Straw Incorporation with Exogenous Degrading Bacteria (ZJW-6): An Integrated Greener Approach to Enhance Straw Degradation and Improve Rice Growth.

Rice straw is an agricultural waste, the disposal of which through open burning is an emerging challenge for ecology. Green manufacturing using straw returning provides a more avant-garde technique that is not only an effective management measure to improve soil fertility in agricultural ecosystems but also nurtures environmental stewardship by reducing waste and the carbon footprint. However, fresh straw that is returned to the field cannot be quickly decomposed, and screening microorganisms with the capacity to degrade straw and understanding their mechanism of action is an efficient approach to solve such problems.

Influence of extrinsic inputs and synaptic gains on dynamics of Wendling's neural mass model: A bifurcation analysis.

We analyze the neurodynamics attributed by a model proposed by Wendling and co-workers (2002) [Wendling, F., Bartolomei, F., Bellanger, J.

Epileptogenic zone localization and seizure control in coupled neural mass models.

Exact localization of the epileptogenic zone (EZ) is the first priority for ensuring epilepsy treatments and reducing side effects. The results of traditional visual methods for localizing the origin of seizures are far from satisfactory in some cases. Signal processing methods could extract substantial information that may complement visual inspection of EEG signals.

Seizure detection approach using S-transform and singular value decomposition.

Automatic seizure detection plays a significant role in the diagnosis of epilepsy. This paper presents a novel method based on S-transform and singular value decomposition (SVD) for seizure detection. Primarily, S-transform is performed on EEG signals, and the obtained time-frequency matrix is divided into submatrices.

Bifurcation and oscillation in a time-delay neural mass model.

The neural mass model developed by Lopes da Silva et al. simulates complex dynamics between cortical areas and is able to describe a limit cycle behavior for alpha rhythms in electroencephalography (EEG). In this work, we propose a modified neural mass model that incorporates a time delay.

Tissue-specific and cation/anion-specific DNA methylation variations occurred in C. virgata in response to salinity stress.

Salinity is a widespread environmental problem limiting productivity and growth of plants. Halophytes which can adapt and resist certain salt stress have various mechanisms to defend the higher salinity and alkalinity, and epigenetic mechanisms especially DNA methylation may play important roles in plant adaptability and plasticity. In this study, we aimed to investigate the different influences of various single salts (NaCl, Na2SO4, NaHCO3, Na2CO3) and their mixed salts on halophyte Chloris.

Feature extraction and recognition of ictal EEG using EMD and SVM.

Automatic seizure detection is significant for long-term monitoring of epilepsy, as well as for diagnostics and rehabilitation, and can decrease the duration of work required when inspecting the EEG signals. In this study we propose a novel method for feature extraction and pattern recognition of ictal EEG, based upon empirical mode decomposition (EMD) and support vector machine (SVM). First the EEG signal is decomposed into Intrinsic Mode Functions (IMFs) using EMD, and then the coefficient of variation and fluctuation index of IMFs are extracted as features.

Synchronization regulation in a model of coupled neural masses.

A model of coupled neural masses can generate seizure-like events and dynamics similar to those observed during interictal to ictal transitions and thus can be used for theoretical study of the control of epileptic seizures. In an effort to understand the mechanisms underlying epileptic seizures and how to avoid them, we added a control input to this model. Epileptic seizures are always accompanied by hypersynchronous firing of neurons, so research on synchronization among cortical areas is significant for seizure control.

EEG non-linear feature extraction using correlation dimension and Hurst exponent.

In this work, we evaluated the differences between epileptic electroencephalogram (EEG) and interictal EEG by computing some non-linear features. Correlation dimension (CD) and Hurst exponent (H) were calculated for 100 segments of epileptic EEG and 100 segments of interictal EEG. A comparison was made between epileptic EEG and interictal EEG in those non-linear parameters.