A novel approach for estimation of aboveground biomass of a carbon-rich mangrove site in India

Mangroves can play a crucial part in climate change mitigation policies due to their high carbon-storing capacity. However, the carbon sequestration potential of Indian mangroves generally remained unexplored to date. In this study, multi-temporal Sentinel-1 and 2 data-derived variables were used to estimate the AGB of a tropical carbon-rich mangrove forest of India. Ensemble prediction of multiple machine learning algorithms, including Random Forest (RF), Gradient Boosted Model (GBM), and Extreme Gradient Boosting (XGB), were used for AGB prediction. The multi-temporal dataset was used in two different ways to find the most suitable method of using them. The results of the analysis showed that the modeling field measured AGB with individual date data values results in estimates with root mean square errors (RMSE) ranging from 149.242 t/ha for XGB to 151.149 t/ha for the RF. Modeling AGB with the average and percentile metrics of the multi-temporal image stack improves the prediction accuracy of AGB, with RMSE ranging from 81.882 t/ha for the XGB to 74.493 t/ha for the RF. The AGB modeling using ensemble prediction showed further improvement in accuracy with an RMSE of 72.864 t/ha and normalized RMSE of 11.38%. In this study, the intra-seasonal variation of Sentinel-1 and 2 data for mangrove ecosystems was explored for the first time. The variations in remotely sensed variables could be attributed mainly to soil moisture availability and rainfall in the mangrove ecosystem. The efficiency of Sentinel-1 and 2 data-derived variables and ensemble prediction of machine learning models for Indian mangroves were also explored for the first time. The methodologies established in this study can be used in the future for accurate prediction and repeated monitoring of AGB for mangrove ecosystems.

A Novel Feature Extraction Framework for Four Class Motor Imagery Classification using Log Determinant Regularized Riemannian Manifold

Brain-Computer Interface (BCI) systems allow the person in communicating with the external world using Electroencephalography (EEG). Motor Imagery (MI) based BCI systems play a vital role in interacting with the external environment. In this paper, we propose a novel robust feature extraction and classification framework for four class MI classification to improve the classification accuracy. The proposed architecture is developed using log-determinant (log-det) based Regularized Riemannian mean (LDRRM) and linear SVM. The robustness of features extracted from the four class MI data is improved to the outliers and noise by using the proposed LDRRM framework. We evaluated the performance of the proposed LDRRM classification framework on publicly available four class MI dataset 2a of BCI competition IV. The performance results show that the proposed LDRRM classification architecture obtained a mean classification accuracy of 69.12%, also achieved 1.54% higher classification accuracy when compared with the existing studies.

A Real-Time Health 4.0 Framework with Novel Feature Extraction and Classification for Brain-Controlled IoT-Enabled Environments

In this letter, we propose two novel methods for four-class motor imagery (MI) classification using electroencephalography (EEG). Also, we developed a real-time health 4.0 (H4.0) architecture for brain-controlled internet of things (IoT) enabled environments (BCE), which uses the classified MI task to assist disabled persons in controlling IoT-enabled environments such as lighting and heating, ventilation, and air-conditioning (HVAC). The first method for classification involves a simple and low-complex classification framework using a combination of regularized Riemannian mean (RRM) and linear SVM. Although this method performs better compared to state-of-the-art techniques, it still suffers from a nonnegligible misclassification rate. Hence, to overcome this, the second method offers a persistent decision engine (PDE) for the MI classification, which improves classification accuracy (CA) significantly. The proposed methods are validated using an in-house recorded four-class MI data set (data set I, collected over 14 subjects), and a four-class MI data set 2a of BCI competition IV (data set II, collected over 9 subjects). The proposed RRM architecture obtained average CAs of 74.30% and 67.60% when validated using datasets I and II, respectively. When analyzed along with the proposed PDE classification framework, an average CA of 92.25% on 12 subjects of data set I and 82.54% on 7 subjects of data set II is obtained. The results show that the PDE algorithm is more reliable for the classification of four-class MI and is also feasible for BCE applications. The proposed low-complex BCE architecture is implemented in real time using Raspberry Pi 3 Model B+ along with the Virgo EEG data acquisition system. The hardware implementation results show that the proposed system architecture is well suited for body-wearable devices in the scenario of Health 4.0. We strongly feel that this study can aid in driving the future scope of BCE research.

Isolation and characterization of a green tissue-specific promoter from pigeonpea [Cajanus cajan (L.) Millsp.]

Expression of rbcS genes encoding small subunit of rubisco, most abundant protein in green tissue, is regulated by at least three parameters--tissue type, light conditions and stage of development. One of the green tissue-specific promoters of rbcS gene family was isolated from pigeonpea by PCR. Expression of uidA gene encoding beta-glucuronidase in the transgenic tobacco plants under the control of pigeonpea rbcS promoter, clearly showed that this promoter was as strong as pea rbcS3A promoter characterized earlier. Study of the sequence similarity with pea rbcS3A promoter, especially the region (boxes I and III) that is required for rbcS3A expression, showed more than 50% divergence. In contrast, pigeonpea promoter sequence isolated in the present study was more similar to that of spinach and rice rbcS promoters.

Monomethylarsonous acid (MMA(III)) and arsenite: LD(50) in hamsters and in vitro inhibition of pyruvate dehydrogenase

Monomethylarsonous acid (MMA(III)), a metabolite of inorganic arsenic, has received very little attention from investigators of arsenic metabolism in humans. MMA(III), like sodium arsenite, contains arsenic in the +3 oxidation state. Although we have previously demonstrated that it is more toxic than arsenite in cultured Chang human hepatocytes, there are no data showing in vivo toxicity of MMA(III). When MMA(III) or sodium arsenite was administered intraperitoneally to hamsters, the LD(50)s were 29.3 and 112.0 micromol/kg of body wt, respectively. In addition, inhibition of hamster kidney or purified porcine heart pyruvate dehydrogenase (PDH) activity by MMA(III) or arsenite was determined. To inhibit hamster kidney PDH activity by 50%, the concentrations (mean +/- SE) of MMA(III) as methylarsine oxide, MMA(III) as diiodomethylarsine, and arsenite were 59.9 +/- 6.5, 62.0 +/- 1.8, and 115.7 +/- 2.3 microM, respectively. To inhibit activity of purified porcine heart PDH activity by 50%, the concentrations (mean +/- SE) of MMA(III) as methylarsine oxide and arsenite were 17.6 +/- 4.1 and 106.1 +/- 19.8 microM, respectively. These data demonstrate that MMA(III) is more toxic than inorganic arsenite, both in vivo and in vitro, and call into question the hypothesis that methylation of inorganic arsenic is a detoxication process.

Use of duodenal drainage in the diagnosis of acalculous gallbladder disease

Because of overreliance on oral cholecystograms, we have re-evaluated a little used laboratory test, duodenal drainage. In a series of 56 patients with normal oral cholecystograms and upper GI series who underwent duodenal drainage and cholecystectomy, 53 (95%) were found to have positive microscopic examinations. We think duodenal drainage is a safe, simple test which should be used as an adjunct to history and physical examination and oral cholecystography. In regard to clinical follow-up, 96 per cent of patients followed were judged to have excellent-to-good results.