Insight into the Pseudocapacitive Behavior of Electroactive Biofilms in Response to Dynamic-Controlled Electron Transfer and Metabolism Kinetics for Current Generation in Water Treatment.

Electroactive biofilms (EBs) engage in complex electron transfer and storage processes involving intracellular and extracellular mediators with temporary electron storage capabilities. Consequently, electroactive biofilms exhibit pseudocapacitive behaviors during substrate degradation processes. However, comprehensive systematic research in this area has been lacking. This study demonstrated that the pseudocapacitive property was an intrinsic characteristic of EBs. This property represents dynamic-controlled electron transfer and is critical in current generation, unlike noncapacitive responses. Nontransient charge and discharge experiments revealed a correlation between capacitive charge accumulation and current generation in EBs. Additionally, analysis of substrate degradation suggested that the maximum power density (Pmax) changed with the kinetic constants of COD degradation, with pseudocapacitances of EBs directly proportional to Pmax. The interaction networks of key latent variables were evaluated through partial least-squares path modeling analysis. The results indicated that cytochrome c was closely associated with the formation of pseudocapacitance in EBs. In conclusion, pseudocapacitance can be considered a valuable indicator for assessing the complex electron transfer behavior of EBs. Pseudocapacitive biofilms have the potential to efficiently regulate biological reactions and serve as a promising carbon-neutral and renewable strategy for energy generation and storage. An in-depth understanding of the intrinsic property of pseudocapacitive behavior in EBs can undoubtedly advance the development of this concept in the future.

In-Situ Measurement of Fresh Produce Respiration Using a Modular Sensor-Based System.

In situ, continuous and real-time monitoring of respiration (R) and respiratory quotient (RQ) are crucial for identifying the optimal conditions for the long-term storage of fresh produce. This study reports the application of a gas sensor (RMS88) and a modular respirometer for in situ real-time monitoring of gas concentrations and respiration rates of strawberries during storage in a lab-scale controlled atmosphere chamber (190 L) and of Pinova apples in a commercial storage facility (170 t). The RMS88 consisted of wireless O2 (0% to 25%) and CO2 sensors (0% to 0.5% and 0% to 5%). The modular respirometer (3.3 L for strawberries and 7.4 L for apples) consisted of a leak-proof arrangement with a water-containing base plate and a glass jar on top. Gas concentrations were continuously recorded by the RMS88 at regular intervals of 1 min for strawberries and 5 min for apples and, in real-time, transferred to a terminal program to calculate respiration rates ( R O 2 and R CO 2 ) and RQ. Respiration measurement was done in cycles of flushing and measurement period. A respiration measurement cycle with a measurement period of 2 h up to 3 h was shown to be useful for strawberries under air at 10 °C. The start of anaerobic respiration of strawberries due to low O2 concentration (1%) could be recorded in real-time. R O 2 and R CO 2 of Pinova apples were recorded every 5 min during storage and mean values of 1.6 and 2.7 mL kg-1 h-1, respectively, were obtained when controlled atmosphere (CA) conditions (2% O2, 1.3% CO2 and 2 °C) were established. The modular respirometer was found to be useful for in situ real-time monitoring of respiration rate during storage of fresh produce and offers great potential to be incorporated into RQ-based dynamic CA storage system.

Supervised probabilistic dynamic-controlled latent-variable model for quality pattern prediction and optimisation.

A supervised probabilistic dynamic-controlled latent-variable (SPDCLV) model is proposed for online prediction, as well as real-time optimisation of process quality indicators. Compared to existing probabilistic latent-variable models, the key advantage of the proposed method lies in explicitly modelling the dynamic causality from the manipulated inputs to the quality pattern. This is achieved using a well-designed, dynamic-controlled Bayesian network. Furthermore, the algorithms for expectation-maximisation, forward filtering, and backward smoothing are designed for learning the SPDCLV model. For engineering applications, a framework for pattern-based quality prediction and optimisation is proposed, under which the pattern-filtering and pattern-based soft sensor are explored for online quality prediction. Furthermore, quality optimisation can be realised by directly controlling the pattern to the desired condition. Finally, case studies on both an industrial primary milling circuit and a numerical example illustrate the benefits of the SPDCLV method in that it can fully model the process dynamics, effectively predict and optimise the quality indicators, and monitor the process.

Dynamic controllable residual generative adversarial network for low-dose computed tomography imaging.

Background: Computed tomography (CT) imaging technology has become an indispensable auxiliary method in medical diagnosis and treatment. In mitigating the radiation damage caused by X-rays, low-dose computed tomography (LDCT) scanning is becoming more widely applied. However, LDCT scanning reduces the signal-to-noise ratio of the projection, and the resulting images suffer from serious streak artifacts and spot noise. In particular, the intensity of noise and artifacts varies significantly across different body parts under a single low-dose protocol. Methods: To improve the quality of different degraded LDCT images in a unified framework, we developed a generative adversarial learning framework with a dynamic controllable residual. First, the generator network consists of the basic subnetwork and the conditional subnetwork. Inspired by the dynamic control strategy, we designed the basic subnetwork to adopt a residual architecture, with the conditional subnetwork providing weights to control the residual intensity. Second, we chose the Visual Geometry Group Network-128 (VGG-128) as the discriminator to improve the noise artifact suppression and feature retention ability of the generator. Additionally, a hybrid loss function was specifically designed, including the mean square error (MSE) loss, structural similarity index metric (SSIM) loss, adversarial loss, and gradient penalty (GP) loss. Results: The results obtained on two datasets show the competitive performance of the proposed framework, with a 3.22 dB peak signal-to-noise ratio (PSNR) margin, 0.03 SSIM margin, and 0.2 contrast-to-noise ratio margin on the Challenge data and a 1.0 dB PSNR margin and 0.01 SSIM margin on the real data. Conclusions: Experimental results demonstrated the competitive performance of the proposed method in terms of noise decrease, structural retention, and visual impression improvement.

A Review of Storage Temperature Recommendations for Apples and Pears.

An exploration of the range of expert opinions on the optimum storage temperature for apples and pears in RA (refrigerated air), CA (controlled atmosphere), and DCA (dynamic controlled atmosphere) is provided, based on the accumulated postharvest data from the last 20 years. Apple cultivars have been divided into two storage temperature groups (0 to 1 °C and >1 °C), based on chilling sensitivity. Increasingly, gradual cooling, rather than rapid cooling, is recommended for apple cultivars, especially for chilling-sensitive cultivars. European pear cultivars are held at storage temperatures close to or just below 0 °C since they are not chilling-sensitive, and most cultivars require a cold temperature to induce ethylene production and ripening, especially if picked early for long-term storage. Asian pears apparently have higher temperature requirements in CA, compared with European pears. The temperature recommendations for RA and CA storage differ in some apple and European pear cultivars. In such cases, the CA recommendation is, on average, approximately 0.9 °C higher for apple cultivars and approximately 0.5 °C higher for pear cultivars, compared with RA. Research evidence suggests that some apple and pear cultivars can be stored at higher temperatures in DCA than in CA, and if the ethylene inhibitor, 1-methylcyclopropene (1-MCP), is applied in CA and/or DCA, leading to possible energy savings and quality benefits. A cool growing season may increase postharvest disorders, depending on cultivar and region. The store or packinghouse manager may choose to mitigate potential postharvest problems by maintaining the storage temperature at or above the temperature listed here and/or using stepwise (gradual) cooling. The storage temperature can affect the humidity and vapour pressure deficit (driving force) in the storage room. Altering the vapour pressure deficit controls the water loss in stored fruit, which can affect various quality parameters and the occurrence of several storage disorders.

Short-Term Responses of Apple Fruit to Partial Reoxygenation during Extreme Hypoxic Storage Conditions.

The short-term (24 h) responses of apple fruit (cv. 'Granny Smith') to a shift in the oxygen concentration from 0.4 to 0.8 kPa, a protocol applied in the dynamic controlled atmosphere (DCA) storage technique, have been studied. Metabolomics and transcriptomics analyses of cortex tissue showed an immediate down-regulation of fermentative metabolism and of the GABA shunt in parallel with the activation of several 2-oxoglutarate-dependent dioxygenase genes. Down-regulation of the free phenylpropanoid pathway genes and the diversion of propanoid synthesis toward the methyl-erythritol phosphate route were also observed. Partial reoxygenation induced increases of glyceric, palmitic, and stearic acids and of several phosphatidylcholines and phosphatidylethanolamines and decreases of specific amino acids (valine, methionine, glycine, phenylalanine, and GABA), organic acids (arachidic and citric acids), and secondary metabolites (catechin and epicatechin). The oxygen shift also resulted in transcriptional rewiring of several components of IAA and ABA regulation and signaling. These results provide novel insights on the complexity of the short-term physiological responses of apple fruit to partial reoxygenation applied during DCA storage.

Effects of a high O2 dynamic-controlled atmosphere technology on the browning of postharvest white mushroom ( Agaricus bisporus) in relation to energy metabolism.

Browning is one of the main problems in senescence of mushrooms, and it is also one of the most important attributes accounting for the loss of the quality and reduction in market value. In order to study the relationship between the energy metabolism and the browning of white mushroom under high O2 dynamic-controlled atmosphere (HO-DCA), mushrooms were stored in 100% O2 (SCA1), 80% O2 + 20% CO2 (SCA2), 100% O2 for three days and then transferred into the treatment of 80% O2 + 20% CO2 (HO-DCA) at 2 ± 1 ℃ and air as control. In this study, adenosine triphosphate (ATP) content, energy charge level, sensory evaluation, browning of surface and flesh, cell membrane integrity, exogenous ATP, polyphenol oxidase (PPO) and peroxidase (POD) activity and genes encoding PPO of the white mushroom were investigated. These were all closely related to the browning of products. The optimal storage condition of the HO-DCA treatment could delay the browning of pericarp and flesh tissues of the mushrooms, inhibit PPO activity and reduce the relative expression levels of the three genes encoding PPO. Meanwhile, it maintained moderate POD activity, good sensory properties and cell membrane integrity in a certain extent and thus slowed down the senescence of mushrooms. Results indicated that there was a positive correlation between the ATP content and whitening index ( r = 0.901). In addition, HO-DCA maintained a higher ATP level, prolonged the storage time to 28 days and it might be an ideal strategy for preserving the quality of mushroom during storage.