ConTraNet: A hybrid network for improving the classification of EEG and EMG signals with limited training data.

OBJECTIVE: Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals is however non-trivial because of the time-varying and non-stationary characteristics. Furthermore, existence of short- and long-range dependencies in these time-series signal makes the decoding even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures for the classification of these bio-signals, which are proven useful to learn spatial representations. However, CNNs because of the fixed size convolutional kernels and shared weights pay only uniform attention and are also suboptimal in learning short-long term dependencies, simultaneously, which could be pivotal in decoding EEG and EMG signals. Therefore, it is important to address these limitations of CNNs. To learn short- and long-range dependencies simultaneously and to pay more attention to more relevant part of the input signal, Transformer neural network-based architectures can play a significant role. Nonetheless, it requires a large corpus of training data. However, EEG and EMG decoding studies produce limited amount of the data. Therefore, using standalone transformers neural networks produce ordinary results. In this study, we ask a question whether we can fix the limitations of CNN and transformer neural networks and provide a robust and generalized model that can simultaneously learn spatial patterns, long-short term dependencies, pay variable amount of attention to time-varying non-stationary input signal with limited training data. APPROACH: In this work, we introduce a novel single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that contains the strengths of both CNN and Transformer neural networks. ConTraNet uses a CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the short- and long-range or global dependencies in the signal and learn to pay different attention to different parts of the signals. MAIN RESULTS: We evaluated and compared the ConTraNet with state-of-the-art methods on four publicly available datasets (BCI Competition IV dataset 2b, Physionet MI-EEG dataset, Mendeley sEMG dataset, Mendeley sEMG V1 dataset) which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, 7-class, and 10-class decoding tasks). SIGNIFICANCE: With limited training data ConTraNet significantly improves classification performance on four publicly available datasets for 2, 3, 4, 7, and 10-classes compared to its counterparts.

Whole-genome SNP markers reveal runs of homozygosity in indigenous cattle breeds of Pakistan.

The runs of homozygosity (ROH) were identified in 14 Pakistani cattle breeds (n = 105) by genotyping with the Illumina 50 K SNP BeadChip. These breeds were categorized into Dairy, Dual, and Draft breeds based on their utility and production performance. We identified a total of 10,936 ROHs which mainly consisted of a high number of shorter segments (1-4 Mb). Dairy group exhibited the highest level of inbreeding (FROH: 0.078 ± 0.028) while the lowest (FROH: 0.002 ± 0.008) was observed in Dual group. In 48 genomic regions identified with a high frequency of ROH, 207 genes were detected in the three breed groups. A substantially higher number of ROH islands detected in dairy breeds indicated the impact of the positive selection pressure over the years. Important candidate genes and QTL were detected in the ROH islands associated with economic traits like milk production, reproduction, meat, carcass, and health traits in dairy cattle.

Genetic variants of CSN1S1, CSN2, CSN3, and BLG genes and their association with dairy production traits in Sahiwal cattle and Nili-Ravi buffaloes.

Milk protein genes are associated with milk yield and composition in dairy animals. The present study aimed to identify milk protein genes (CSN1S1, CSN2, CSN3, and BLG) genetic variants and their association with milk yield in Sahiwal cattle and Nili-Ravi buffaloes. One hundred animals from each species were selected to collect blood samples and milk production records. Primers were designed for these milk protein genes for PCR amplification. Sequencing of resultant PCR products revealed a higher number of SNPs (13 vs. 7, 5 vs. 1, and 6 vs. 2) in Sahiwal as compared to Nili-Ravi animals in CSN1S1, CSN2, and CSN3 genes, respectively. However, a single SNP was observed in BLG gene of both species. Association analysis revealed that one SNP in BLG gene of Nili-Ravi was associated (p < 0.05) with 305-day milk yield. Two SNPs at CSN1S1 gene in Sahiwal were associated with dry-period. Similarly, one SNP at CSN1S1 and two SNPs at CSN3 gene showed significant association (p < 0.05) with average calving-interval in Sahiwal while two SNPs in CSN1S1 gene were associated (p < 0.05) with this trait in Nili-Ravi. These SNPs could be helpful as candidate variants for marker-assisted selection in cattle and buffaloes for improvement of lactation performance.

Deep transfer learning compared to subject-specific models for sEMG decoders.

Objective. Accurate decoding of surface electromyography (sEMG) is pivotal for muscle-to-machine-interfaces and their application e.g. rehabilitation therapy. sEMG signals have high inter-subject variability, due to various factors, including skin thickness, body fat percentage, and electrode placement. Deep learning algorithms require long training time and tend to overfit if only few samples are available. In this study, we aim to investigate methods to calibrate deep learning models to a new user when only a limited amount of training data is available.Approach. Two methods are commonly used in the literature, subject-specific modeling and transfer learning. In this study, we investigate the effectiveness of transfer learning using weight initialization for recalibration of two different pretrained deep learning models on new subjects data and compare their performance to subject-specific models. We evaluate two models on three publicly available databases (non invasive adaptive prosthetics database 2-4) and compare the performance of both calibration schemes in terms of accuracy, required training data, and calibration time.Main results. On average over all settings, our transfer learning approach improves 5%-points on the pretrained models without fine-tuning, and 12%-points on the subject-specific models, while being trained for 22% fewer epochs on average. Our results indicate that transfer learning enables faster learning on fewer training samples than user-specific models.Significance. To the best of our knowledge, this is the first comparison of subject-specific modeling and transfer learning. These approaches are ubiquitously used in the field of sEMG decoding. But the lack of comparative studies until now made it difficult for scientists to assess appropriate calibration schemes. Our results guide engineers evaluating similar use cases.

Novel metallic stainless-steel mesh-supported conductive membrane and its performance in the electro-filtration process.

In this study, we demonstrate the fabrication of a thoroughly metallic electro-conductive membrane by using simple filtration to uniformly coat AgNWs dispersion through stainless steel (SUS)-mesh, which functions both as filter and a flexible conductive substrate. The as-prepared AgNWs networks layer on the SUS-mesh was further strengthened by electroplating Ag layers (P-SUS membrane); exhibiting an overall electrical conductivity of 9.2 × 104 S/m, which is up to 42 times greater than the conductivity of pristine SUS-mesh. The P-SUS membrane exhibited adequate physical durability against chemical and mechanical stresses under prolonged filtration, and high pure water flux of 534 ± 54 LMH/bar. This electro-membrane displayed the anticipated flux recovery in harvesting microalgae (Chlorella sp. HS-2) when filtration was done with the membrane used as a cathode: micro-sized bubbles, generated from the cathodic membrane, functioned to detach the foulants and recover the relative flux to a significant level. The P-SUS membrane indeed possesses necessary traits that the polymer-support membrane lacks, in terms of not only electrical conductivity and mechanical strength but also filtration performance with anti-fouling capability, all of which are of necessity to be considered workable electroconductive membrane.

Nutrigenomic Interventions to Address Metabolic Stress and Related Disorders in Transition Cows.

For dairy cattle, the period involving a shift from late pregnancy to early lactation termed transition or periparturient is an excruciating phase. Health-related disorders are likely to happen in this time frame. Timely postpartum and metabolic adjustments to this new physical state demands correct management strategies to fulfill the cow's needs for a successful transition to this phase. Among the management strategies, one of the most researched methods for managing transition-related stress is nutritional supplementation. Dietary components directly or indirectly affect the expression of various genes that are believed to be involved in various stress-related responses during this phase. Nutrigenomics, an interdisciplinary approach that combines nutritional science with omics technologies, opens new avenues for studying the genome's complicated interactions with food. This revolutionary technique emphasizes the importance of food-gene interactions on various physiological and metabolic mechanisms. In animal sciences, nutrigenomics aims to promote the welfare of livestock animals and enhance their commercially important qualities through nutritional interventions. To this end, an increasing volume of research shows that nutritional supplementation can be effectively used to manage the metabolic stress dairy cows undergo during the transition period. These nutritional supplements, including polyunsaturated fatty acids, vitamins, dietary amino acids, and phytochemicals, have been shown to modulate energy homeostasis through different pathways, leading to addressing metabolic issues in transition cows.

Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method.

Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs are pivotal. Here, we propose a feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a data augmentation method derived from l2-norm fast gradient sign method (FGSM), called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms the current state-of-the-art methods and yields classification accuracies of 90.7% on BCI competition II dataset III and 89.5%, 81.8%, 76.0% and 85.4%, 69.1%, 80.9% on different data distributions of BCI Competition IV dataset 2b and 2a, respectively.

Catalytic Conversion of Xylose to Furfural by p-Toluenesulfonic Acid (pTSA) and Chlorides: Process Optimization and Kinetic Modeling.

Furfural is one of the most promising precursor chemicals with an extended range of downstream derivatives. In this work, conversion of xylose to produce furfural was performed by employing p-toluenesulfonic acid (pTSA) as a catalyst in DMSO medium at moderate temperature and atmospheric pressure. The production process was optimized based on kinetic modeling of xylose conversion to furfural alongwith simultaneous formation of humin from xylose and furfural. The synergetic effects of organic acids and Lewis acids were investigated. Results showed that the catalyst pTSA-CrCl3·6H2O was a promising combined catalyst due to the high furfural yield (53.10%) at a moderate temperature of 120 °C. Observed kinetic modeling illustrated that the condensation of furfural in the DMSO solvent medium actually could be neglected. The established model was found to be satisfactory and could be well applied for process simulation and optimization with adequate accuracy. The estimated values of activation energies for xylose dehydration, condensation of xylose, and furfural to humin were 81.80, 66.50, and 93.02 kJ/mol, respectively.

Comparative efficacy of biogenic zinc oxide nanoparticles synthesized by Pseudochrobactrum sp. C5 and chemically synthesized zinc oxide nanoparticles for catalytic degradation of dyes and wastewater treatment.

Discharge of untreated textile wastewaters loaded with dyes is not only contaminating the soil and water resources but also posing a threat to the health and socioeconomic life of the people. Hence, there is a need to devise the strategies for effective treatment of such wastewaters. The present study reports the catalytic potential of biogenic ZnO nanoparticles (ZnO NPs) synthesized by using a bacterial strain Pseudochrobactrum sp. C5 for degradation of dyes and wastewater treatment. The catalytic potential of the biogenic ZnO NPs for degradation of dyes and wastewater treatment was also compared with that of the chemically synthesized ones. The characterization of the biogenic ZnO NPs through FT-IR, XRD, and field emission scanning electron microscopy (FESEM) indicated that these are granular agglomerated particles having a size range of 90-110 nm and zeta potential of -27.41 mV. These catalytic NPs had resulted into almost complete (> 90%) decolorization of various dyes including the methanol blue and reactive black 5. These NPs also resulted into a significant reduction in COD, TDS, EC, pH, and color of two real wastewaters spiked with reactive black 5 and reactive red 120. The findings of this study suggest that the biosynthesized ZnO NPs might serve as a potential green solution for treatment of dye-loaded textile wastewaters.

Quantifying the alignment error and the effect of incomplete somatosensory feedback on motor performance in a virtual brain-computer-interface setup.

Invasive brain-computer-interfaces (BCIs) aim to improve severely paralyzed patient's (e.g. tetraplegics) quality of life by using decoded movement intentions to let them interact with robotic limbs. We argue that the performance in controlling an end-effector using a BCI depends on three major factors: decoding error, missing somatosensory feedback and alignment error caused by translation and/or rotation of the end-effector relative to the real or perceived body. Using a virtual reality (VR) model of an ideal BCI decoder with healthy participants, we found that a significant performance loss might be attributed solely to the alignment error. We used a shape-drawing task to investigate and quantify the effects of robot arm misalignment on motor performance independent from the other error sources. We found that a 90° rotation of the robot arm relative to the participant leads to the worst performance, while we did not find a significant difference between a 45° rotation and no rotation. Additionally, we compared a group of subjects with indirect haptic feedback with a group without indirect haptic feedback to investigate the feedback-error. In the group without feedback, we found a significant difference in performance only when no rotation was applied to the robot arm, supporting that a form of haptic feedback is another important factor to be considered in BCI control.

SpikeDeep-classifier: a deep-learning based fully automatic offline spike sorting algorithm.

Objective.Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted electrode can record spike activity (SA) of one or more neurons along with background activity (BA). The aim of this study is to isolate SA of each neural source. This process is called spike sorting or spike classification. Advanced spike sorting algorithms are time consuming because of the human intervention at various stages of the pipeline. Current approaches lack generalization because the values of hyperparameters are not fixed, even for multiple recording sessions of the same subject. In this study, a fully automatic spike sorting algorithm called 'SpikeDeep-Classifier' is proposed. The values of hyperparameters remain fixed for all the evaluation data.Approach.The proposed approach is based on our previous study (SpikeDeeptector) and a novel background activity rejector (BAR), which are both supervised learning algorithms and an unsupervised learning algorithm (K-means). SpikeDeeptector and BAR are used to extract meaningful channels and remove BA from the extracted meaningful channels, respectively. The process of clustering becomes straight-forward once the BA is completely removed from the data. Then, K-means with a predefined maximum number of clusters is applied on the remaining data originating from neural sources only. Lastly, a similarity-based criterion and a threshold are used to keep distinct clusters and merge similar looking clusters. The proposed approach is called cluster accept or merge (CAOM) and it has only two hyperparameters (maximum number of clusters and similarity threshold) which are kept fixed for all the evaluation data after tuning.Main results.We compared the results of our algorithm with ground-truth labels. The algorithm is evaluated on data of human patients and publicly available labeled non-human primates (NHPs) datasets. The average accuracy of BAR on datasets of human patients is 92.3% which is further reduced to 88.03% after (K-means + CAOM). In addition, the average accuracy of BAR on a publicly available labeled dataset of NHPs is 95.40% which reduces to 86.95% after (K-mean + CAOM). Lastly, we compared the performance of the SpikeDeep-Classifier with two human experts, where SpikeDeep-Classifier has produced comparable results.Significance.The SpikeDeep-Classifier is evaluated on the datasets of multiple recording sessions of different species, different brain areas and different electrode types without further retraining. The results demonstrate that 'SpikeDeep-Classifier' possesses the ability to generalize well on a versatile dataset and henceforth provides a generalized and fully automated solution to offline spike sorting.Clinical trial registration numberThe clinical trial registration number for patients implanted with the Utah array isNCT 01849822.For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation. The Clinical trial registration number for the epilepsy patients implanted with microwires is16-5670.

SpikeDeeptector: a deep-learning based method for detection of neural spiking activity.

OBJECTIVE: In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain-computer interface (BCI) applications, in which only simple threshold crossing events are often considered for feature extraction. To our knowledge, there is no method that can universally and automatically identify channels containing neural data. In this study, we aim to identify and track channels containing neural data from implanted electrodes, automatically and more importantly universally. By universally, we mean across different recording technologies, different subjects and different brain areas. APPROACH: We propose a novel algorithm based on a new way of feature vector extraction and a deep learning method, which we call SpikeDeeptector. SpikeDeeptector considers a batch of waveforms to construct a single feature vector and enables contextual learning. The feature vectors are then fed to a deep learning method, which learns contextualized, temporal and spatial patterns, and classifies them as channels containing neural spike data or only noise. MAIN RESULTS: We trained the model of SpikeDeeptector on data recorded from a single tetraplegic patient with two Utah arrays implanted in different areas of the brain. The trained model was then evaluated on data collected from six epileptic patients implanted with depth electrodes, unseen data from the tetraplegic patient and data from another tetraplegic patient implanted with two Utah arrays. The cumulative evaluation accuracy was 97.20% on 1.56 million hand labeled test inputs. SIGNIFICANCE: The results demonstrate that SpikeDeeptector generalizes not only to the new data, but also to different brain areas, subjects, and electrode types not used for training. CLINICAL TRIAL REGISTRATION NUMBER: The clinical trial registration number for patients implanted with the Utah array is NCT01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation.

Association of BRCA1 185 del AG with early age onset of breast cancer patients in selected cohort from Pakistani population.

BACKGROUND & OBJECTIVE: Large spectrum of pathogenic BRCA mutations is known as a major cause of hereditary breast ovarian cancer in human all over the world. The objective of present study was to find out the association of mutations185-del-AG and 185Ins.A at BRCA1 exon-2 with age of onset and family history of gynecological cancer among the selected cohort of breast cancer patients in Pakistani population and to provide guidelines for treatment strategies. METHODS: For the present study 115 subjects were recruited from different hospitals of Punjab, Pakistan, during May, 2017 to February, 2018. The inclusion criteria were age ≥30, without any previous BRCA testing and willingness to participate in present study. Subjects were interviewed for various demographic factors. Out of 115 subjects, 46 were selected on the basis of findings of previous studies and approximately 3 ml of blood was collected in EDTA coated vials for analysis of BRCA1 exon-2. Column based DNA extraction was performed by using commercial kit and exon specific primers were used to amplify BRCA1 exon 2 and PCR products were sent for sequencing to Eurofins Genomics. Sequences were analyzed through the BLAST program at National Center for Biotechnology Information (NCBI) and Bio Edit software. Accession numbers were obtained on submission of sequences in GenBank. RESULTS: BRCA1-185-del AG mutation was found in one of the breast cancer patient who was 33 years of age at diagnosis. None of the samples revealed positive results for BRCA1-185 Ins. A. CONCLUSION: BRCA1-185 Del AG mutation has association with early age onset of breast cancer. The direct sequencing is very useful approach for BRCA analysis and exon specific selected cohort from Pakistani population.

Ecological risk assessment of pharmaceuticals in the receiving environment of pharmaceutical wastewater in Pakistan.

The pharmaceutical industry of Pakistan is growing with an annual growth rate of 10%. Besides this growth, this industry is not complying with environmental standards, and discharging its effluent into domestic wastewater network. Only limited information is available about the occurrence of pharmaceutical compounds (PCs) in the environmental matrices of Pakistan that has motivated us to aim at the occurrence and ecological risk assessment of 11 PCs of different therapeutic classes in the wastewater of pharmaceutical industry and in its receiving environmental matrices such as sludge, solid waste and soil samples near the pharmaceutical formulation units along Shiekhupura road, Lahore, Pakistan. Target PCs (paracetamol, naproxen, diclofenac, ibuprofen, amlodipine, rosuvastatin, ofloxacin, ciprofloxacin, moxifloxacin, sparfloxacin and gemifloxacin) were quantified using in-house developed HPLC-UV. Ibuprofen (1673µg/L, 6046µg/kg, 1229µg/kg and 610µg/kg), diclofenac (836µg/L, 4968µg/kg, 6632µg/kg and 257µg/kg) and naproxen (464µg/L, 7273µg/kg, 4819µg/kg and 199µg/kg) showed the highest concentrations among 11 target PCs in wastewater, sludge, solid waste and soil samples, respectively. Ecological risk assessment, in terms of risk quotient (RQ), was also carried out based on the maximum measured concentration of PCs in wastewater. The maximum RQ values obtained were with paracetamol (64 against daphnia), naproxen (177 against fish), diclofenac (12,600 against Oncorhynchus mykiss), ibuprofen (167,300 against Oryzias latipes), ofloxacin (81,000 against Pseudomonas putida) and ciprofloxacin (440 against Microcystis aeruginosa). These results show a high level of ecological risk due to the discharge of untreated wastewater from pharmaceutical units. This risk may further lead to food web contamination and drug resistance in pathogens. Thus, further studies are needed to detect the PCs in crops as well as the government should strictly enforce environmental legislation on these pharmaceutical units.

Occurrence and ecological risk assessment of fluoroquinolone antibiotics in hospital waste of Lahore, Pakistan.

In the present study, wastewater and sludge samples of two major hospitals of Lahore, Pakistan were analyzed by developing an HPLC-UV method for the possible occurrence of five frequently used fluoroquinolone antibiotics i.e. ofloxacin, ciprofloxacin, sparfloxacin, moxifloxacin and gemifloxacin. The highest detected concentration was for moxifloxacin in both wastewater (224 µg/L) and sludge samples (219 µg/kg. The highest concentration of ofloxacin, ciprofloxacin, sparfloxacin and gemifloxacin were found to be 66, 18, 58 and 0.2 µg/L respectively. Risk quotient (RQ) was also calculated based on maximum measured concentrations and the RQ values were very high particularly for ofloxacin and ciprofloxacin. The maximum RQ values for ofloxacin against Vibrio fisheri, Pseudomonas putida, fish, Daphnia, Green algae and Pseudokirchneriella subcapitata were 3300, 66,000, 124, 46, 3300 and 6000, respectively. In case of ciprofloxacin, RQ values were found to be 1750 and 3500 against green algae and Microcystis aeruginosa, respectively.

Worldwide patterns of ancestry, divergence, and admixture in domesticated cattle.

The domestication and development of cattle has considerably impacted human societies, but the histories of cattle breeds and populations have been poorly understood especially for African, Asian, and American breeds. Using genotypes from 43,043 autosomal single nucleotide polymorphism markers scored in 1,543 animals, we evaluate the population structure of 134 domesticated bovid breeds. Regardless of the analytical method or sample subset, the three major groups of Asian indicine, Eurasian taurine, and African taurine were consistently observed. Patterns of geographic dispersal resulting from co-migration with humans and exportation are recognizable in phylogenetic networks. All analytical methods reveal patterns of hybridization which occurred after divergence. Using 19 breeds, we map the cline of indicine introgression into Africa. We infer that African taurine possess a large portion of wild African auroch ancestry, causing their divergence from Eurasian taurine. We detect exportation patterns in Asia and identify a cline of Eurasian taurine/indicine hybridization in Asia. We also identify the influence of species other than Bos taurus taurus and B. t. indicus in the formation of Asian breeds. We detect the pronounced influence of Shorthorn cattle in the formation of European breeds. Iberian and Italian cattle possess introgression from African taurine. American Criollo cattle originate from Iberia, and not directly from Africa with African ancestry inherited via Iberian ancestors. Indicine introgression into American cattle occurred in the Americas, and not Europe. We argue that cattle migration, movement and trading followed by admixture have been important forces in shaping modern bovine genomic variation.

Enhanced electricity generation by using algae biomass and activated sludge in microbial fuel cell.

Recently, interest is growing to explore low-cost and sustainable means of energy production. In this study, we have exploited the potential of sustainable energy production from wastes. Activated sludge and algae biomass are used as substrates in microbial fuel cell (MFC) to produce electricity. Activated sludge is used at anode as inoculum and nutrient source. Various concentrations (1-5 g/L) of dry algae biomass are tested. Among tested concentrations, 5 g/L (5000 mg COD/L) produced the highest voltage of 0.89 V and power density of 1.78 W/m(2) under 1000 Ω electric resistance. Pre-treated algae biomass and activated sludge are also used at anode. They give low power output than without pre-treatment. Spent algae biomass is tested to replace whole (before oil extraction) algae biomass as a substrate, but it gives low power output. This work has proved the concept of using algae biomass in MFC for high energy output.

Generation of electricity from FeCl3 pretreatment of rice straw using a fuel cell system.

This study explored a new approach to the pretreatment of lignocellulosic biomass using FeCl3 combined with a fuel cell system to generate electricity. After pretreatment, ferric iron (Fe(3+)), a strong catalyst in the hydrolysis of carbohydrate, was found to be reduced to ferrous iron (Fe(2+)) by means of the oxidation of xylose and lignin. Ferrous iron, as a fuel, was employed to the anode part of a fuel cell, generating power of 1110 mW/m(2). During the fuel cell operation, ferrous iron was completely removed through oxidation to ferric iron and precipitated out. The optimal conditions for the operation of the fuel cell were found to be a pH of 7.0 and ferrous iron concentration of above 0.008 M. These results clearly show that a fuel cell system could be used not only to remove ferrous iron from liquid hydrolysate, but also to produce electricity.