Evaluation of various techniques for microalgal biomass quantification

Classification machine learning to detect de facto reuse and cyanobacteria at a drinking water intake

Harmful algal blooms (HABs) or higher levels of de facto water reuse (DFR) can increase the levels of certain contaminants at drinking water intakes. Therefore, the goal of this study was to use multi-class supervised machine learning (SML) classification with data collected from six online instruments measuring fourteen total water quality parameters to detect cyanobacteria (corresponding to approximately 950 cells/mL, 2900 cells/mL, and 8600 cells/mL) or DFR (0.5, 1 and 2 % of wastewater effluent) events in the raw water entering an intake. Among 56 screened models from the caret package in R, four (mda, LogitBoost, bagFDAGCV, and xgbTree) were selected for optimization. mda had the greatest testing set accuracy, 98.09 %, after optimization with 7 false alerts. Some of the most important water parameters for the different models were phycocyanin-like fluorescence, UVA254, and pH. SML could detect algae blending events (estimated <9000 cells/mL) due in part to the phycocyanin-like fluorescence sensor. UVA254 helped identify higher concentrations of DFR. These results show that multi-class SML classification could be used at drinking water intakes in conjunction with online instrumentation to detect and differentiate HABs and DFR events. This could be used to create alert systems for the water utilities at the intake, rather than the finished water, so any adjustment to the treatment process could be implemented.

Mechanistic characterization of dissolved inorganic phosphorus in water during the red tide

The eutrophication of water, such as excessive nitrogen and phosphorus, are closely associated with the outbreak of red tide. However, the response of dissolved inorganic phosphorus (DIP) to red tide remained unclear in water. In this study, three species of diatoms capable of causing red tides were cultured in simulated seawater with different concentrations of DIP. The changes of biomass, chlorophyll a concentration and the carbon stable isotope composition of microalgae, the DIP concentration and pH of the culture medium were compared among the experimental groups. In addition, correlation verification was used to test the correlation between the change of DIP concentration and other indicators. The results showed that in the experimental period, the DIP concentration of each experimental group decreased significantly first, and the concentration dropped to less than 40% of the initial level. After that, the pH of the medium, the biomass, chlorophyll a concentration and carbon stable isotope composition of the microalgae showed varying degrees of increase, and then stabilized or decreased. These also marked the outbreak of red tide. Moreover, the correlation test showed that there was a correlation between them and the change of DIP concentration. Therefore, by exploring the relationship between the change of DIP concentration in water and the occurrence of red tide, this study provides a possible direction for the current prediction of red tide, and provides a basis for further investigation of the occurrence mechanism of red tide.

A microfluidic impedance cytometry device for robust identification of H. pluvialis

H. pluvialis contains rich oleic acid and astaxanthin, which have important applications in the fields of biodiesel and biomedicine. Detection of live H. pluvialis is the prerequisite to obtaining oleic acid and astaxanthin. For this purpose, we successfully developed a reliable microfluidic impedance cytometry for the identification of live H. pluvialis. Firstly, we established a simulation model for detecting H. pluvialis based on their morphology and studied the effect of medium conductivity on the impedance of H. pluvialis at different frequencies. From the simulations, we determined that the optimal solution conductivity for the detection of H. pluvialis was 1500 μS cm-1 and studied the frequency responses of the impedance of H. pluvialis. Secondly, we fabricated the microchannels and stainless-steel detection electrodes and assembled them into microfluidic impedance cytometry. The frequency dependence of live and dead H. pluvialis was explored under different frequencies, and live and dead H. pluvialis were distinguished at a frequency of 1 MHz. The impedance of live H. pluvialis at the frequency of 1 MHz ranges from 33.73 to 52.23 Ω, while that of dead ones ranges from 13.05 to 19.59 Ω. Based on these findings, we accomplished the identification and counting of live H. pluvialis in the live and dead sample solutions. Furthermore, we accomplished the identification and counting of live H. pluvialis in the mixed samples containing Euglena and H. pluvialis. This approach possesses the promising capacity to serve as a robust tool in the identification of target microalgae, addressing a challenge in the fields of biodiesel and biomedicine.

Investigating quantitative approach for microalgal biomass using deep convolutional neural networks and image recognition

The effective monitoring of microalgae cultivation is crucial for optimizing their energy utilization efficiency. In this paper, a quantitative analysis method, using microalgae images based on two convolutional neural networks, EfficientNet (EFF) and residual network (RES), is proposed. Suspension samples prepared from two types of dried microalgae powders, Rhodophyta (RH) and Spirulina (SP), were used to mimic real microalgae cultivation settings. The method's prediction accuracy of the algae concentration ranges from 0.94 to 0.99. RH, with a distinctively pronounced red-green-blue value shift, achieves a higher prediction accuracy than SP. The prediction results of the two algorithms were significantly superior to those of a linear regression. Additionally, RES outperforms EFF in terms of its generalization ability and robustness, which is attributable to its distinct residual block architecture. The RES provides a viable approach for the image-based quantitative analysis.

Growth and productivity of Haematococcus pluvialis and Coelastrella saipanensis by photosystem modulation for understanding the heterotrophic nutritional strategy for bioremediation application

In this study, Haematococcus pluvialis and Coelastrella saipanensis were evaluated for heterotrophic nutrition potential in dairy waste medium by blocking the PSII using DCMU. The study was done by four sets of experiments. In the first set, in the different concentrations DCMU-treatments, 20μL showed pronounced effect in H. pluvialis and C. saipanensis as 89 % and 83% decrease in cells (>30 and > 250 cells/mL) compared to control (536 ± 12.35 × 104 and 1167 ± 15.35 × 104 cells/mL, respectively). Damage to the PS II by DCMU interrupted the growth, which in turn produced a significant drop in the number of cells. In the second round of experiment, growth of algae in various dairy waste concentrations suggest that dairy wastewater (DWW) provides enough nutrients to produce 35.71 % and 64.74 % more cells in H. pluvialis and C. saipanensis, respectively compared to the control. In the third set, high DCMU concentration was added to microalgae cultures in DWW to assess the heterotrophic nutrition potential. Growth in cell number 34.4 ± 19 and 617.46 ± 60.44 cells/mL was recorded in H. pluvialis and C. saipanensis when grown control medium whereas addition of DCMU reduced the cell number to 1.53 ± 0.75 and 55.13 ± 0.75 cells/mL on 15th day, respectively. This shows cells in cultures treated with DCMU reveal that algae can sustain their metabolic activity by utilizing the nutrients of dairy waste inhibiting photosystem. Fourth round of experiments found that microalgae could resume their growth and productivity by adapting to heterotrophic nutritional behaviour when DCMU given in mild dose at different time interval. This study conclude as C. saipanensis grows more readily by absorbing dairy waste nutrients than H. pluvialis. Therefore, C. saipanensis is an excellent choice for wastewater treatment through sustainable environmentally benign process after scale-up investigation. These results provide useful information to advance to molecular study for measuring microalgae's capability for bioremediation application.

Exploring the potential of Diplosphaera mucosa VSPA for the treatment of petroleum effluent with simultaneous lipid production

The discovery of unexplored, robust microalgal strains will assist in treating highly polluted industrial effluent, including petroleum effluent. In the current analysis, a newly isolated microalgal strain, Diplosphaera mucosa VSPA, was used to treat petroleum effluent in a lab-scale raceway bioreactor. Its treatment efficiency was compared with a well-known species, Chlorella pyrenoidosa. The D. mucosa VSPA strain proliferated in petroleum effluent at a high growth rate, with final biomass, and lipid concentrations reaching 6.93 g/L and 2.72 g/L, respectively. Treatment efficiency was calculated based on the final removal efficiency of ammonium nitrogen, phosphate phosphorus, and chemical oxygen demand, which was more than 90%. Control experiments suggested that the maximum removal of pollutants from petroleum effluent was due to microalgae growth. Some growth models, including the Gompertz, Logistic, Stannard, Richard, and Schnute, were used to simulate the experimental data, verifying the results. Good fitting of all models was obtained, with the R2 value reaching more than 0.90. The development of a suitable model can help in decreasing the efforts required for the scale-up of the process.

Label-free and noninvasive analysis of microorganism surface epistructures at the single-cell level

Cell surface properties of microorganisms provide abundant information for their physiological status and fate choice. However, current methods for analyzing cell surface properties require labeling or fixation, which can alter the cell activity. This study establishes a label-free, rapid, noninvasive, and quantitative analysis of cell surface properties, including the presence and the dimension of epistructure, down to the single-cell level and at the nanometer scale. Simultaneously, electrorotation provides dielectric properties of intracellular contents. With the combined information, the growth phase of microalgae cells can be identified. The measurement is based on electrorotation of single cells, and an electrorotation model accounting for the surface properties is developed to properly interpret experimental data. The epistructure length measured by electrorotation is validated by scanning electron microscopy. The measurement accuracy is satisfactory in particular in the case of microscale epistructures in the exponential phase and nanoscale epistructures in the stationary phase. However, the measurement accuracy for nanoscale epistructures on cells in the exponential phase is offset by the effect of a thick double layer. Lastly, a diversity in epistructure length distinguishes exponential phase from stationary phase.

Improvement of Lutein and Zeaxanthin Production in Mychonastes sp. 247 by Optimizing Light Intensity and Culture Salinity Conditions

In this study, we sought to improve lutein and zeaxanthin production in Mychonastes sp. 247 and investigated the effect of environmental factors on lutein and zeaxanthin productivity in Mychonastes sp. The basic medium selection and N:P ratio were adjusted to maximize cell growth in one-stage culture, and lutein and zeaxanthin production conditions were optimized using a central composite design for two-stage culture. The maximum lutein production was observed at a light intensity of 60 μE/m²/s and salinity of 0.49%, and the maximum zeaxanthin production was observed at a light intensity of 532 μE/m²/s and salinity of 0.78%. Lutein and zeaxanthin production in the optimized medium increased by up to 2 and 2.6 folds, respectively, compared to that in the basic medium. Based on these results, we concluded that the optimal conditions for lutein and zeaxanthin production are different and that optimization of light intensity and culture salinity conditions may help increase carotenoid production. This study presents a useful and potential strategy for optimizing microalgal culture conditions to improve the productivity of lutein and zeaxanthin, which has applications in the functional food field.

Robust control designs for microalgae cultivation in continuous photobioreactors

Microalgae are used to produce renewable biofuels and high-value components and in bioremediation and CO2 sequestration tasks. These increasing applications, in conjunction with a desirable constant large-scale productivity, motivate the development and application of practical controllers. This paper addresses the application of robust control schemes for microalgae cultivation in continuous photobioreactors. Due to the model uncertainties and external perturbations, robust control designs are required to guarantee the desired microalgae productivity. Furthermore, simple controller designs are desirable for practical implementation purposes. Therefore, two robust control designs are applied and evaluated in this paper for two relevant case studies of microalgae cultivation in photobioreactors. The first control design is based on an enhanced simple-input output model with uncertain estimation. The second control design is the robust nonlinear model predictive control considering different uncertain scenarios. Numerical simulations of two case studies aimed at lipid production and CO2 capture under different conditions are presented to evaluate the robust closed-loop performance.

Smart microalgae farming with internet-of-things for sustainable agriculture

Agriculture farms such as crop, aquaculture and livestock have begun the implementation of Internet of Things (IoT) and artificial intelligence (AI) technology in improving their productivity and product quality. However, microalgae farming which requires precise monitoring, controlling and predicting the growth of microalgae biomass has yet to incorporate with IoT and AI technology, as it is still in its infancy phase. Particularly, the cultivation stage of microalgae involves many essential parameters (i.e. biomass concentration, pH, light intensity, temperature and tank level) which require precise monitoring as these parameters are important to ensure an effective biomass productivity in the microalgae farming. Besides, the conventional practices in the current process equipment are still powered by electricity, thus further development by integrating IoT into these processes can ease the production process. Further to that, many researchers has studied the machine learning approach for the identification and classification of microalgae. However, there are still limited studies reported on applying machine learning for the application of microalgae industry such as optimising microalgae cultivation for higher biomass productivity. Therefore, the implementation of IoT and AI in microalgae farming can contribute to the development of the global microalgae industry. The purpose of this current review paper focuses on the overview microalgae biomass production process along with the implementation of IoT toward the future of smart farming. To bridge the gap between the conventional and microalgae smart farming, this paper also highlights the insights on the implementation phases of microalgae smart farming starting from the infant stage that involves the installation and programming of IoT hardware. Then, it is followed by the application of machine learning to predict and auto-optimise the microalgae smart farming process. Furthermore, the process setup and detailed overview of microalgae farming with the integration of IoT have been discussed critically. This review paper would provide a new vision of microalgae farming for microalgae researchers and bio-processing industries into the digitalisation industrial era.

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

Microalgae are promising sources of fuels and other chemicals. To operate microalgal cultivations efficiently, process control based on monitoring of process variables is needed. On-line sensing has important advantages over off-line and other analytical and sensing methods in minimizing the measurement delay. Consequently, on-line, in-situ sensors are preferred. In this respect, optical sensors occupy a central position since they are versatile and readily implemented in an on-line format. In biotechnological processes, measurements are performed in three phases (gaseous, liquid and solid (biomass)), and monitored process variables can be classified as physical, chemical and biological. On-line sensing technologies that rely on standard industrial sensors employed in chemical processes are already well-established for monitoring the physical and chemical environment of an algal cultivation. In contrast, on-line sensors for the process variables of the biological phase, whether biomass, intracellular or extracellular products, or the physiological state of living cells, are at an earlier developmental stage and are the focus of this review. On-line monitoring of biological process variables is much more difficult and sometimes impossible and must rely on indirect measurement and extensive data processing. In contrast to other recent reviews, this review concentrates on current methods and technologies for monitoring of biological parameters in microalgal cultivations that are suitable for the on-line and in-situ implementation. These parameters include cell concentration, chlorophyll content, irradiance, and lipid and pigment concentration and are measured using NMR, IR spectrophotometry, dielectric scattering, and multispectral methods. An important part of the review is the computer-aided monitoring of microalgal cultivations in the form of software sensors, the use of multi-parameter measurements in mathematical process models, fuzzy logic and artificial neural networks. In the future, software sensors will play an increasing role in the real-time estimation of biological variables because of their flexibility and extendibility.

How does the Internet of Things (IoT) help in microalgae biorefinery?

Microalgae biorefinery is a platform for the conversion of microalgal biomass into a variety of value-added products, such as biofuels, bio-based chemicals, biomaterials, and bioactive substances. Commercialization and industrialization of microalgae biorefinery heavily rely on the capability and efficiency of large-scale cultivation of microalgae. Thus, there is an urgent need for novel technologies that can be used to monitor, automatically control, and precisely predict microalgae production. In light of this, innovative applications of the Internet of things (IoT) technologies in microalgae biorefinery have attracted tremendous research efforts. IoT has potential applications in a microalgae biorefinery for the automatic control of microalgae cultivation, monitoring and manipulation of microalgal cultivation parameters, optimization of microalgae productivity, identification of toxic algae species, screening of target microalgae species, classification of microalgae species, and viability detection of microalgal cells. In this critical review, cutting-edge IoT technologies that could be adopted to microalgae biorefinery in the upstream and downstream processing are described comprehensively. The current advances of the integration of IoT with microalgae biorefinery are presented. What this review discussed includes automation, sensors, lab-on-chip, and machine learning, which are the main constituent elements and advanced technologies of IoT. Specifically, future research directions are discussed with special emphasis on the development of sensors, the application of microfluidic technology, robotized microalgae, high-throughput platforms, deep learning, and other innovative techniques. This review could contribute greatly to the novelty and relevance in the field of IoT-based microalgae biorefinery to develop smarter, safer, cleaner, greener, and economically efficient techniques for exhaustive energy recovery during the biorefinery process.

Comprehensive review of water management and wastewater treatment in food processing industries in the framework of water-food-environment nexus

Food processing is among the greatest water-consuming industries with a significant role in the implementation of sustainable development goals. Water-consuming industries such as food processing have become a threat to limited freshwater resources, and numerous attempts are being carried out in order to develop and apply novel approaches for water management in these industries. Studies have shown the positive impact of the new methods of process integration (e.g., water pinch, mathematical optimization, etc.) in maximizing water reuse and recycle. Applying these methods in food processing industries not only significantly supported water consumption minimization but also contributed to environmental protection by reducing wastewater generation. The methods can also increase the productivity of these industries and direct them to sustainable production. This interconnection led to a new subcategory in nexus studies known as water-food-environment nexus. The nexus assures sustainable food production with minimum freshwater consumption and minimizes the environmental destructions caused by untreated wastewater discharge. The aim of this study was to provide a thorough review of water-food-environment nexus application in food processing industries and explore the nexus from different aspects. The current study explored the process of food industries in different sectors regarding water consumption and wastewater generation, both qualitatively and quantitatively. The most recent wastewater treatment methods carried out in different food processing sectors were also reviewed. This review provided a comprehensive literature for choosing the optimum scenario of water and wastewater management in food processing industries.

Accurate method for rapid biomass quantification based on specific absorbance of microalgae species with biofuel importance

The development of microalgae culture technology has been an integral part to produce biomass feedstock to biofuel production. Due to this, numerous attempts have been made to improve some operational parameters of microalgae production. Despite this, specialized research in cell growth monitoring, considered as a fundamental parameter to achieve profitable applications of microalgae for biofuels production, presents some opportunity areas mainly related to the development of specific and accurate methodologies for growth monitoring. In this work, predictive models were developed through statistical tools that correlate a specific micro-algal absorbance with cell density measured by cell count (cells∙per ml), for three species of interest for biofuels production. The results allow the precise prediction of cell density through a logistic model based on spectrophotometry, valid for all the kinetics analysed. The adjusted determination coefficients ( r adj 2 ) for the developed models were 0·993, 0·995 and 0·994 for Dunaliella tertiolecta, Nannochloropsis oculata and Chaetoceros muelleri respectively. The results showed that the equations obtained here can be used with an extremely low error (≤2%) for all the cell growth ranges analysed, with low operational cost and high potential of automation. Finally, a user-friendly software was designed to give practical use to the developed predictive models.

Interactions of microalgae-bacteria consortia for nutrient removal from wastewater: A review

Nitrogen and phosphorus pollution can cause eutrophication, resulting in ecosystem disruption. Wastewater treatment systems employing microalgae-bacteria consortia have the potential to enhance the nutrient removal efficiency from wastewater through mutual interaction and synergetic effects. The knowledge and control of the mechanisms involved in microalgae-bacteria interaction could improve the system's ability to transform and recover nutrients. In this review, a critical evaluation of recent literature was carried out to synthesize knowledge related to mechanisms of interaction between microalgae and bacteria consortia for nutrient removal from wastewater. It is now established that microalgae can produce oxygen through photosynthesis for bacteria and, in turn, bacteria supply the required metabolites and inorganic carbon source for algae growth. Here we highlight how the interaction between microalgae and bacteria is highly dependent on the nitrogen species in the wastewater. When the nitrogen source is ammonium, the generated oxygen by microalgae has a positive influence on nitrifying bacteria. When the nitrogen source is nitrate, the oxygen can have an inhibitory effect on denitrifying bacteria. However, some strains of microalgae have the capability to supply hydrogen gas for hydrogenotrophic denitrifiers as an energy source. Recent literature on biogranulation of microalgae and bacteria and its application for nutrient removal and biomass recovery is also discussed as a promising approach. Significant research challenges remain for the integration of microalgae-bacteria consortia into wastewater treatment processes including microbial community control and process stability over long time horizons.

Development of Digital Image Processing as an Innovative Method for Activated Sludge Biomass Quantification

Activated sludge process is the most common method for biological treatment of industrial and municipal wastewater. One of the most important parameters in performance of activated sludge systems is quantitative monitoring of biomass to keep the cell concentration in an optimum range. In this study, a novel method for activated sludge quantification based on image processing and RGB analysis is proposed. According to the results, the intensity of blue color in the macroscopic image of activated sludge culture can be a very accurate index for cell concentration measurement and R² coefficient, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) which are 0.990, 2.000, 0.323, and 13.848, respectively, prove this claim. Besides, in order to avoid the difficulties of working in the three-parameter space of RGB, converting to grayscale space has been applied which can estimate cell concentration with R ² = 0.99. Ultimately, an exponential correlation between RGB values and cell concentrations in lower amounts of biomass has been proposed based on Beer-Lambert law which can estimate activated sludge biomass concentration with R ² = 0.97 based on B index.

Classification, identification, and growth stage estimation of microalgae based on transmission hyperspectral microscopic imaging and machine learning

A transmission hyperspectral microscopic imager (THMI) that utilizes machine learning algorithms for hyperspectral detection of microalgae is presented. The THMI system has excellent performance with spatial and spectral resolutions of 4 µm and 3 nm, respectively. We performed hyperspectral imaging (HSI) of three species of microalgae to verify their absorption characteristics. Transmission spectra were analyzed using principal component analysis (PCA) and peak ratio algorithms for dimensionality reduction and feature extraction, and a support vector machine (SVM) model was used for classification. The average accuracy, sensitivity and specificity to distinguish one species from the other two species were found to be 94.4%, 94.4% and 97.2%, respectively. A species identification experiment for a group of mixed microalgae in solution demonstrates the usability of the classification method. Using a random forest (RF) model, the growth stage in a phaeocystis growth cycle cultivated under laboratory conditions was predicted with an accuracy of 98.1%, indicating the feasibility to evaluate the growth state of microalgae through their transmission spectra. Experimental results show that the THMI system has the capability for classification, identification and growth stage estimation of microalgae, with strong potential for in-situ marine environmental monitoring and early warning detection applications.

Cultivation of Mixed Microalgae Using Municipal Wastewater: Biomass Productivity, Nutrient Removal, and Biochemical Content

BACKGROUND

Microalgal biotechnology has gained much attention previously. Monoculture algae cultivation has been carried out extensively in the last decades. However, although the mixed microalgae cultivation has some advantageous over pure cultures, there is still a lack of knowledge about the performance of mixed cultures.

OBJECTIVE

In this study, it has been tried to investigate all growth aspects of marine and freshwater microalgal species in a mixed culture and their biological effects on biomass growth and composition based on wastewater nutrient consumption.

MATERIAL AND METHODS

Three algal species of Chlorella vulgaris, Scenedesmus obliquus, and Nannochloropsis sp. were cultivated in saline wastewater individually, then the effects of mixing the three strains on biomass productivity, nutrient removal efficiency, chlorophyll, carotenoid, and lipid content were investigated.

RESULTS

The obtained results revealed that the mixed culture of three strains showed the highest biomass productivity of 191 mg. L^-1.d^-1. Also, while there were no significant differences between the performance of mono and mixed culture of algal species in the removal efficiency of wastewater nutrients, the three-strain microalgal mixed culture showed the highest values of 3.5 mg.L^-1.d^-1 and 5.75 mg.L^-1.d^-1 in the removal rate of phosphate and nitrate, respectively. In terms of total chlorophyll and carotenoid per produced biomass, however, the mixed culture of three species showed the lowest values of 4.08 and 0.6 mg. g biomass^-1, respectively.

CONCLUSIONS

The finding proves the potential of attractive and economically feasible mixed microalgae cultivation for high percentage nutrient removal and microalgal biomass production.

Microalgae with artificial intelligence: A digitalized perspective on genetics, systems and products

With recent advances in novel gene-editing tools such as RNAi, ZFNs, TALENs, and CRISPR-Cas9, the possibility of altering microalgae toward designed properties for various application is becoming a reality. Alteration of microalgae genomes can modify metabolic pathways to give elevated yields in lipids, biomass, and other components. The potential of such genetically optimized microalgae can give a "domino effect" in further providing optimization leverages down the supply chain, in aspects such as cultivation, processing, system design, process integration, and revolutionary products. However, the current level of understanding the functional information of various microalgae gene sequences is still primitive and insufficient as microalgae genome sequences are long and complex. From this perspective, this work proposes to link up this knowledge gap between microalgae genetic information and optimized bioproducts using Artificial Intelligence (AI). With the recent acceleration of AI research, large and complex data from microalgae research can be properly analyzed by combining the cutting-edge of both fields. In this work, the most suitable class of AI algorithms (such as active learning, semi-supervised learning, and meta-learning) are discussed for different cases of microalgae applications. This work concisely reviews the current state of the research milestones and highlight some of the state-of-art that has been carried out, providing insightful future pathways. The utilization of AI algorithms in microalgae cultivation, system optimization, and other aspects of the supply chain is also discussed. This work opens the pathway to a digitalized future for microalgae research and applications.

Autotrophic granulation of hydrogen consumer denitrifiers and microalgae for nitrate removal from drinking water resources at different hydraulic retention times

To avoid hydrogen injection and to enhance the settleability of microbial biomass in biological treatment of nitrate-contaminated drinking water resources, a new method based on granulation of a mixture of hydrogen consumer denitrifiers (HCD) and microalgae is introduced. Decreasing hydraulic retention time (HRT) was applied as the selection pressure in an up-flow photobioreactor to increase the speed of granulation and nitrate removal under autotrophic condition during a 50-day operation. Formation of granules occurred at three phases including granule nucleation, growth of granule, and mature granule, with decreasing the values of ζ-potential from -19 mV to -4 mV. Enhancement of microbial attachment within granule formation could reduce the presence of total suspended solids in the effluent. Developed granules of HCD and microalgae could settle down with velocity of 40 ± 0.6 m/h when reaching the average size of 1.2 mm at day 40. Complete NO₃^--N removal from drinking water was achieved from the initial stage of granulation until the end of operation at all HRTs of 3 days-5 h. The clear treated water was obtained at the growth phase when the chemical oxygen demand and phosphate were undetectable. Therefore, the application of HCD-microalgae granule is a promising way for nitrate removal from water.

Characterization of physiological states of the suspended marine microalgae using polarized light scattering

Physiological states of marine microalgal cells can influence photosynthesis efficiency, which affects approximately half of global carbon fixation. The detection of the algae physiological profiles is important for marine ecology and economy. In this paper, we propose a polarized light-scattering method to detect sensitive changes in the physiological states of the suspended marine microalgal cells. Our experimental setup is designed to measure the scattered polarization parameters of the cells suspended individually in the seawater. Two species of microalgal cells cultured in the laboratory were measured for several days. Experimental results showed that both species display distinctive changes in their polarized photon scattering features corresponding to changes in their physiological states. The changes are far more prominent than those displayed in unpolarized light scattering. Microscopy observations, simulations for microspheres of different diameters and refractive indices, or different shapes, indicated that the polarization features of the scattered photons are sensitive to the submicrometer microstructures of the cells. This study demonstrates the potential of the polarized light-scattering technique to characterize the physiological states of suspended marine microalgae.

Differential proteomic analyses of green microalga Ettlia sp. at various dehydration levels

Water deprivation could be a lethal stress for aquatic and aero-terrestrial organisms. Ettlia sp. is a unicellular photosynthetic freshwater microalga. In the present study, proteomic alterations and physiological characteristics of Ettlia sp. were analyzed to comprehend the molecular changes in dehydrated conditions. Varying levels of dehydration were achieved by incubating drained Ettlia sp. in different relative humidity environments for 24  hours. Using a comparative proteomic analysis, 52 differentially expressed protein spots were identified that could be divided into eight functional groups. The PCA analysis of normalized protein expression values demonstrated a clear segregation of protein expression profiles among different dehydration levels. Identified proteins could be grouped into four clusters based on their expression profiles. Proteins relating to photosynthesis comprised the largest group with 25% of the identified proteins that were decreased in dehydrated samples and belonged to cluster I. The photosynthetic activities were measured with rehydrated Ettlia sp. These results revealed that photosynthesis remained inhibited over extended time in response to dehydration. The expressions of reactive oxygen species (ROS) scavenger proteins increased in strong dehydration and were assigned to cluster III. Carbon metabolism proteins were suppressed, which might limit energy consumption, whereas glycolysis was activated at mild dehydration. The accumulation of desiccation-associated late embryogenesis proteins might inhibit the aggregation of housekeeping proteins. DNA protective proteins were expressed higher in the dehydrated state, which might reduce DNA damage, and membrane-stabilizing proteins increased in abundance in desiccation. These findings provide an understanding of Ettlia's adaptation and survival capabilities in a dehydrated state.

Cost-effective batch production process of scFv antibody in Escherichia coli

BACKGROUND

Cost-effective production of antibody (Ab) fragments is of great interests of many pharmaceutical industries, in large part due to their high usages in research, diagnosis and therapy. Thus, the production of Abs necessitates accomplishment of the optimal strategies.

OBJECTIVE

In this study, based on the induction start time using arabinose, we implemented a novel strategy for the cost-effective production of single chain variable fragment (scFv) in Escherichia coli (E. coli).

METHODS

Complex and minimum media were used to investigate the batch fermentation in 50 mL batch tubes to find the optimum conditions for the production of a scFv in the Escherichia coli HB2151.

RESULTS

Arabinose was used as an appropriate economical alternative of isopropyl β-D-1-thiogalactopyranoside (IPTG) for the production of scFv antibody. The optimum concentration of arabinose as an inducer was 0.1% (w/w), while below this point the scFv production yield (YP/X) decreased significantly. The start time of the induction of E. coli HB2151 cells was adjusted to the stationary phase of the growth, and the results showed higher specific scFv production yields up to 0.9 mg scFv/g biomass in the minimum media. The optimum induction duration times for the complex and minimum media were about 12 and 24 hours, respectively.

CONCLUSIONS

We propose this method to possibly be used for the large-scale production of recombinant proteins/peptides such as scFv and Fab antibodies.

Optimal strategies for bioremediation of nitrate-contaminated groundwater and microalgae biomass production

Optimizing the mono-cultivation and mixed cultivation of Chlamydomonas reinhardtii, Chlorella vulgaris, and an Ettlia sp. was evaluated for treating nitrate-contaminated groundwater and biomass production. Ettlia sp. showed the highest nutrient assimilation and growth rate among the three microalgae during bioremediation. Light-dark cycle was the effective condition for nutrient removal and COD mitigation by microalgae. Mixed microalgae with a larger presence of the Ettlia sp. exhibited the highest biomass productivity, nitrate-nitrogen, and phosphate-phosphorus removal rates of 0.21 g/L/d, 16.6, and 3.06 mg/L/d, respectively. An N:P mass ratio of 5 was necessary to increase the mixed-microalgal performance. The settling efficiency of the mixed microalgae increased up to 0.55 when using pH modulation during 30 min. Therefore, applying an Ettlia sp.-dominant consortium was the optimum strategy for the bioremediation of nitrate-contaminated groundwater in 3 days.

Phosphorus optimization for simultaneous nitrate-contaminated groundwater treatment and algae biomass production using Ettlia sp

The effects of phosphorus concentration on the cell growth, nutrient assimilation, photosynthetic parameters, and biomass recovery of Ettlia sp. were evaluated with batch experiments using groundwater, 50mg/L of N-NO₃^-, and different concentrations of P-PO₄^3-: 0.5, 2.5, 5, and 10mg/L. The maximum biomass productivity and phosphorus removal rate were 0.2g/L/d and 5.95mg/L/d, respectively, with the highest phosphorus concentration of 10mg/L. However, a phosphorus concentration of 5mg/L (N:P=10) was sufficient to ensure an effective nitrogen removal rate of 11mg/L/d, maximum growth rate of 0.88/d, and biomass recovery of 0.72. The appropriate hydraulic retention time was considered as 4days on a large scale to meet the effluent limitation demands of water. While nitrogen depletion had a significant effect on the photosynthetic parameters and ratio of chlorophyll a to dry cell weight during the stationary phase, the effect of phosphorus was negligible during the cultivation.

Light intensity as major factor to maximize biomass and lipid productivity of Ettlia sp. in CO2-controlled photoautotrophic chemostat

The optimal culture conditions are critical factors for high microalgal biomass and lipid productivity. To optimize the photoautotrophic culture conditions, combination of the pH (regulated by CO₂ supply), dilution rate, and light intensity was systematically investigated for Ettlia sp. YC001 cultivation in a chemostat during 143days. The biomass productivity increased with the increase in dilution rate and light intensity, but decreased with increasing pH. The average lipid content was 19.8% and statistically non-variable among the tested conditions. The highest biomass and lipid productivities were 1.48gL^-1d^-1 and 291.4mgL^-1d^-1 with a pH of 6.5, dilution rate of 0.78d^-1, and light intensity of 1500μmolphotonsm^-2s^-1. With a sufficient supply of CO₂ and nutrients, the light intensity was the main determinant of the photosynthetic rate. Therefore, the surface-to-volume ratio of a photobioreactor should enable efficient light distribution to enhance microalgal growth.