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Most industrial process data are highly dynamic and nonlinear with time-varying characteristics, which poses great challenges to industrial fault diagnosis tasks. In addition, in actual industries, limited training samples are often obtained due to safety requirements and high maintenance costs. Therefore, it is crucial to effectively extract representative features and identify faults in a scenario with limited data. To address these issues, this study proposes an elastic slow feature prototypical network (ESFPN) under the meta-learning framework for few-shot dynamic fault diagnosis. First, a Siamese embedding network is customized with a loss function for slow feature (SF) extraction, aiming at characterizing process states effectively. Second, an elastic mechanism is introduced to the metric-based prototypical network (PN), which employs elastic distances to emphasize the classification impact of more important prototype vectors. ESFPN utilizes the extracted nonlinear SFs as the elastic metric network inputs to construct SF prototypes, which significantly improves the fault classification performance. The performance of ESFPN and other advanced comparative methods is evaluated using the Tennessee Eastman process (TEP). The results demonstrate the significant superiority and effectiveness of the proposed method.

[Display omitted] • The wider interlayer spacing and high the water flux of the membrane by inducing zeolite nanoparticles. • The excellent stability of the membrane by polyamide crosslinked reaction of GO and NaY. • A novel GO/NaY/Nylon membrane has an outstanding adsorption capability for CIP in water. To improve the water flux and stability of GO membrane, GO/NaY/Nylon membrane was prepared, in which zeolite were used to increase the layer spacing of GO nanosheets. And the interfacial polymerization of piperazine (PIP) and trimesoyl chloride (TMC) was employed to improve the stability of membrane. The GO/NaY/Nylon membrane with high content of nanoparticles was first used for antibiotic adsorption. The static thermodynamics, kinetics and isotherm for ciprofloxacin (CIP) were studied. The adsorption mechanism on CIP is chemisorption, conforming to the pseudo-second-order model and Langmuir model, and as the equilibrium concentration (c e) increases, the maximum adsorption capacity of GO/NaY/Nylon membrane first increases rapidly, then reaches a plateau, with a maximum adsorption capacity of 49.2 mg·g−1 at 328 K (c e :45.1 mg·L-1). The GO/NaY/Nylon membrane showed higher water flux and stability than the GO membranes, indicating great promising in removing CIP from aqueous solution. [ABSTRACT FROM AUTHOR]

Homocysteine (Hcy) is a sulphur-containing nonessential amino acid derived from the intermediate metabolites of methionine. Methionine is obtained from dietary proteins, such as poultry, meat, eggs, seafood, and dairy products. Abnormalities in Hcy metabolic pathways, deficiencies in dietary methionine, folate, and vitamins B12, B6, and B2and genetic defects, polymorphisms, or mutations in Hcy metabolism-related enzymes may lead to an increase in plasma Hcy levels. Generally, a plasma Hcy level higher than 10 or 15 μmol/L has been defined as hyperhomocysteinemia (HHcy). An individual with essential hypertension complicated with HHcy is considered to have H-type hypertension (HTH). Currently, HHcy is considered a novel independent risk factor for various cardiovascular diseases. To provide a useful reference for clinicians, the research progress on Hcy, HHcy, and HTH in recent years was systematically reviewed here, with a focus on the source and metabolic pathways of Hcy, plasma Hcy levels and influencing factors, detection methods for plasma Hcy levels, relationship between Hcy concentration and hypertension, pathogenesis of HTH, cardiovascular complications of HTH, and treatment of HTH.

The efficient construction of π-conjugated polycyclic heteroarenes represents a significant task in the field of functional materials. A one-step oxidative tandem cyclization of aromatic acids with (benzo)thiophenes was developed to access planar sulfur-containing polycyclic heteroarenes. This protocol undergoes intermolecular cross-dehydrogenative coupling followed by intramolecular Friedel–Crafts acylation and provides a facile pathway to planar polycyclic compounds from inexpensive reactants. The synthesized heteroarenes serving as lipid-droplet-targeted probes exhibit outstanding performance with favorable biocompatibility and photostability.

The purpose of this study was to explore the histopathological staging and differential diagnosis of marginal zone lymphoma in gastric mucosa-associated lymphoid tissue (MALT lymphoma). We performed detailed histomorphology and immunohistochemistry investigations as well as genetic testing on endoscopic biopsy and endoscopic mucosal resection specimens from 18 patients with gastric MALT lymphoma. We found that gastric MALT lymphoma typically begins as a small, isolated area outside the lymphoid follicular mantle zone or proliferates in a multifocal, patchy manner, gradually spreads to the interfollicular zone, forming diffuse proliferation, invades the gastric mucosal glands, and infiltrates or proliferates into the center of peripheral reactive lymphoid follicles. Abnormally proliferating lymphocytes invade the surrounding lymphoid follicles, resulting in damage, atrophy, and disappearance of their normal follicles as well as of the gastric mucosa glands, forming diffuse proliferation. Redifferentiation and proliferation lead to the transformation of lymphocytes; that is, MALT transitions into highly invasive lymphoma. Based on our findings in this study, we propose the following five stages in the process of development and progression of gastric MALT lymphoma: the stage of cell proliferation outside the lymphoid follicular mantle zone; the stage of heterogeneous proliferative lymphoepithelial lesion; the stage of reactive lymphoid follicular implantation; the stage of lymphoid follicular clonal proliferation; and the stage of MALT transforming into highly invasive lymphoma. We examined the differential diagnosis of histopathological features at each stage. The clinicopathological staging of gastric MALT lymphoma can help clinicians provide accurate treatment and track malignant cell transformation, thus playing a significant role in controlling its development and progression.

Understanding the undular tidal bores in the Qiantang River is essential for effective river management and maintenance. While breaking tidal bores have been studied extensively, reports on undular tidal bores in the Qiantang River remain limited. Furthermore, observed data on undular tidal bores fulfilling the requirements of short measurement time intervals, and spring, medium, and neap tide coverage, and providing detailed data for the global vertical stratification of flow velocity are quite limited. Based on field observations at Qige in the Qiantang estuary, we analyzed the characteristics of undular tidal bores. The results showed that the flooding amplitude (a) of the first wave is always larger than its ebbing amplitude (b). Moreover, the vertical distribution of the maximum flood velocity exhibites three shapes, influenced by the tidal range, while that of the maximum ebb velocity exhibites a single shape. During the initial phase of the flood tide in the spring and medium tides, the upper water body experiences multiple oscillating changes along the flow direction, corresponding to the alternating process of the crest and trough of the tide level upon the arrival of the tidal bore. The tidal range is a crucial parameter in tidal bore hydrodynamics. By establishing the relationship between hydrodynamic parameters and tidal range, other hydrodynamic parameters, such as the tidal bore height, maximum flood depth-averaged velocity, maximum flood stratified velocity at the measurement points, and duration of the flood tide current, can be effectively predicted, thereby providing an important reference for river management and maintenance.

Aiming at solving low Ga leaching efficiency during actual industrial production in Danxia Smelter, chemical composition, phases and morphologies of various zinc refinery residues were characterized using ICP−AES, XRD and SEM−EDS. Compared with the normal material, Ga was embedded in the jarosite phase from the special zinc refinery residue, causing difficulty in Ga leaching. During the oxidation pressure leaching, the dissolution behaviors of Ga, As and Fe presented similar variation tendency. Namely, a sufficient H2SO4concentration and liquid-to-solid ratio promoted the dissolution of Ga, Fe and As, whereas higher reaction temperature and oxygen partial pressure weakened the leaching. Based on the results, leaching parameters were optimized for enhancing Ga recovery. Through weakening the 1st oxidation pressure leaching (by decreasing reaction temperature and total pressure to 90 °C and 0.2 MPa, respectively) and strengthening the 3rd atmospheric leaching (by increasing H2SO4concentration and reaction temperature to 160 g/L and 85 °C, respectively), the leaching efficiency of Ga increased from 77.83% to 96.46%.

Oil–gas–water three-phase medium is frequently encountered in various industrial processes. A comprehensive understanding of the discrete phase is of great importance for the industrial process safety assurance and economic benefits of production. Concerns about the discrete phase in the three-phase medium not only refer to the spatial resolution but also acoustically distinguish the gas bubble and oil droplet. We present a novel ultrasonic measurement technique for the three-phase medium using the ultrasonic phased array (UPA) sensor and complex sound pressure measurement to accomplish this goal. The proposed method can accurately provide size and location measurements to spatially resolve the bubble and droplet, and accurately provide acoustic impedance measurements to distinguish the bubble and droplet in oil-gas-water three-phase medium. In brief, we develop a forward scattering complex sound pressure peak (FSCSPP) method to quantitatively characterize the size and distribution of the bubble and droplet, a sound transmission degree (STD) method to calculate these discrete phases’ acoustic impedance values, and a sound transmission threshold (STT) parameter to distinguish the bubble and droplet. The simulation and experimental results show that the above method is highly accurate and sensitive in spatially resolving and acoustically distinguishing the bubble and droplet in the oil–gas–water three-phase medium.

A deep understanding of oil–gas–water three-phase flow behaviors and mechanics has great significance for flow controlling and modeling, production efficiency improvement, and operation safety assurance. To accurately identify horizontal oil–gas–water three-phase flow patterns, a decision-level fusion method with a parallel-tree structure is proposed based on a combined differential pressure (DP) and pulse wave ultrasonic Doppler (PWUD) sensor. With advantages of nonintrusive, easy-to-mount operation, and low cost, the combined sensor can simultaneously acquire various flowing information, including instantaneous DP fluctuations, time-varying echograms, and velocity profiles. By analyzing the dynamic sensor responses to different flow behaviors, typical flow characteristics of oil–gas–water three-phase flow are first revealed. On this basis, several features are extracted from the time-accumulated echograms, average flow velocity time series, and decomposed DP fluctuations to objectively characterize the flow patterns from different perspectives in the time, space, and time-frequency domains. Then, two parallel decision trees are constructed to, respectively, identify the gas-liquid interphase structure and oil-water interphase structure. Using the visibility of tree structures, the ability of these extracted features for flow structure distinguishment is analyzed. Finally, the final decision on the oil–gas–water three-phase flow pattern was generated by fusion the outputs of two decision trees. Tenfold cross-validation method is adopted for model performance evaluation. The results show that 12 types of oil–gas–water three-phase flow pattern are identified with the overall identification accuracy of 92.1%. This study provides a simple, affordable, and nonintrusive solution with good intelligibility and interpretability for flow pattern identification of complex industrial three-phase flows.

The process states in gas–liquid flow are the synthetical consequence of multiple apparent factors related to time, space, flow conditions, and so on. Higher order tensor decomposition in multilinear algebra provides a powerful framework mathematically to analyze the multifactor structure of flow state ensembles. Focusing on entangling the constitute factors or modes embedded in various states, a novel strategy about graph-embedding-based multilinear subspace analysis (MSA) of state ensembles in gas–liquid flow is proposed. Pulse-wave ultrasonic Doppler (PWUD) sensor is adopted to acquire spatiotemporal information of flow states under varying flow conditions. Under the architecture of graph embedding, tensor dynamic local preservation projection (TDLPP) is constructed via PWUD signal to further describe the latent characteristics of various flow states in time–space dimensions. In the reconstructed graph embedding latent space, higher order singular value decomposition (HOSVD) is used for tensor analysis of flow state ensembles that combine several modes, including different state categories, time, space, and flow conditions. The purpose of the strategy is to further achieve state monitoring of typical and transitional processes in gas–liquid flow under varying flow conditions with a simple sensor, on the foundation of refining apparent factors underlying flow state formation. Dynamic experiments are designed to verify the effectiveness of the proposed scheme.

As a high-speed, noninvasive process measurement technology, electrical resistance tomography (ERT) is well suited for visualization of media distribution in industrial and biomedical fields. An innovative optimal hybrid deep-learning strategy utilizing a 1-D convolution neural network (CNN) and recurrent neural network (RNN) is proposed to solve the ERT inverse problem. In the proposed hybrid deep-learning model, the priority-based adaptive firefly algorithm (PAFA) optimizes the neuron structure by feature engineering and adaptively estimates the local optimum to accelerate convergence. The feature selection technique controls randomness through efficient local search and finding the optimal value fit. The input and output relation is enforced by local recurrent cells in the hybrid model feedback with a bidirectional long-short-term memory (BiLSTM) network. The proposed model extracts the latent features from the prior cells during the training period of the network architecture. Simulation and experimental tests are carried out, and the quantitative analysis shows that the proposed hybrid deep-learning model has better imaging accuracy than the traditional image reconstruction methods.

Due to the advantage of high temporal resolution, cost-effectiveness, and radiation-free, electrical impedance Tomography (EIT) is considered a promising technique owning a large number of potential industrial and biomedical applications. However, the spatial resolution of EIT is still limited and its imaging results are susceptible to noise. To reduce the impact of measurement noise on the quality of EIT imaging, an adaptive statistical error model (ASEM) is proposed. Unlike noisy models trained by comparing a physical model to its digital twin, ASEM is trained by comparing a digital model to its equal resistance network. The programmable resistance network is configured according to the transfer conductivity matrix derived from the digital model and can be connected to the data acquisition system (DAS) as the physical models. Using the programmable resistance network, a large-scale training dataset can be efficiently collected. To evaluate the performance of the proposed method, a series of experiments were performed with a water tank model. Three different image reconstruction algorithms and one absolute imaging algorithm were tested. The proposed ASEM is trained on 12000 data samples collected by the developed programmable resistance network. The results show that for all tested algorithms, the conductivity reconstruction accuracy is significantly improved using ASEM.

Pressure ulcers are a particularly high incidence of chronic trauma. When a superficial wound is visible, the underlying wound is often serious, so early detection of pressure ulcers is critical. Currently, there is still no technique or real-time monitoring system that can visualize progressive tissue damage of pressure ulcers. Electrical impedance tomography (EIT) is a functional imaging technique that can aid in the diagnose of tissue health by visualizing the distribution of bioelectrical impedance parameters. Based on the differences between the electrical properties of pressure ulcers and normal tissue, a noninvasive pressure ulcer depth detection method based on EIT is proposed. Given surface voltage data measured on an open rectangular electrode array, the conductivity distribution under the skin surface was reconstructed to obtain pressure ulcer depth information which is expected to visualize progressive tissue damage. Based on EIT combined with flexible sensor arrays, finite-element simulation models and tissue-mimicking agar phantom models were established to verify the effectiveness of the EIT-based depth detection method of pressure ulcers. The effects of time-difference imaging and frequency-difference imaging on the depth detection of pressure ulcer were compared. Both simulation and physical phantom experiments demonstrated the feasibility of EIT for detection of early pressure ulcer depth.

The performance of traditional multivariate statistical process monitoring (MSPM) approaches highly depends on data quality. Due to noise disturbance, sensor drift, and some circumstance factors, process data are inevitably contaminated with uncertainty, which may destroy the correlation structure of original process data, resulting in an increase in false alarms and missed detections of traditional methods. In such cases, it is urgent to explore a new form to scientifically describe uncertainty and establish a reliable fault detection model. Inspired by the demands, a generalized Gaussian distribution-based interval principal component analysis (GGD-IPCA) method is proposed for industrial processes with uncertainty. First, a GGD-based interval estimation method is developed to transform uncertainty-contaminated data into interval-valued data and represent process uncertainty in a generalized distribution form. Then, a new interval feature extraction algorithm called GGD-IPCA is proposed by redefining mean, inner product, and square norm operators under an adjustable interval distribution form of process uncertainty with different shape parameters. Two monitoring statistics applicable to interval data are defined to monitor the operating conditions. The key to discriminate GGD-IPCA from many well-established IPCA methods, including centers PCA and complete-information PCA (CIPCA), is its ability to flexibly reduce the dimension of interval data with full consideration of various interval distributions. Experiments show that compared with the other eight algorithms, GGD-IPCA averagely reduces MDR by 4.35% with FAR no more than 2.00% in a numerical example and reduces FAR by 15.60% with average MDR as low as 2.94% in the Tennessee Eastman (TE) process.

Gas–liquid two-phase flow is a complex dynamic and nonlinear process that is widely encountered in many process industries. Accurate flow state identification is crucial for ensuring operation safety and economic benefits. However, obtaining training samples for certain flow states can be difficult due to safety requirements and high costs. Therefore, a zero-shot learning (ZSL) based flow state identification strategy is proposed from the perspective of attribute description and attribute transfer, in which the common attribute space is constructed by the semantic description of flow state categories. The attribute-relevant features are extracted by the proposed supervised deep slow and steady feature analysis (SD-S$^{\mathbf{2}}$FA) under the supervision of attributes. In SD-S$^{\mathbf{2}}$FA, an extended Siamese network is designed to extract slow and steady features (S$^{\mathbf{2}}$ Fs), in which three 1D convolutional neural networks (1D-CNN) represent the nonlinear feature embedding function, and the Siamese architecture can capture the long-term temporal coherence. Since the state attributes are shared by all the flow states, the identification for unseen flow states can be realized by attribute prediction and attribute transfer. The effectiveness and superiority of the proposed method is demonstrated through the gas–liquid two-phase flow experiment.

The interactive control of lower limb exoskeleton robots (LLERs) is important to achieve compliance and safety. Significant challenges in the interactive control of LLERs include how to accurately recognize human motion intention, how to accurately recognize the motion environment, and how to achieve natural, stable, and accurate interactive control in accordance with the human motion intention and the environment. This article presents a detailed classification of motion intention recognition, interactive control based on motion intention, and interactive control based on the LLER’s motion environment and summarizes the methodologies in each category. In addition, the advantages and disadvantages of motion intention recognition, motion environment recognition, and interactive control based on the motion intention and motion environment are analyzed from a macroscopic perspective, and current trends are discussed. Finally, this article explores which requirements should be met in future LLER applications to improve the naturalness, stability, and accuracy of interactive control.

Actinidia erianthais one of the species of kiwifruit with a particularly high ascorbic acid (AsA) content. However, the molecular mechanism driving AsA richness in fruit remains unclear. In order to reveal the molecular mechanism of AsA richness in A. eriantha, this study constructed a regulatory network related to AsA metabolism by combining genomics, metabolomics and transcriptomics. We assembled a high-quality genome of A. eriantha‘Ganlv 1’ with only five remaining gaps. The assembly is comprised of 29 pseudochromosomes with a total size of 615.95 Mb, and contig N50 of 20.35 Mb. Among them, 24 of the pseudochromosomes were obtained directly from telomere-to-telomere. The LTR assembly index score and consensus quality value were 21.34 and 39.90%, respectively. Subsequently, 61 metabolites and 2 092 genes were found to be differentially accumulated/expressed during fruit development by metabolome and transcriptome assays, respectively. AsA metabolism and the cyclic regeneration pathway were found to have high expression levels throughout fruit growth and development, suggesting its crucial role in the regulation of AsA. Furthermore, the AsA contents are highly associated with ascorbate peroxidase genes. The genome obtained in this study provides genomic resources for the genetic and breeding research of A. eriantha, and the constructed regulatory network can provide a public data platform for future research on kiwifruit.

As a rapidly growing field, microbiota research offers novel approaches to promoting ocular health and treating major retinal diseases, such as glaucoma. Gut microbiota changes throughout life; however, certain patterns of population changes have been increasingly associated with specific diseases. It has been well established that a disrupted microbiome contributes to central nervous system diseases, including Alzheimer disease, Parkinson disease, multiple sclerosis, and glioma, suggesting a prominent role of microbiome in neurodegenerative diseases. This review summarizes the progress in identifying significant changes in the microbial composition of patients with glaucoma by compiling studies on the association between microbiota and disease progression. Of interest is the relationship between increased Firmicutes/Bacteroidetes ratio in patients with primary open-angle glaucoma, increased taurocholic acid, decreased glutathione, and a reduction in retinal ganglion cell survival. Connecting these microbes to specific metabolites sheds light on the pathogenic mechanism and novel treatment strategies. In summary, the current review synthesizes the findings of several studies investigating the effects of shifting bacterial population in retinal diseases, particularly glaucoma, with the aim to identify the current direction of treatment and help direct future endeavors.

Industrial ultrasonic tomography (UT) possesses unique advantages in multiphase medium imaging and has received broad attention. In this article, a novel transmission/reflection dual-mode image reconstruction algorithm based on information fusion is proposed. The transmissive attenuation and reflective time-delay information are both integrated into an improved Lagrange framework with the weighted least square transformation of objective function, which is then solved by a pair of coupled preconditioned gradient approaches. Experiment results show that the proposed algorithm performs better than existing image fusion strategies in terms of accuracy (average relative error 0.456, average correlation coefficient 0.870) and robustness (average standard derivation of relative error and correlation coefficient 0.056 and 0.041). Accordingly, the dual-mode UT approach is proved feasible to provide more accurate image of biphasic medium distribution.

Shenyang in Liaoning Province, is an important industrial city in China, contributing to the development of the economy. Recently, Shenyang has experienced degradation of forests and grasslands, and water shortages leading to ecological dysfunction of urban water bodies, which in turn influence floods. This study combined remote sensing (RS) and geographic information system (GIS) technologies to analyze land use change trends in Shenyang from 2005 to 2018. Then, the HEC–HMS hydrological model was applied to forecast flood characteristics parameters, such as flood peak flow and peak present time for multiple floods in the basin, taking the Xiushui river basin as an example, to provide values for achieving advance flood forecasting. The results showed that (1) all land categories in Shenyang had interconverted to different degrees; (2) the spatial and temporal distribution of annual precipitation in Shenyang varied significantly, showing an overall increasing and decreasing trend in the southeast and northwest, respectively; (3) the NASH coefficients of eight flood simulations ranged from 0.704 to 0.992 and the simulation accuracy has reached Grade B, indicating that the HEC–HMS model had good simulation results in the Xiushui river basin, which could guide advance flood forecasting in this area.

Plastics–microorganism interactions have aroused growing environmental and ecological concerns. However, previous studies concentrated mainly on the direct interactions and paid little attention to the ecotoxicology effects of phthalates (PAEs), a common plastic additive that is continuously released and accumulates in the environment. Here, we provide insights into the impacts of PAEs on the dissemination of antibiotic resistance genes (ARGs) among environmental microorganisms. Dimethyl phthalate (DMP, a model PAE) at environmentally relevant concentrations (2–50 μg/L) significantly boosted the plasmid-mediated conjugation transfer of ARGs among intrageneric, intergeneric, and wastewater microbiota by up to 3.82, 4.96, and 4.77 times, respectively. The experimental and molecular dynamics simulation results unveil a strong interaction between the DMP molecules and phosphatidylcholine bilayer of the cell membrane, which lowers the membrane lipid fluidity and increases the membrane permeability to favor transfer of ARGs. In addition, the increased reactive oxygen species generation and conjugation-associated gene overexpression under DMP stress also contribute to the increased gene transfer. This study provides fundamental knowledge of the PAE–bacteria interactions to broaden our understanding of the environmental and ecological risks of plastics, especially in niches with colonized microbes, and to guide the control of ARG environmental spreading.

Transition metal carbon/nitride (MXene) holds immense potential as an innovative electrocatalyst for enhancing the overall water splitting properties. Nevertheless, the re-stacking nature induced by van der Waals force remains a significant challenge. In this work, the lattice tensile-strained porous V2C-MXene (named as TS(24)-P(50)-V2C) is successfully constructed via the rapid spray freezing method and the following hydrothermal treatment. Besides, the influence of lattice strain degree and microscopic pores on the catalytic ability is reviewed and explored systematically. The lattice tensile strain within V2C-MXene could widen the interlayer spacing and accelerate the ion transfer. The microscopic pores could change the ion transmission path and shorten the migration distance. As a consequence, the obtained TS(24)-P(50)-V2C shows extraordinary hydrogen evolution reaction and oxygen evolution reaction activity with the overpotential of 154 and 269 mV, respectively, at the current density of 10 mA/cm2, which is quite remarkable compared to the MXene-based electrocatalysts. Moreover, the overall water splitting device assembled using TS(24)-P(50)-V2C as both anode and cathode demonstrates a low cell voltage requirement of 1.57 V to obtain 10 mA/cm2. Overall, the implementation of this work could offer an exciting avenue to overcome the re-stacking issue of V2C-MXene, affording a high-efficiency electrocatalyst with superior catalytic activity and desirable reaction kinetics.

C24H20Co2I2N4O12, monoclinic, P21/c(no. 14), a= 10.5649(3) Å, b= 18.6071(6) Å, c= 7.4403(2) Å, β= 92.388(2)°, Z= 4, V= 1461.36(7) Å3, Rgt(F) = 0.032, wRref(F2) = 0.078, T= 293 K.

Phenylketonuria (PKU), a disease resulting in the disability to degrade phenylalanine (Phe) is an inborn error with a 1 in 10,000 morbidity rate on average around the world which leads to neurotoxicity. As an potential alternative to a protein-restricted diet, oral intake of engineered probiotics degrading Phe inside the body is a promising treatment, currently at clinical stage II (Isabella, et al., 2018). However, limited transmembrane transport of Phe is a bottleneck to further improvement of the probiotic’s activity. Here, we achieved simultaneous degradation of Phe both intracellularly and extracellularly by expressing genes encoding the Phe-metabolizing enzyme phenylalanine ammonia lyase (PAL) as an intracellularly free and a cell surface-immobilized enzyme in Escherichia coliNissle 1917 (EcN) which overcomes the transportation problem. The metabolic engineering strategy was also combined with strengthening of Phe transportation, transportation of PAL-catalyzed trans-cinnamic acid and fixation of released ammonia. Administration of our final synthetic strain TYS8500 with PAL both displayed on the cell surface and expressed inside the cell to the PahF263SPKU mouse model reduced blood Phe concentration by 44.4% compared to the control EcN, independent of dietary protein intake. TYS8500 shows great potential in future applications for PKU therapy.

Pressure ulcers are chronic skin wounds with a particularly high morbidity and a relatively low recovery rate. At present, there is still a lack of a portable and visual real-time monitoring system for early pressure ulcers. Electrical impedance tomography (EIT) is a kind of functional imaging technique that can diagnose the healthy condition in the measured tissues by visualizing the distribution of bioelectrical impedance parameters. According to the principle of electrical characteristic of pressure ulcers, i.e., changes in physiological and pathological information of tissues caused by pressure lead to changes in electrical impedance of tissues, a noninvasive imaging system of pressure ulcers based on EIT with novel flexible electrode array is proposed. Two kinds of 3-D finite element simulation models are constructed to discuss the measurement effects of the flexible array on simulated pressure ulcers at different locations, depths, and with different sizes. The effects of different imaging algorithms on imaging results are also compared and analyzed. Physical phantom model experiments and preliminary experiments on human skin are conducted. Both simulations and physical experiments indicate that EIT technique is excepted to realize a safer, portable, low-cost, and visual real-time monitoring system for early pressure ulcers.

Electrical capacitance tomography (ECT) and electrical resistance tomography (ERT) are promising techniques for multiphase flow imaging due to high-speed, low-cost, noninvasive, and visualization features. At present, ECT/ERT dual-mode imaging is generally accomplished by the combination of ECT and ERT sensors implemented through different electrode arrays, and with separate systems to collect the projection data, due to their different sensing principles. To expand different sensing ranges of ECT and ERT, a dual-mode tomography over a wide electrical sensing frequency band based on dual-frequency response is proposed in this article. A dual-frequency excitation/probing scheme is used to measure the capacitance and conductance of the fluid through one set of electrode arrays, to visualize the permittivity and conductivity distribution in the same pipe cross section. The frequency responses of different combinations of conductive, weakly conductive, and nonconductive substances were tested. Static and dynamic experimental tests were carried out. The experimental results in ECT and ERT are satisfactory, demonstrating the feasibility of the proposed ECT/ERT dual-mode technique.

Particle size is an essential parameter for industrial processes. The ultrasound method can acquire the particle size distribution (PSD) with multifrequency attenuation characteristics. However, the acoustic path in the traditional measurement mode with a pair of transducers cannot cover the whole measurement zone, and the heterogeneous distribution affects the measurement results. Hence, an ultrasound array strategy with multiple acoustic paths is proposed to eliminate the disadvantage. A hybrid ultrasound attenuation model combining McClements and Bouguer–Lambert–Beer Law (BLBL) is adopted to describe the different attenuation mechanisms. The PSD is obtained with an improved particle swarm optimization (PSO) algorithm, and the efficiency of the proposed PSD inversion algorithm is verified by the simulations. A liquid–solid two-phase measurement device is built for the experimental investigation. The PSD measurements show agreement with the optical measurement results, and the measurement accuracy using the ultrasound array method is improved compared to the traditional one-pair transducer measurement. Combining the PSD measurements of different acoustic paths, the particle distribution of the measurement zone is characterized, and the flow behaviors of the mixing process can be further captured.

In recent decades, process tomography (PT) has received widespread attentions in industrial multiphase flow monitoring. Traditional single-modality tomography is usually sensitive to a specific physical parameter and is widely used in two-phase flow monitoring. However, when the fluids are more than two phases, such as oil/gas/water three-phase flow, the complexity of existence forms of three fluids brings a challenge to single-modality tomography. Multimodality tomography can obtain more information, possessing the unique advantages in three-phase flow monitoring. In this article, a combinable multimodality tomography system based on industrial compact peripheral component interconnect (CPCI) bus is designed. The system includes electrical resistance tomography (ERT), electrical capacitance tomography (ECT), and ultrasonic transmission tomography (UTT), supporting independent work or free combination of different modalities for different measurement requirements. The data acquisition and transmission are controlled by the field programmable gate array (FPGA), which can synchronize the asynchronous clock signals of different boards through flexible programming. Interboard communication based on CPCI bus supports the coordination and synchronization of multimodal boards, and also facilitates the expansion of system functions in the future. Static experimental results show that the designed multimodal system can coordinate multimodal data acquisition timing, and exhibit better performance in acquisition speed, signal-to-noise ratio (SNR), and image reconstruction.