothesis testing coupled with the variance stability transformation (VST) for Poisson or Poisson⁻Gaussian denoising. VST transforms signal-dependent Poisson noise to a signal independent Gaussian noise with stable variance. Subsequently, multiscale transforms are employed on the noisy image to segregate signal and noise into separate coefficients. That facilitates the application of local binary hypothesis testing on multiple scales using empirical distribution function (EDF) for the purpose of detection and removal of noise. We demonstrate the effectiveness of the proposed framework with different multiscale transforms and on a wide variety of input datasets.

Dual tree complex wavelet transform-based signal denoising method exploiting neighbourhood dependencies and goodness-of-fit test.

A novel signal denoising method is proposed whereby goodness-of-fit (GOF) test in combination with a majority classifications-based neighbourhood filtering is employed on complex wavelet coefficients obtained by applying dual tree complex wavelet transform (DT-CWT) on a noisy signal. The DT-CWT has proven to be a better tool for signal denoising as compared to the conventional discrete wavelet transform (DWT) owing to its approximate translation invariance. The proposed framework exploits statistical neighbourhood dependencies by performing the GOF test locally on the DT-CWT coefficients for their preliminary classification/detection as signal or noise. Next, a deterministic neighbourhood filtering approach based on majority noise classifications is employed to detect false classification of signal coefficients as noise (via the GOF test) which are subsequently restored. The proposed method shows competitive performance against the state of the art in signal denoising.

Functional characterization of soybean strigolactone biosynthesis and signaling genes in Arabidopsis MAX mutants and GmMAX3 in soybean nodulation.

BACKGROUND: Strigolactones (SLs) play important roles in controlling root growth, shoot branching, and plant-symbionts interaction. Despite the importance, the components of SL biosynthesis and signaling have not been unequivocally explored in soybean. RESULTS: Here we identified the putative components of SL synthetic enzymes and signaling proteins in soybean genome. Soybean genome contains conserved MORE AXILLARY BRANCHING (MAX) orthologs, GmMAX1s, GmMAX2s, GmMAX3s, and GmMAX4s. The tissue expression patterns are coincident with SL synthesis in roots and signaling in other tissues under normal conditions. GmMAX1a, GmMAX2a, GmMAX3b, and GmMAX4a expression in their Arabidopsis orthologs' mutants not only restored most characteristic phenotypes, such as shoot branching and shoot height, leaf shape, primary root length, and root hair growth, but also restored the significantly changed hormone contents, such as reduced JA and ABA contents in all mutant leaves, but increased auxin levels in atmax1, atmax3 and atmax4 mutants. Overexpression of these GmMAXs also altered the hormone contents in wild-type Arabidopsis. GmMAX3b was further characterized in soybean nodulation with overexpression and knockdown transgenic hairy roots. GmMAX3b overexpression (GmMAX3b-OE) lines exhibited increased nodule number while GmMAX3b knockdown (GmMAX3b-KD) decreased the nodule number in transgenic hairy roots. The expression levels of several key nodulation genes were also altered in GmMAX3b transgenic hairy roots. GmMAX3b overexpression hairy roots had reduced ABA, but increased JA levels, with no significantly changed auxin content, while the contrast changes were observed in GmMAX3b-KD lines. Global gene expression in GmMAX3b-OE or GmMAX3b-KD hairy roots also revealed that altered expression of GmMAX3b in soybean hairy roots changed several subsets of genes involved in hormone biosynthesis and signaling and transcriptional regulation of nodulation processes. CONCLUSIONS: This study not only revealed the conservation of SL biosynthesis and signaling in soybean, but also showed possible interactions between SL and other hormone synthesis and signaling during controlling plant development and soybean nodulation. GmMAX3b-mediated SL biosynthesis and signaling may be involved in soybean nodulation by affecting both root hair formation and its interaction with rhizobia.

Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition.

We present a data driven approach to classify ictal (epileptic seizure) and non-ictal EEG signals using the multivariate empirical mode decomposition (MEMD) algorithm. MEMD is a multivariate extension of empirical mode decomposition (EMD), which is an established method to perform the decomposition and time-frequency (T-F) analysis of non-stationary data sets. We select suitable feature sets based on the multiscale T-F representation of the EEG data via MEMD for the classification purposes. The classification is achieved using the artificial neural networks. The efficacy of the proposed method is verified on extensive publicly available EEG datasets.

Neurally adjusted ventilatory assist (NAVA) mode as an adjunct diagnostic tool in congenital central hypoventilation syndrome.

A full term female newborn was admitted to the neonatal intensive care unit (NICU) for continuous observation of apnea. Infant was noted to have apnea while asleep requiring intubation and mechanical ventilation. A video EEG was performed which demonstrated normal awake background without any seizure activity. Neurally adjusted ventilatory assist (NAVA) demonstrated the absence of electrical activity of the diaphragm (Edi) when the patient was in quiet phase of sleep. This finding on NAVA monitor raised the suspicion of central hypoventilation syndrome (CCHS) which was confirmed by genetic identification of the PHOX2B mutation.

Application of multivariate empirical mode decomposition for seizure detection in EEG signals.

We present a method for the analysis of electroencephalogram (EEG) signals which has the potential to distinguish between ictal and seizure-free intracranial EEG recordings. This is achieved by analyzing common frequency components in multichannel EEG recordings, using the multivariate empirical mode decomposition (MEMD) algorithm. The mean frequency of the signal is calculated by applying the Hilbert-Huang transform on the resulting intrinsic mode functions (IMFs). It has been shown that the mean frequency estimates for the ictal and seizure-free EEG recordings are statistically different, and hence, can serve as a test statistic to distinguish between the two classes of signals. Simulation results on real world EEG signals support the analysis and demonstrate the potential of the proposed scheme.