Impact of Commercial Sugar as a Substrate in Single-Chamber Microbial Fuel Cells to Improve the Energy Production with Bioremediation of Metals

Microbial fuel cells (MFCs) have emerged as a viable method for bioremediation of toxic metals while also producing energy. In this paper, we examine the issue of organic substrate as a source of metabolism for microbe growth in MFC, as well as its significance for metal ion degradation in tandem with energy production. This study focused on the use of commercial sugar as an organic substrate in a single-chamber MFC. The MFC was operated for 27 days, with the highest voltage of 150 mV achieved on day 12, and toxic metal bioremediation efficiencies of 89%, 76.45%, and 89.45% for Pb2+, Cd2+, and Hg2+, respectively. Every 24 hours, the organic substrate (sugar solution) was fed into the cell. This study’s mechanism of metal ion degradation and electron transport is also thoroughly described. In addition, some future views have been highlighted

Classification of Deep-SAT Images under Label Noise

One of the challenges of training artificial intelligence models for classifying satellite images is the presence of label noise in the datasets that are sometimes crowd-source labeled and as a result, somewhat error prone. In our work, we have utilized three labeled satellite image datasets namely, SAT-6, SAT-4, and EuroSAT. The combined dataset consists of over 900,000 image patches that belong to a land cover class. We have applied some standard pixel-based feature extraction algorithms to extract features from the images and then trained those features with various machine learning algorithms. In our experiment, three types of artificial label noises are injected – Noise Completely at Random (NCAR), Noise at Random (NAR) and Noise Not at Random (NNAR) to the training datasets. The noisy data are used to train the algorithms, and the effect of noise on the algorithm performance are compared with noise-free test sets. From our study, the Random Forest and the Back-propagation Neural Network classifiers are found to be the least sensitive to label noises. As label noise is a common scenario in human-labeled image datasets, the current research initiative will help the development of noise robust classification methods for various relevant applications

Sensitive Detection of Motor Neuron Disease Derived Exosomal miRNA Using Electrocatalytic Activity of Gold-Loaded Superparamagnetic Ferric Oxide Nanocubes

© 2020 Wiley-VCH GmbH Dysregulated microRNA associated pathways contribute to the pathology of neurological disorders, hence presenting themselves as a potential candidate for motor neuron disease (MND) diagnosis. Herein, we reported an enzymatic amplification-free approach for the electrochemical detection of exosomal microRNA (miR-338-3p) from preconditioned media of motor neurons obtained from amyotrophic lateral sclerosis (ALS) patients and healthy controls. Our assay utilizes a three-step strategy that involves i) initial isolation and purification of exosomal miR-338-3p from patients and healthy controls using biotinylated complementary capture probe followed by heat-release of the specific target, ii) direct adsorption of target miR-338-3p onto the gold-loaded ferric oxide nanocatalyst (AuNP-Fe2O3NC) through affinity interaction between microRNA and exposed gold surfaces within the AuNP-Fe2O3NC, and iii) gold nanocatalyst-induced electrocatalytic signal amplification through methylene blue-ferricyanide redox cycling (MB/[Fe(CN)6]3−). The electrocatalytic signal is monitored by using chronocoulometry at the AuNP–Fe2O3NC-modified screen-printed carbon electrode (AuNP-Fe2O3NC/SPCE). We demonstrated the detection of miR-338-3p as low as 100 aM in spiked buffer samples with a relative standard deviation of (%RSD) \u3c5.0 % (n=5). We also demonstrate the successful detection of miR-338-3p from a small cohort of preconditioned media of motor neurons obtained from ALS patients and healthy controls. The sensor avoids the use of conventional recognition and transduction layers in hybridization-based electrochemical miRNA biosensors, polymerase-based amplifications. It is robust, fast (\u3c2.5 h) and potentially applicable to a wide variety of RNA biomarker detection

Sensitive detection of Motor Neuron Disease (MND) derived exosomal miRNA using electrocatalytic activity of gold‐loaded superparamagnetic ferric oxide nanocubes

Dysregulated microRNA associated pathways contribute to the pathology of neurological disorders, hence presenting themselves as a potential candidate for motor neuron disease (MND) diagnosis. Herein, we reported an enzymatic amplification‐free approach for the electrochemical detection of exosomal microRNA (miR‐338‐3p) from preconditioned media of motor neurons obtained from amyotrophic lateral sclerosis (ALS) patients and healthy controls. Our assay utilizes a three‐step strategy that involves i) initial isolation and purification of exosomal miR‐338‐3p from patients and healthy controls using biotinylated complementary capture probe followed by heat‐release of the specific target, ii) direct adsorption of target miR‐338‐3p onto the gold‐loaded ferric oxide nanocatalyst (AuNP‐Fe2O3NC) through affinity interaction between microRNA and exposed gold surfaces within the AuNP‐Fe2O3NC, and iii) gold nanocatalyst‐induced electrocatalytic signal amplification through methylene blue‐ferricyanide redox cycling (MB/[Fe(CN)6]3−). The electrocatalytic signal is monitored by using chronocoulometry at the AuNP–Fe2O3NC‐modified screen‐printed carbon electrode (AuNP‐Fe2O3NC/SPCE). We demonstrated the detection of miR‐338‐3p as low as 100 aM in spiked buffer samples with a relative standard deviation of (%RSD