Single-Cell Detection of Erwinia amylovora Using Bio-Functionalized SIS Sensor.

Herein, we developed a bio-functionalized solution-immersed silicon (SIS) sensor at the single-cell level to identify Erwinia amylovora (E. amylovora), a highly infectious bacterial pathogen responsible for fire blight, which is notorious for its rapid spread and destructive impact on apple and pear orchards. This method allows for ultra-sensitive measurements without pre-amplification or labeling compared to conventional methods. To detect a single cell of E. amylovora, we used Lipopolysaccharide Transporter E (LptE), which is involved in the assembly of lipopolysaccharide (LPS) at the surface of the outer membrane of E. amylovora, as a capture agent. We confirmed that LptE interacts with E. amylovora via LPS through in-house ELISA analysis, then used it to construct the sensor chip by immobilizing the capture molecule on the sensor surface modified with 3'-Aminopropyl triethoxysilane (APTES) and glutaraldehyde (GA). The LptE-based SIS sensor exhibited the sensitive and specific detection of the target bacterial cell in real time. The dose-response curve shows a linearity (R2 > 0.992) with wide dynamic ranges from 1 to 107 cells/mL for the target bacterial pathogen. The sensor showed the value change (dΨ) of approximately 0.008° for growing overlayer thickness induced from a single-cell E. amylovora, while no change in the control bacterial cell (Bacillus subtilis) was observed, or negligible change, if any. Furthermore, the bacterial sensor demonstrated a potential for the continuous detection of E. amylovora through simple surface regeneration, enabling its reusability. Taken together, our system has the potential to be applied in fields where early symptoms are not observed and where single-cell or ultra-sensitive detection is required, such as plant bacterial pathogen detection, foodborne pathogen monitoring and analysis, and pathogenic microbial diagnosis.

Multiple-layer statistical methodology for developing data-driven models of anaerobic digestion process.

When modelling anaerobic digestion, ineffective data handling and inadequate designation of modelling parameters can undermine the model reliability. In this study, a multilayer statistical technique, which employed a machine learning technique using regression models, was introduced to systematically support the development of anaerobic digestion models. Layer-by-layer statistical techniques including cubic smoothing splines (missing data reconstruction), principal component analysis (identifying correlated parameters), analysis of variance (analysing differences among datasets), and linear regression (developing data-driven models) were used to develop and validate anaerobic digestion models. Experimental data collected from the long-term operation of lab-scale (operated for 350 days), pilot-scale (operated for 150 days), and full-scale reactors (operated for 750 days) were used to demonstrate the modelling process. The multivariate models based on a data-driven modelling technique were developed by subjecting the experimental and monitored data to a modelling process. The developed models could predict the biogas production and effluent chemical oxygen demand during anaerobic digestion. Statistical analyses verified the modelling hypotheses, evaded invalid model development, and ensured data integrity and parameter validity. Multiple linear regression of principal components demonstrated that the performance of biogas production using food waste was influenced by the variances of the nitrogen and organic concentrations, but not by the chemical oxygen demand to total nitrogen (C/N) ratio. In the validation process, the model developed with lab-scale reactor data showed relatively high accuracy with R2, SSE, and RMSE values of 0.86, 34.45, and 0.72.

Multi-Odor Discrimination by Rat Sniffing for Potential Monitoring of Lung Cancer and Diabetes.

The discrimination learning of multiple odors, in which multi-odor can be associated with different responses, is important for responding quickly and accurately to changes in the external environment. However, very few studies have been done on multi-odor discrimination by animal sniffing. Herein, we report a novel multi-odor discrimination system by detection rats based on the combination of 2-Choice and Go/No-Go (GNG) tasks into a single paradigm, in which the Go response of GNG was replaced by 2-Choice, for detection of toluene and acetone, which are odor indicators of lung cancer and diabetes, respectively. Three of six trained rats reached performance criterion, in 12 consecutive successful tests within a given set or over 12 sets with a success rate of over 90%. Through a total of 1300 tests, the trained animals (N = 3) showed multi-odor sensing performance with 88% accuracy, 87% sensitivity and 90% specificity. In addition, a dependence of behavior response time on odor concentrations under given concentration conditions was observed, suggesting that the system could be used for quantitative measurements. Furthermore, the animals' multi-odor sensing performance has lasted for 45 days, indicating long-term stability of the learned multi-odor discrimination. These findings demonstrate that multi-odor discrimination can be achieved by rat sniffing, potentially providing insight into the rapid, accurate and cost-effective multi-odor monitoring in the lung cancer and diabetes.

Olfactory Detection of Toluene by Detection Rats for Potential Screening of Lung Cancer.

Early detection is critical to successfully eradicating a variety of cancers, so the development of a new cancer primary screening system is essential. Herein, we report an animal nose sensor system for the potential primary screening of lung cancer. To establish this, we developed an odor discrimination training device based on operant conditioning paradigms for detection of toluene, an odor indicator component of lung cancer. The rats (N = 15) were trained to jump onto a floating ledge in response to toluene-spiked breath samples. Twelve rats among 15 trained rats reached performance criterion in 12 consecutive successful tests within a given set, or over 12 sets, with a success rate of over 90%. Through a total of 1934 tests, the trained rats (N = 3) showed excellent performance for toluene detection with 82% accuracy, 83% sensitivity, 81% specificity, 80% positive predictive value (PPV) and 83% negative predictive value (NPV). The animals also acquired considerable performance for odor discrimination even in rigorous tests, validating odor specificity. Since environmental and long-term stability are important factors that can influence the sensing results, the performance of the trained rats was studied under specified temperature (20, 25, and 30 °C) and humidity (30%, 45%, and 60% RH) conditions, and monitored over a period of 45 days. At given conditions of temperature and humidity, the animal sensors showed an average accuracy within a deviation range of ±10%, indicating the excellent environmental stability of the detection rats. Surprisingly, the trained rats did not differ in retention of last odor discrimination when tested 45 days after training, denoting that the rats' memory for trained odor is still available over a long period of time. When taken together, these results indicate that our odor discrimination training system can be useful for non-invasive breath testing and potential primary screening of lung cancer.

Genetically Modified Ferritin Nanoparticles with Bone-Targeting Peptides for Bone Imaging.

Bone homeostasis plays a major role in supporting and protecting various organs as well as a body structure by maintaining the balance of activities of the osteoblasts and osteoclasts. Unbalanced differentiation and functions of these cells result in various skeletal diseases, such as osteoporosis, osteopetrosis, and Paget's disease. Although various synthetic nanomaterials have been developed for bone imaging and therapy through the chemical conjugation, they are associated with serious drawbacks, including heterogeneity and random orientation, in turn resulting in low efficiency. Here, we report the synthesis of bone-targeting ferritin nanoparticles for bone imaging. Ferritin, which is a globular protein composed of 24 subunits, was employed as a carrier molecule. Bone-targeting peptides that have been reported to specifically bind to osteoblast and hydroxyapatite were genetically fused to the N-terminus of the heavy subunit of human ferritin in such a way that the peptides faced outwards. Ferritin nanoparticles with fused bone-targeting peptides were also conjugated with fluorescent dyes to assess their binding ability using osteoblast imaging and a hydroxyapatite binding assay; the results showed their specific binding with osteoblasts and hydroxyapatite. Using in vivo analysis, a specific fluorescent signal from the lower limb was observed, demonstrating a highly selective affinity of the modified nanoparticles for the bone tissue. These promising results indicate a specific binding ability of the nanoscale targeting system to the bone tissue, which might potentially be used for bone disease therapy in future clinical applications.

Application of principal component analysis (PCA) to the assessment of parameter correlations in the partial-nitrification process using aerobic granular sludge.

For the first time, principal component analysis (PCA) was used to extract relevant information hidden in the partial-nitrification process using aerobic granular sludge. The objectives of this research are (a) to determine total ammonia nitrogen (TAN), total nitrite nitrogen (NO2-N), nitrate nitrogen (NO3-N), and other water quality parameters; (b) to identify the diversity of nitrification and denitrification bacterial community of wastewater samples during the partial-nitrification process using aerobic granular sludge and; (c) to analyze the correlation of available parameters using PCA. The nitrite accumulation ratio was determined from TAN, NO2-N, and NO3-N. Other water quality parameters were mixed liquor volatile suspended solids (MLVSS), alkalinity, total nitrogen (TN) and sludge volume index (SVI), pH, and dissolved oxygen (DO). The identification of bacterial community was conducted using 16S rRNA gene-based pyrosequencing by GS Junior Sequencing system. The water quality parameters were computed for PCA using software MATLAB. A nitrite accumulation ratio (NAR) between 0.55 and 0.85 was determined while maintaining the aerobic granular sludge's compact and dense structure. The PCA was used to reduce the data dimensionality from the original 8 variables to 2 principal components explaining 75% of the total data variance. Applying PCA to the data analysis in biological wastewater treatment can support detecting data anomalies and separating useful information from unwanted interferences.

Effect of hydrothermal pre-treatment on physical properties and co-digestion from food waste and sewage sludge mixture.

Anaerobic digestion (AD) is generally considered to be an economic and environmentally friendly technology for treating waste activated sludge, but has some limitations, such as the time it takes for the sludge to be digested and also the ineffectiveness of degrading the solids. Various pre-treatment technologies have been suggested to overcome these limitations and to improve the biogas production rate by enhancing the hydrolysis of organic matter. This paper studies the use of hydrothermal pre-treatment (HTP) for a food waste and sewage sludge mixture (FW-SS mixture) as pre-treatment of co-digestion. The results of the capillary suction time, time to filter, and particle size decreased with increasing HTP temperature. These results of the assessment that was conducted in this study confirm that the HTP process indeed modifies the physical properties of the FW-SS mixture to enhance the solubilization of organic solids. A maximum increase in biogas production of 50% is achieved with a HTP temperature of 140oC. These findings show that to achieve high conversion efficiency, an accurately designed pre-treatment step must be included in the overall AD process for wastewater treatment.

Application of curve-fitting techniques to develop numerical calibration procedures for a river water quality model.

River water quality models are often constrained by a lack of understanding of model structures and complicated estimation procedures for unknown parameters. This paper demonstrates a new calibration strategy by setting up a simple model structure for river water quality. The unknown parameters of RWQM were calibrated through the use of small river water quality data sets. In order to facilitate the calibration procedure, data reconstruction and parameter estimation were performed by the systematic application of cubic smoothing spline, polynomial curve-fitting and nonlinear least squares. The quality of calibrated parameters was estimated by developing a sensitivity ranking system. The variation of model outputs showed a slight difference at a sensitivity index of less than 10% and a significant difference at a sensitivity index of more than 50%. The one-way analysis of variance showed a large p-value of 0.8431, indicating that differences between model data and measured data means are not significant. The calibrated model responses and their statistical envelopes were in good agreement with the river water quality data. A MATLAB GUI platform was developed to perform numerical and graphical analysis, which can be used as a relatively simple but robust calibration tool to support model application and data analysis.

Optimizing pre-treatment conditions for anaerobic co-digestion of food waste and sewage sludge.

In this study, the optimum conditions of thermal-alkali pre-treatment and the performance of ammonia stripping were investigated for improving solubilization efficiency and methane yield in the anaerobic co-digestion of food waste (FW) and sewage sludge (SS). The reaction temperature, NaOH concentration and reaction time for the thermal-alkali pre-treatment were investigated to determine optimum pre-treatment conditions. Solubilization rate, volatile suspended solids (VSS) reduction rate and total volatile fatty acid (VFAs) yields were improved with increasing reaction temperature, NaOH concentration and reaction time. In addition, by applying the optimum pre-treatment conditions (140 °C, 60 meq/L and 60 min), the experimental methane yield of thermal-alkali pre-treatment of a mixture of FW and SS was 483.0 ±â€¯15.7 mL CH4/g VSadded, which was about 84% higher than that of the untreated one. However, after thermal-alkali pre-treatment, the NH4+ concentration of the thermal-alkali pre-treatment liquid showed a concentration that could inhibit anaerobic digestion, so ammonia stripping was performed to remove NH4+. As a result, the experimental methane yield was increased by about 7% compared to when ammonia stripping was not performed.

Applications and Advances in Bioelectronic Noses for Odour Sensing.

A bioelectronic nose, an intelligent chemical sensor array system coupled with bio-receptors to identify gases and vapours, resembles mammalian olfaction by which many vertebrates can sniff out volatile organic compounds (VOCs) sensitively and specifically even at very low concentrations. Olfaction is undertaken by the olfactory system, which detects odorants that are inhaled through the nose where they come into contact with the olfactory epithelium containing olfactory receptors (ORs). Because of its ability to mimic biological olfaction, a bio-inspired electronic nose has been used to detect a variety of important compounds in complex environments. Recently, biosensor systems have been introduced that combine nanoelectronic technology and olfactory receptors themselves as a source of capturing elements for biosensing. In this article, we will present the latest advances in bioelectronic nose technology mimicking the olfactory system, including biological recognition elements, emerging detection systems, production and immobilization of sensing elements on sensor surface, and applications of bioelectronic noses. Furthermore, current research trends and future challenges in this field will be discussed.

An innovative U-shaped sludge bed anammox process for nitrogen removal.

In this study, we proposed an innovative U-shaped sludge bed reactor which could be a cost effective and simplified approach for the operation of an anammox reactor. The performance for nitrogen removal and the composition of bacterial communities were investigated for about 500 days of operation. The nitrogen removal rate could be approximately 85% when the total nitrogen loading rate was about 0.54 kg N/m3/d. The 16S rRNA gene pyrosequencing analysis of the bacterial community determined that Betaproteobacteria (class level) of the ammonia-oxidizing bacteria (AOB) community, Nitrospira (genus level) of the nitrite-oxidizing bacteria (NOB) community, and Brocadia (genus level) of the anammox bacteria community simultaneously coexisted in the reactor sludge. These results demonstrated that simultaneous growth and coexistence of AOB, NOB, and anammox were capable within the reactor. Furthermore, a mathematical modeling system was developed to simulate the nitrification and anammox processes. The model simulation showed that the oxygen was rapidly depleted and that led to a drop in the activity of AOB and NOB, then the growth of anammox bacteria started under anaerobic conditions.

Mathematical models and bacterial communities for ammonia toxicity in mesophilic anaerobes not acclimated to high concentrations of ammonia.

In this study, we evaluated ammonia toxicity in mesophilic anaerobic digestion at various pH values and total ammonia nitrogen (TAN) concentrations. We performed anaerobic toxicity assays (ATAs) to evaluate the toxicity effects of TAN and pH on mesophilic anaerobic digestion. Modeling based on the results of the ATAs indicated that the specific methanogenic activity (SMA) decreased by 30% at a TAN concentration higher than 3.0 g/L compared to a TAN concentration of 0 g/L. In addition, the highest SMA for a given TAN level (0.5-10.0 g/L) was observed at a pH of around 7.6. The results of bacterial community analyses showed that the diversity and richness of microorganisms with increasing TAN concentration were decreased. Chloroflexi and Synergistetes were the dominant phyla at TAN concentrations less than 3.0 g/L, and Firmicutes was the dominant phylum at TAN concentrations higher than 3.0 g/L, implying that the ammonia toxicity concentration may influence the kind of dominant species. In conclusion, to start a stable mesophilic anaerobic digestion concerning ammonia toxicity, a TAN concentration less than 3.0 g/L is preferable.

Fisetin, a dietary flavonoid, induces apoptosis of cancer cells by inhibiting HSF1 activity through blocking its binding to the hsp70 promoter.

Heat shock factor 1 (HSF1) is a transcription factor for heat shock proteins (HSPs) expression that enhances the survival of cancer cells exposed to various stresses. HSF1 knockout suppresses carcinogen-induced cancer induction in mice. Therefore, HSF1 is a promising therapeutic and chemopreventive target. We performed cell-based screening with a natural compound collection and identified fisetin, a dietary flavonoid, as a HSF1 inhibitor. Fisetin abolished heat shock-induced luciferase activity with an IC50 of 14 µM in HCT-116 cancer cells. The treatment of HCT-116 with fisetin inhibited proliferation with a GI50 of 23 µM. When the cells were exposed to heat shock in the presence of fisetin, the induction of HSF1 target proteins, such as HSP70, HSP27 and BAG3 (Bcl-2-associated athanogene domain 3), were inhibited. HSP70/BAG3 complexes protect cancer cells from apoptosis by stabilizing anti-apoptotic Bcl-2 family proteins. The downregulation of HSP70/BAG3 by fisetin significantly reduced the amounts of Bcl-2, Bcl-xL and Mcl-1 proteins, subsequently inducing apoptotic cell death. Chromatin immunoprecipitation assays showed that fisetin inhibited HSF1 activity by blocking the binding of HSF1 to the hsp70 promoter. Intraperitoneal treatment of nude mice with fisetin at 30mg/kg resulted in a 35.7% (P < 0.001) inhibition of tumor growth.

Surface plasmon resonance: a versatile technique for biosensor applications.

Surface plasmon resonance (SPR) is a label-free detection method which has emerged during the last two decades as a suitable and reliable platform in clinical analysis for biomolecular interactions. The technique makes it possible to measure interactions in real-time with high sensitivity and without the need of labels. This review article discusses a wide range of applications in optical-based sensors using either surface plasmon resonance (SPR) or surface plasmon resonance imaging (SPRI). Here we summarize the principles, provide examples, and illustrate the utility of SPR and SPRI through example applications from the biomedical, proteomics, genomics and bioengineering fields. In addition, SPR signal amplification strategies and surface functionalization are covered in the review.

Evaluation of denitrification-nitrification biofilter systems in treating wastewater with low carbon: nitrogen ratios.

A two-stage biological aerated/anoxic filter (BAF) system for denitrification-nitrification was developed to increase nitrogen removal in the treatment of municipal wastewater with low carbon:nitrogen (C/N) ratio [Formula: see text]. This system exhibited a high denitrification efficiency (67%), despite the low C/N ratio, and the ratio of reduced nitrate to consumed organic compounds was greater than the theoretical value due to the minimization of the conversion of organic carbon to biomass growth, the maintenance of low levels of dissolved oxygen in recycled water, and the maximization of use of organic carbon biosorbed inside biomass in the denitrification BAF. The maximum rate of nitrogen removal was achieved at a recycle ratio of 170%, and the headloss in two BAFs was maintained after a 24-h backwash. Biological nitrogen removal in a two-stage BAF system was possible in a short hydraulic retention time (1.2 h) because the maximum reaction rates of nitrifiers and denitrifiers in each column were achieved.

Overexpression and self-assembly of virus-like particles in Nicotiana benthamiana by a single-vector DNA replicon system.

Based on recent developments, virus-like particles (VLPs) are considered to be perfect candidates as nanoplatforms for applications in materials science and medicine. To succeed, mass production of VLPs and self-assembly into a correct form in plant systems are key factors. Here, we report expression of synthesized coat proteins of the three viruses, Brome mosaic virus, Cucumber mosaic virus, and Maize rayado fino virus, in Nicotiana benthamiana and production of self-assembled VLPs by transient expression system using agroinfiltration. Each coat protein was synthesized and cloned into a pBYR2fp single replicon vector. Target protein expression in cells containing p19 was fourfold higher than that of cells lacking p19. After agroinfiltration, protein expression was analyzed by SDS-PAGE and quantitative image analyzer. Quantitative analysis showed that BMVCP, CMVCP, and MRFVCP concentrations were 0.5, 1.0, and 0.8 mg · g(-1) leaf fresh weight, respectively. VLPs were purified by sucrose cushion ultracentrifugation and then analyzed by transmission electron microscopy. Our results suggested that BMVCP and CMVCP proteins expressed in N. benthamiana leaves were able to correctly self-assemble into particles. Moreover, we evaluated internal cavity accessibility of VLPs to load foreign molecules. Finally, plant growth conditions after agroinfiltration are critical for increasing heterologous protein expression levels in a transient expression system.

Anaerobic degradation of amino acids generated from the hydrolysis of sewage sludge.

The anaerobic degradation of each amino acid that could be generated through the hydrolysis of sewage sludge was evaluated. Stickland reaction as an intermediate reaction between two kinds of amino acids was restricted in order to evaluate each amino acid. Changes in the chemical oxygen demand (COD), T-N, NH4(+)-N, biogas, and CH4 were analysed for the anaerobic digestion process. The initial nitrogen concentration of all amino acids is adjusted as 1000 mg/L. The degradation rate of the amino acids was determined based on the ammonia form of nitrogen, which is generated by the deamination of amino acids. Among all amino acids, such as alpha-alanine, beta-alanine, lysine, arginine, glycine, histidine, cysteine, methionine, and leucine, deamination rates of cysteine, leucine, and methionine were just 61.55%, 54.59%, and 46.61%, respectively, and they had low removal rates of organic matter and showed very low methane production rates of 13.55, 71.04, and 80.77 mL CH4/g CODin, respectively. Especially for cysteine, the methane content was maintained at approximately 7% during the experiment. If wastewater contains high levels of cysteine, leucine, and methionine and Stickland reaction is not prepared, these amino acids may reduce the efficiency of the anaerobic digestion.

Model-based development of cell voltage control system for modified bio-electro-Fenton process.

A modified bio-electro-Fenton (M-BEF) process with a cell voltage control system that improves the efficiency of organic removal and energy savings is demonstrated. The M-BEF process can accomplish bioelectricity generation, H2O2 production, and the Fenton reaction in a continuous-flow reactor. During synthetic wastewater treatment containing biodegradable (glucose) and recalcitrant (biphenyl) organic matter, the effluent chemical oxygen demand (COD) concentration was maintained between 2 and 6 mg L-1. To investigate the impact of different operating schemes on energy usage, model-based design (MBD) modeling and simulations were performed, which showed that COD removal efficiency without an external voltage supply was unstable at < 70 %. The automatic cell voltage control system saved 90 % of the power compared to the continuous cell voltage supply system. Further testing on more environmental samples and pollutants will enable real-time optimization of supplied power and wastewater treatment using the cell voltage control system.

Use of steady-state biofilm model to characterize aerobic granular sludge.

In this study, a steady-state biofilm model was utilized to elucidate the aerobic granular sludge process. In order to describe the experiment data using the model, two different sizes of stabilized aerobic granules (with mean diameters of 0.6 mm and 3 mm, respectively) were investigated through the laboratory operations of sequencing batch reactors (SBRs). On the basis of the estimated parameters from experiments, the model was established to describe the biofilm functions in aerobic granules, such as substrate transfer and substrate concentration variations within the granules. In the modeling at different chemical oxygen demand (COD) concentrations (100-1000 mg/L), the biofilm functions were affected by the characteristics of aerobic granules, such as diameter and density. The larger granules modeled higher substrate fluxes (1.8-14 mg/cm(2)-d) and deeper effective film thicknesses (0.18-1.3 cm). The multi-aspect simulation results demonstrated that the substrate either approaches zero in the film (at low concentration) or penetrates the granules (at high concentration). The sum of aerobic granules in the SBRs can be considered as a mass of biofilm, where the substrate approaches zero at sufficient depth. The steady-state biofilm model could be an effective prediction method for optimizing the aerobic granular sludge process.

Recalcitrant organic matter removal from textile wastewater by an aerobic cell-immobilized pellet column.

The treatment of textile wastewater is difficult because of its recalcitrant organic content. The biological removal of recalcitrant organics requires a long retention time for microbial growth. Activated sludge was immobilized in a polyethylene glycol pellet to allow for sufficient sludge retention time. The pellets were filled in an aerobic cell-immobilized pellet column (CIPC) reactor in order to investigate the removal of recalcitrant organics from textile wastewater. A textile wastewater effluent treated by a conventional activated sludge reactor was used as a target wastewater. The chemical oxygen demand (COD) removal efficiency of the aerobic CIPC reactor at various empty bed contact times was in the range of 42.2-60.5%. Half of the input COD was removed in the lower part (bottom 25% of the reactor volume) of the reactor when the organic loading rate was less than 1.5 kg COD/(m(3)•d). About 15-30% of the input COD was removed in the remaining part of the column reactor. The COD removed in this region was limitedly biodegradable. The biodegradation of recalcitrant organics could be carried out by the interactional functions of the various bacteria consortia by using a cell-immobilization process. The CIPC process could effectively treat textile wastewater using a short retention time because the microorganisms that degrade limitedly biodegradable organics were dominant in the reactor.