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Marine underwater imaging facilitates non-destructive sampling of species at frequencies, durations, and accuracies that are unattainable by conventional sampling methods. These systems necessitate complex automated processes to identify organisms efficiently, however, current frameworks struggle to disentangle ecological foreground components from their dispensable background content. Underwater image processing relies on common architecture: namely image binarization for segmenting potential targets, prior to information extraction and classification by deep learning models. While intuitive, this infrastructure underperforms as it has difficulty in handling: high concentrations of biotic and abiotic particles, rapid changes in dominant taxa, and target sizes that vary by several orders of magnitude. To overcome these issues, a new framework is presented that begins with a scene classifier to capture large within-image variation, such as disparities in particle concentration and dominant taxa. Following scene classification, scene-specific regional convolutional neural network (Mask R-CNN) models were trained to separate target objects into different taxonomic groups. The procedure allows information to be extracted from different image types, while minimizing potential bias for commonly occurring features. Usingin situcoastal PlanktonScope images, we compared the scene-specific models to the Mask R-CNN model including all scene categories without scene classification, defined as the full model, and found that the scene-specific approach outperformed the full model with >20% accuracy in noisy images. The full model missed up to 78% of the dominant taxonomic groups, such asLyngbya, Noctiluca, andPhaeocystiscolonies. This performance improvement is due to the scene classifier, which reduces the variation among images and allows an improved match between the observed taxonomic groups and the taxonomic groups in pre-trained models. We further tested the framework on images from a benthic video camera and an imaging sonar system. Results demonstrate that the procedure is applicable to different types of underwater images and achieves significantly more accurate results than the full model. Given that the unified framework is neither instrument nor ecosystem-specific, the proposed model facilitates deployment throughout the marine biome.

Haematococcus pluvialis (H. pluvialis), the richest bioresource for natural astaxanthin, encounters a challenge of achieving high growth rate when it comes to mass biomass production. Base on substrate consumption model and Redfield ratio, rapid algae growth benefits from a proper carbon supply. However, the conventional cultivation schemes with limited CO2 supply and inefficient carbon mass transfer may restrict the carbon capture and growing ability of H. pluvialis. We hypothesize that optimal H. pluvialis growth improvement may be achieved by efficient CO2 supply. In this study, we first identified the carbon consumption of H. pluvialis during exponential growth. Then, a novel microbubble-driven photobioreactor (MDPBR) was designed to satisfy the carbon demand. The novel microbubble photobioreactor improve the CO 2 supply by reducing bubble size, then the CO2 mass transfer significantly elevated. With only 20% of gas flow rate, higher cell growth rate (0.49 d-1) has been achieved in MDPBR.

Given the ecological significance of microorganisms in algal blooming events, it is critical to understand the mechanisms regarding their distribution under different conditions. We tested the hypothesis that microbial community succession is strongly associated with algal bloom stages, and that the assembly mechanisms are cocontrolled by deterministic and stochastic processes. Community structures and underlying ecological processes of microbial populations (attached and free-living bacteria) at three algal bloom stages (pre-, during, and postbloom) over a complete dinoflagellate Scrippsiella trochoidea bloom were investigated. Both attached and free-living taxa had a strong response to the bloom event, and the latter was more sensitive than the former. The contribution of environmental parameters to microbial variability was 40.2%. Interaction analysis showed that complex positive or negative correlation networks exist in phycosphere microbes. These relationships were the potential drivers of mutualist and competitive interactions that impacted bacterial succession. Null model analysis showed that the attached bacterial community primarily exhibited deterministic processes at pre- and during-bloom stages, while dispersal-related processes contributed to a greater extent at the postbloom stage. In the free-living bacterial community, homogeneous selection and dispersal limitation dominated in the initial phase, which gave way to more deterministic processes at the two later stages. Relative contribution analyses further demonstrated that the community turnover of attached bacteria was mainly driven by environmental selection, while stochastic factors had partial effects on the assembly of free-living bacteria. Taken together, these data demonstrated that a robust link exists between bacterioplankton community structure and bloom progression, and phycosphere microbial succession trajectories are cogoverned by both deterministic and random processes. IMPORTANCE Disentangling the mechanisms shaping bacterioplankton communities during a marine ecological event is a core concern for ecologists. Harmful algal bloom (HAB) is a typical ecological disaster, and its formation is significantly influenced by alga-bacterium interactions. Microbial community shifts during the HAB process are relatively well known. However, the assembly processes of microbial communities in an HAB are not fully understood, especially the relative influences of deterministic and stochastic processes. We therefore analyzed the relative contributions of deterministic and stochastic processes during an HAB event. Both free-living and attached bacterial groups had a dramatic response to the HAB, and the relative importance of determinism versus stochasticity varied between the two bacterial groups at various bloom stages. Environmental factors and biotic interactions were the main drivers impacting the microbial shift process. Our results strengthen the understanding of the ecological mechanisms controlling microbial community patterns during the HAB process.

Summary The mitigation of biofouling has received significant research attention, with particular focus on non‐toxic and sustainable strategies. Here, we investigated quorum sensing inhibitor (QSI) bacteria as a means of controlling biofouling in a laboratory‐scale system. Approximately, 200 strains were isolated from coral (Pocillopora damicornis) and screened for their ability to inhibit quorum sensing (QS). Approximately, 15% of the isolates exhibited QSI activity, and a typical coral symbiotic bacterium, H12‐Vibrio alginolyticus, was selected in order for us to investigate quorum sensing inhibitory activity further. Confocal microscopy revealed that V. alginolyticus extract inhibited biofilm formation from Pseudomonas aeruginosa PAO1. In addition, the secondary metabolites of V. alginolyticus inhibited PAO1 virulence phenotypes by downregulating motility ability, elastase activity and rhamnolipid production. NMR and MS spectrometry suggested that the potential bioactive compound involved was rhodamine isothiocyanate. Quantitative real‐time PCR indicated that the bacterial extract induced a significant downregulation of QS regulatory genes (lasB, lasI, lasR, rhlI, rhlR) and virulence‐related genes (pqsA, pqsR). The possible mechanism underlying the action of rhodamine isothiocyanate analogue involves the disruption of the las and/or rhl system of PAO1. Our results highlight coral microbes as a bioresource pool for developing QS inhibitors and identifying novel antifouling agents.

Wireless sensor networks (WSNs) havebeen employed as an ideal solution in many applications fordata gathering in harsh environment. Energy consumptionis a key issue in wireless sensor networks since nodes areoften battery operated. Medium access control (MAC) protocol plays an important role in energy efficiency in wireless sensor networks because nodes’ access to the sharedmedium is coordinated by the MAC layer. An energy efficient MAC protocol is designed for data gathering inlinear wireless sensor networks. In order to enhance theperformance, when a source node transmits data to thesink, proper relay nodes are selected for forwarding dataaccording to the energy consumption factor and residualenergy balance factor. Some simulation experiments areconducted and the results show that, the proposed protocol provides better energy efficiency and long lifetime thanthe existing DMAC protocol.

In this study, Chlorella vulgaris (C. vulgaris) were cultured in cell culture flask supplied with intermittent CO2 enriched gas. The impact of CO2 concentration (from 1% to 20% v/v) on the growth of C. vulgaris cultured in domestic wastewater was exploited in various perspectives which include biomass, specific growth rate, culture pH, carbon consumption, and the removal of nitrogen and phosphorus compounds. The results showed that the maximum microalgal biomass concentration, 1.12 g L−1, was achieved with 10% CO2 as a feed gas. At 20% CO2 the growth of C. vulgaris suffered from inhibition during initial 1.5 d, but acclimated to low pH (6.3 in average) with relatively higher specific growth rate (0.3–0.5 d−1) during subsequent culture period. After the rapid consumption of ammonium in the wastewater, an obvious decline in the nitrate concentration was observed, indicating that C. vulgaris prefer ammonium as a primary nitrogen source. The total nitrogen and phosphorus decreased from 44.0 mg L−1 to 2.1–5.4 mg L−1 and from 5.2 mg L−1 to 0–0.6 mg L−1 within 6.5 d under the aeration of 1–20% CO2, respectively, but no significant difference in consumed nitrogen versus phosphorus ratio was observed among different CO2 concentration. The kinetics of nutrients removal were also determined through the application of pseudo first order kinetic model. 5–10% CO2 aeration was optimal for the growth of C. vulgaris in the domestic wastewater, based on the coupling of carbon consumption, microalgal biomass, the nutrients removal and kinetics constants.

Background: Harvesting algal biomass is an important unit operation in the production of biofuel from algae. However, many of the known techniques available for harvesting and dewatering microalgal biomass are energy intensive and in some cases intrusive and inefficient. Here we show that microflotation mediated by fluidic oscillation is an approach that differs from dissolved air flotation by the nonintrusive laminar flow approach, as well as low energy consumptions, and is also different from dispersed air flotation by the method of bubble generation. Results and discussion: Using three metallic coagulant types, recovery efficiencies of 99.2, 98.1 and 95.2% were obtained for ferric chloride, ferric sulfate and aluminum sulfate, respectively. The benchmarks in the literature for dissolved air flotation are 40–98%. Conclusion: Biofuel production from microalgae can be facilitated by the improvement of a key unit operation such as harvesting and dewatering. The study outcome strongly recommends the techniq...

In this study, the effect of microfluidic microbubbles on overall gas-liquid mass transfer (CO2 dissolution and O2 removal) was investigated under five different flow rates. The effect of different liquid substrate on CO2 mass transfer properties was also tested. The results showed that the KLa can be enhanced by either increasing the dosing flowrate or reducing the bubble size; however, increasing the flow rate to achieve a higher KLa would ultimately lower the CO2 capture efficiency. In order to achieve both higher CO2 mass transfer rate and capture efficiency, reducing bubble size (e.g. using microbubbles) has been proved more promising than increasing flow rate. Microbubble dosing with 5% CO2 gas showed improved KLa by 30% - 100% across different flow rates, compared to fine-bubble dosing. In the real algal culture medium, there appears to be two distinct stages in terms of KLa, divided by the pH of 8.4.

The efficiency of a novel microalgal culture system (an airlift loop bioreactor [ALB] engaged with a fluidic oscillator to produce microbubbles) is compared with both a conventional ALB (producing fine bubbles without the fluidic oscillator) and non-aerated flask culture. The impact of CO2 mass transfer on Dunaliella salina growth is assessed, through varying the gas (5% CO2, 95% N2) dosing flow rate. The results showed that approximately 6 - 8 times higher chlorophyll content was achieved in the aerated ALB cultures than in the non-aerated flasks, and there was a 20% - 40% increase in specific growth rate of D. salina in the novel ALB with microbubbles when compared with the conventional ALB cultures. The increase in chlorophyll content was found to be proportional to the total amount of CO2 mass transfer. For the same dosing time and flow rate, higher CO2 mass transfer rate (microbubble dosing) resulted in a greater growth rate.

Localization is one of the most important technologies in wireless sensor networks. A lightweight distributed node localization scheme is proposed by considering the limited computational capacity of WSNs. The proposed scheme introduces the virtual force model to determine the location by incremental refinement. Aiming at solving the drifting problem and malicious anchor problem, a location verification algorithm based on the virtual force mode is presented. In addition, an anchor promotion algorithm using the localization reliability model is proposed to re-locate the drifted nodes. Extended simulation experiments indicate that the localization algorithm has relatively high precision and the location verification algorithm has relatively high accuracy. The communication overhead of these algorithms is relative low, and the whole set of reliable localization methods is practical as well as comprehensive.

A periodic CO2 dosing strategy for D. salina 19/30 batch culture is proposed. A model of periodic CO2 dosing including dosing time calculation, dosing interval estimation and final chlorophyll yield prediction was established. In experiments, 5% CO2/95% N2 gas was periodically dosed into D. salina culture. Two different gas dosing flow rates were tested. The corresponding dosing time for each flow rate was estimated via the model (10 min·d−1 for 0.7 L·min−1 and 36 min·d−1 for 0.3 L·min−1). Daily pH measurements showed that the pH of these cultures dosed periodically was always kept between 7.5 and 9.5, which highlights that periodic gas supply can maintain a suitable range of pH for microalgal growth without expensive buffers. Notably the culture dosed for set daily intervals was seen to have similar growth to the culture supplied constantly, but with much higher CO2 capture efficiency (11%–18%) compared to continuous dosing (0.25%). It shows great potential for using periodic gas supply to reduce cost, wasted gas and energy use.

This study was conducted to test the feasibility of growing microalgae on steel plant exhaust gas, generated from the combustion of offgases from steel processing, which has a high CO 2 content. Two field trials of batch algal biomass growth, mediated by microbubble transfer processes in an airlift loop bioreactor showed only steady growth of biomass with 100% survival rate. The gas analysis of CO 2 uptake in the 2200 L bioreactor showed a specific uptake rate of 0.1 g/L/h, an average 14% of the CO 2 available in the exhaust gas with a 23% composition of CO 2 . This uptake led to a steady production of chlorophyll and total lipid constituency in the bioreactor, and an accelerating exponential growth rate of biomass, with a top doubling time of 1.8 days. The gas analysis also showed anti-correlation of CO 2 uptake and O 2 production, which along with the apparent stripping of the O 2 to the equilibrium level by the microbubbles, strongly suggests that the bioreactor is not mass transfer limited, nor O 2 inhibited. Removing O 2 inhibition results in high growth rates and high density of biomass.