Adaptive fractional-order nonsingular terminal sliding mode control and sequential quadratic programming torque distribution for lateral stability of FWID-EVs with actuator constraints

This paper proposes a novel sliding mode control (SMC) algorithm for direct yaw moment control of four-wheel independent drive electric vehicles (FWID-EVs). The algorithm integrates adaptive law theory, fractional-order theory, and nonsingular terminal sliding mode reaching law theory to reduce chattering, handle uncertainty, and avoid singularities in the SMC system. A sequential quadratic programming (SQP) method is also proposed to optimize the yaw moment distribution under actuator constraints. The performance of the proposed algorithm is evaluated by a hardware-in-the-loop test with two driving maneuvers and compared with two existing SMC-based schemes together with the cases with the change of vehicle parameters and disturbances. The results demonstrate that the proposed algorithm can eliminate chattering and achieve the best lateral stability as compared with the existing schemes.

A Gated Recurrent Generative Transfer Learning Network for Fault Diagnostics Considering Imbalanced Data and Variable Working Conditions

Transfer learning (TL) and generative adversarial networks (GANs) have been widely applied to intelligent fault diagnosis under imbalanced data and different working conditions. However, the existing data synthesis methods focus on the overall distribution alignment between the generated data and real data, and ignore the fault-sensitive features in the time domain, which results in losing convincing temporal information for the generated signal. For this reason, a novel gated recurrent generative TL network (GRGTLN) is proposed. First, a smooth conditional matrix-based gated recurrent generator is proposed to extend the imbalanced dataset. It can adaptively increase the attention of fault-sensitive features in the generated sequence. Wasserstein distance (WD) is introduced to enhance the construction of mapping relationships to promote data generation ability and transfer performance of the fault diagnosis model. Then, an iterative "generation-transfer" co-training strategy is developed for continuous parallel training of the model and the parameter optimization. Finally, comprehensive case studies demonstrate that GRGTLN can generate high-quality data and achieve satisfactory cross-domain diagnosis accuracy.

A sticky-end probe biosensor for homogeneous detection of transcription factor binding activity

Transcription factors are essential regulators of various physiological and pathological processes. However, detecting transcription factor-DNA binding activities is often time-consuming and labor-intensive. Homogeneous biosensors that are compatible with mix-and-measure protocols have the potential to simplify the workflow for therapeutic screening and disease diagnostics. In this study, we apply a combined computational-experimental approach to investigate the design of a sticky-end probe biosensor, where the transcription factor-DNA complex stabilizes the fluorescence resonance energy transfer signal of the donor-acceptor pair. We design a sticky-end biosensor for the SOX9 transcription factor based on the consensus sequence and characterize its sensing performance. A systems biology model is also developed to investigate the reaction kinetics and optimize the operating conditions. Taken together, our study provides a conceptual framework for the design and optimization of sticky-end probe biosensors for homogeneous detection of transcription factor-DNA binding activity.

Physical properties and structural characteristics of particulate matter emitted from a diesel engine fueled with biodiesel blends

This research explores the influence of renewable fuels, including three kinds of biodiesel along with ethanol on the physical properties and structural characteristics of particulate matter (PM) emitted from a diesel engine in comparison with pure diesel. After adding 10 vol% of grape seed biodiesel, coffee biodiesel and eucalyptus oil into diesel, three biodiesel blended fuels (10% grape seed biodiesel (DGs10), 10% spent coffee ground biodiesel (DC10) and eucalyptus oil biodiesel (DEu10)) were produced and tested in this study. Besides, one ethanol blend containing 9 vol% of ethanol and 1 vol% of biodiesel (blend stabilizer) was also tested to do the comparison. In the present study, scanning transmission electron microscope (STEM) and scanning electron microscope (SEM) were employed for analyzing the microstructure, nanostructure and electron diffraction pattern of PM. Raman spectrometer (RS) was also used for the analysis of structural characterization of PM. In addition, several experimental instruments like microbalance, measuring cup, viscometer, oxygen bomb calorimeter and Gas Chromatography-Mass Spectrometer (GC-MS) were employed to detect the fuel properties, including density, heating value, viscosity, composition and cetane number. A conclusion can be drawn that both biodiesel blends and ethanol blend have a changing effect on the PM properties compared to pure diesel, where biodiesel blends have a slightly weaker influence than ethanol blend. Regarding the biodiesel blends, DGs10 has more impact than DC10 and DEu10 in changes of PM properties, particularly in the reduction of PM mass, making it a good candidate for renewable fuel for diesel engines.

Semantic Segmentation of Gastric Polyps in Endoscopic Images Based on Convolutional Neural Networks and an Integrated Evaluation Approach

Convolutional neural networks (CNNs) have received increased attention in endoscopic images due to their outstanding advantages. Clinically, some gastric polyps are related to gastric cancer, and accurate identification and timely removal are critical. CNN-based semantic segmentation can delineate each polyp region precisely, which is beneficial to endoscopists in the diagnosis and treatment of gastric polyps. At present, just a few studies have used CNN to automatically diagnose gastric polyps, and studies on their semantic segmentation are lacking. Therefore, we contribute pioneering research on gastric polyp segmentation in endoscopic images based on CNN. Seven classical semantic segmentation models, including U-Net, UNet++, DeepLabv3, DeepLabv3+, Pyramid Attention Network (PAN), LinkNet, and Muti-scale Attention Net (MA-Net), with the encoders of ResNet50, MobineNetV2, or EfficientNet-B1, are constructed and compared based on the collected dataset. The integrated evaluation approach to ascertaining the optimal CNN model combining both subjective considerations and objective information is proposed since the selection from several CNN models is difficult in a complex problem with conflicting multiple criteria. UNet++ with the MobineNet v2 encoder obtains the best scores in the proposed integrated evaluation method and is selected to build the automated polyp-segmentation system. This study discovered that the semantic segmentation model has a high clinical value in the diagnosis of gastric polyps, and the integrated evaluation approach can provide an impartial and objective tool for the selection of numerous models. Our study can further advance the development of endoscopic gastrointestinal disease identification techniques, and the proposed evaluation technique has implications for mathematical model-based selection methods for clinical technologies.

Long noncoding RNA MALAT1 is dynamically regulated in leader cells during collective cancer invasion

Cancer cells collectively invade using a leader-follower organization, but the regulation of leader cells during this dynamic process is poorly understood. Using a dual double-stranded locked nucleic acid (LNA) nanobiosensor that tracks long noncoding RNA (lncRNA) dynamics in live single cells, we monitored the spatiotemporal distribution of lncRNA during collective cancer invasion. We show that the lncRNA MALAT1 (metastasis-associated lung adenocarcinoma transcript 1) is dynamically regulated in the invading fronts of cancer cells and patient-derived spheroids. MALAT1 transcripts exhibit distinct abundance, diffusivity, and distribution between leader and follower cells. MALAT1 expression increases when a cancer cell becomes a leader and decreases when the collective migration process stops. Transient knockdown of MALAT1 prevents the formation of leader cells and abolishes the invasion of cancer cells. Taken together, our single-cell analysis suggests that MALAT1 is dynamically regulated in leader cells during collective cancer invasion.

Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview

Gastrointestinal (GI) cancers are the major cause of cancer-related mortality globally. Medical imaging is an important auxiliary means for the diagnosis, assessment and prognostic prediction of GI cancers. Radiomics is an emerging and effective technology to decipher the encoded information within medical images, and traditional machine learning is the most commonly used tool. Recent advances in deep learning technology have further promoted the development of radiomics. In the field of GI cancer, although there are several surveys on radiomics, there is no specific review on the application of deep-learning-based radiomics (DLR). In this review, a search was conducted on Web of Science, PubMed, and Google Scholar with an emphasis on the application of DLR for GI cancers, including esophageal, gastric, liver, pancreatic, and colorectal cancers. Besides, the challenges and recommendations based on the findings of the review are comprehensively analyzed to advance DLR.

Transient nuclear deformation primes epigenetic state and promotes cell reprogramming

Cell reprogramming has wide applications in tissue regeneration, disease modelling and personalized medicine. In addition to biochemical cues, mechanical forces also contribute to the modulation of the epigenetic state and a variety of cell functions through distinct mechanisms that are not fully understood. Here we show that millisecond deformation of the cell nucleus caused by confinement into microfluidic channels results in wrinkling and transient disassembly of the nuclear lamina, local detachment of lamina-associated domains in chromatin and a decrease of histone methylation (histone H3 lysine 9 trimethylation) and DNA methylation. These global changes in chromatin at the early stage of cell reprogramming boost the conversion of fibroblasts into neurons and can be partially reproduced by inhibition of histone H3 lysine 9 and DNA methylation. This mechanopriming approach also triggers macrophage reprogramming into neurons and fibroblast conversion into induced pluripotent stem cells, being thus a promising mechanically based epigenetic state modulation method for cell engineering.

Adaptive event-based robust passive fault tolerant control for nonlinear lateral stability of autonomous electric vehicles with asynchronous constraints

This work solves the robust passive fault-tolerant control problem for autonomous electric vehicles based on an adaptive event triggered mechanism. Firstly, given the system uncertainties from the tire dynamics and the longitudinal speed, the T-S fuzzy model method is used to approximate the vehicle lateral dynamics. Secondly, taking the communication constraints caused by band-limited networks into account, an adaptive event-triggered scheme is introduced in the process of the control design. Moreover, the asynchronous constraint of the weight function between the controller and system is considered. Thirdly, considering that the actuator faults are inevitably encountered in the control system, a robust passive fault-tolerant control method is proposed to improve vehicle performances. Finally, simulations are carried out to illustrate the effectiveness and robustness of the proposed approach.

Physical, chemical, and cell toxicity properties of mature/aged particulate matter (PM) trapped in a diesel particulate filter (DPF) along with the results from freshly produced PM of a diesel engine

The lifetime and efficiency of diesel particulate filters (DPFs) strongly depend on the proper and periodic cleaning and servicing. Unfortunately, in some cases, inappropriate methods are applied to clean the DPFs, e.g., using air compressors without proper disposal procedures which can have negative impacts on human health, the environment, and DPF's efficiency. However, there is no information available about the properties of this kind of PM. This research is therefore presented to explore the physicochemical and toxicity properties of aged PM trapped in a DPF (using compressed air for PM sampling) employing STEM, SEM, EDS, Organic Carbon Analyzer, TGA/DSC, and Raman Spectrometer for investigating the physicochemical properties, and assays of cell viability, cellular reactive oxygen species (ROS), interleukin-6, and tumor necrosis factor-alpha (TNF-α) for investigating the toxicity properties. Also, analyses from fresh PM samples from the diesel vehicle at two engine speeds are presented. It is found that at a certain/fixed PM number/mass for all three samples tested, the PM from DPF compared with the fresh PM can have both positive (particularly having the lowest water-soluble total carbon ratio) and negative impacts on human health (particularly having the highest cell death rate of 13.4%, ROS, and TNF-α) and the environment.

A reconfigurable microfluidic building block platform for high-throughput nonhormonal contraceptive screening

Identifying nonhormonal contraceptives will have profound impacts on avoiding side effects of hormonal birth control methods, minimizing pregnancy complications and infant mortality rates, and promoting family planning. However, phenotypic screening of contraceptives is challenging due to the diverse procedures associated with oocyte culture, biochemical assays, and molecular imaging. This study reports a multifunctional microfluidic platform comprising reconfigurable building blocks and interfaces to implement various cell-based drug screening protocols. This versatile platform has three major layers. The top layer consists of interchangeable 3D microfluidic building blocks (e.g., branching microchannels, chemical gradient generators, pumpless flow controllers, and emulsion generators) or an open interface. The middle layer incorporates a multiwell array with embedded membrane filters for live cell culture, medium exchange, enzymatic cumulus cell removal, washing, and fluorescence staining. The bottom layer is also reconfigurable for waste collection, oocyte culture, plate reader measurement, and high-resolution microscopy. We demonstrate an 8 by 16 (128 wells) system for performing the cumulus-oocyte complex (COC) expansion and oocyte maturation assays for screening nonhormonal contraceptives. The microfluidic building block platform is scalable and can be reconfigured for a variety of drug screening applications in the future.

Physicochemical and cell toxicity properties of particulate matter (PM) from a diesel vehicle fueled with diesel, spent coffee ground biodiesel, and ethanol

The literature shows that information about the physical, chemical, and cell toxicity properties of particulate matter (PM) from diesel vehicles is not rich as the existence of a remarkable number of studies about the combustion, performance, and emissions of diesel vehicles using renewable liquid fuels, particularly biodiesels and alcohols. Also, the PM analyses from combustion of spent coffee ground biodiesel have not been comprehensively explored. Therefore, this research is presented. Pure diesel, 90% diesel + 10% biodiesel, and 90% diesel + 9% ethanol + 1% biodiesel, volume bases, were tested under a fast idle condition. STEM, SEM, EDS, Organic Carbon Analyzer, TGA/DSC, and Raman Spectrometer were employed for investigating the PM physical and chemical properties, and assays of cell viability, cellular reactive oxygen species, interleukin-6, and tumor necrosis factor-alpha were examined for investigating the PM cell toxicity properties. It is found that the application of both biodiesel and ethanol has the potential to change the PM properties, while the impact of ethanol is more than biodiesel on the changes. Regarding the important aspects, biodiesel can be effective for better human health (due to a decrease in cell death (-60.8%)) as well as good diesel particulate filter efficiency (due to lower activation energy (-7.6%) and frequency factor (-83.2%)). However, despite a higher impact of ethanol on the reductions in activation energy (-24.8%) and frequency factor (-99.0%), this fuel causes an increase in cell death (84.1%). Therefore, biodiesel can be an appropriate fuel to have a positive impact on human health, the environment, and emissions catalysts performance, simultaneously.

Fuzzy KNN Method With Adaptive Nearest Neighbors

Due to its strong performance in handling uncertain and ambiguous data, the fuzzy k -nearest-neighbor method (FKNN) has realized substantial success in a wide variety of applications. However, its classification performance would be heavily deteriorated if the number k of nearest neighbors was unsuitably fixed for each testing sample. This study examines the feasibility of using only one fixed k value for FKNN on each testing sample. A novel FKNN-based classification method, namely, fuzzy KNN method with adaptive nearest neighbors (A-FKNN), is devised for learning a distinct optimal k value for each testing sample. In the training stage, after applying a sparse representation method on all training samples for reconstruction, A-FKNN learns the optimal k value for each training sample and builds a decision tree (namely, A-FKNN tree) from all training samples with new labels (the learned optimal k values instead of the original labels), in which each leaf node stores the corresponding optimal k value. In the testing stage, A-FKNN identifies the optimal k value for each testing sample by searching the A-FKNN tree and runs FKNN with the optimal k value for each testing sample. Moreover, a fast version of A-FKNN, namely, FA-FKNN, is designed by building the FA-FKNN decision tree, which stores the optimal k value with only a subset of training samples in each leaf node. Experimental results on 32 UCI datasets demonstrate that both A-FKNN and FA-FKNN outperform the compared methods in terms of classification accuracy, and FA-FKNN has a shorter running time.

Nrf2 Modulates the Hybrid Epithelial/Mesenchymal Phenotype and Notch Signaling During Collective Cancer Migration

Hybrid epithelial/mesenchymal cells (E/M) are key players in aggressive cancer metastasis. It remains a challenge to understand how these cell states, which are mostly non-existent in healthy tissue, become stable phenotypes participating in collective cancer migration. The transcription factor Nrf2, which is associated with tumor progression and resistance to therapy, appears to be central to this process. Here, using a combination of immunocytochemistry, single cell biosensors, and computational modeling, we show that Nrf2 functions as a phenotypic stability factor for hybrid E/M cells by inhibiting a complete epithelial-mesenchymal transition (EMT) during collective cancer migration. We also demonstrate that Nrf2 and EMT signaling are spatially coordinated near the leading edge. In particular, computational analysis of an Nrf2-EMT-Notch network and experimental modulation of Nrf2 by pharmacological treatment or CRISPR/Cas9 gene editing reveal that Nrf2 stabilizes a hybrid E/M phenotype which is maximally observed in the interior region immediately behind the leading edge. We further demonstrate that the Nrf2-EMT-Notch network enhances Dll4 and Jagged1 expression at the leading edge, which correlates with the formation of leader cells and protruding tips. Altogether, our results provide direct evidence that Nrf2 acts as a phenotypic stability factor in restricting complete EMT and plays an important role in coordinating collective cancer migration.

Improved AET Robust Control for Networked T-S Fuzzy Systems With Asynchronous Constraints

This article proposes a novel improved adaptive event-triggered (AET) control algorithm for networked Takagi-Sugeno (T-S) fuzzy systems with asynchronous constraints. First, taking the limited bandwidth of the network into consideration, an improved AET mechanism is proposed to save the communication resource. Superior to the existing event-triggered mechanism, the improved AET scheme introduces two adjusting parameters, which further contribute to the economization of the communication resource. Second, with consideration of asynchronous premise variables, a reconstructed approach is applied to synchronize the time scales of membership functions of the fuzzy system and the fuzzy controller. Third, to derive a less conservative sufficient condition for the controller design, a new augmented Lyapunov-Krasovskii functional with event-triggered information and triple integral terms is constructed. Meanwhile, by applying a Bessel-Legendre inequality and extended reciprocally convex matrix inequality together, a new control algorithm is derived with less conservatism. Finally, simulations on a cart-damper-spring system are implemented to evaluate and verify the performance and advantages of the proposed algorithm.

Optimizing locked nucleic acid modification in double-stranded biosensors for live single cell analysis

Double-stranded (ds) biosensors are homogeneous oligonucleotide probes for detection of nucleic acid sequences in biochemical assays and live cell imaging.

Automatic detection of multiple types of pneumonia: Open dataset and a multi-scale attention network

The quick and precise identification of COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, mycoplasma pneumonia, and normal lung on chest CT images play a crucial role in timely quarantine and medical treatment. However, manual identification is subject to potential misinterpretations and time-consumption issues owing the visual similarities of pneumonia lesions. In this study, we propose a novel multi-scale attention network (MSANet) based on a bag of advanced deep learning techniques for the automatic classification of COVID-19 and multiple types of pneumonia. The proposed method can automatically pay attention to discriminative information and multi-scale features of pneumonia lesions for better classification. The experimental results show that the proposed MSANet can achieve an overall precision of 97.31%, recall of 96.18%, F1-score of 96.71%, accuracy of 97.46%, and macro-average area under the receiver operating characteristic curve (AUC) of 0.9981 to distinguish between multiple classes of pneumonia. These promising results indicate that the proposed method can significantly assist physicians and radiologists in medical diagnosis. The dataset is publicly available at https://doi.org/10.17632/rf8x3wp6ss.1.

A transparent low intensity pulsed ultrasound (LIPUS) chip for high-throughput cell stimulation

We report an on-chip platform for low-intensity pulsed ultrasound (LIPUS) stimulation of cells directly cultured on a biocompatible surface of a transparent ultrasound transducer (TUT) fabricated using lithium niobate. The high light transmittance (>80%) and compact size (3 mm × 3 mm × 2 mm) of TUTs allowed easy integration with powerful optical microscopy techniques with no additional acoustic coupling and risk for contamination. TUTs were excited with varying acoustic excitation parameters (voltage amplitude and duty cycle) and resulting live cell calcium signaling was simultaneously imaged using time-lapse confocal microscopy, while the temperature change was measured by a thermocouple. Quantitative single-cell fluorescence analysis revealed the dynamic calcium signaling responses and together with the temperature measurements elucidated the optimal stimulation parameters for non-thermal and thermal effects. The fluorescence change profile was distinct from the recorded temperature change (<1 degree Celsius) profile under LIPUS treatment conditions. Cell dead assay results confirmed cells remain viable after the LIPUS treatment. These results confirmed that the TUT platform enables controllable, safe, high-throughput, and uniform mechanical stimulation of all plated cells. The on-chip LIPUS stimulation using TUTs has the potential to attract several in vitro and in vivo biomedical applications such as controlling stem cell differentiation and proliferation, studying biomechanical properties of cancer cells, and gaining fundamental insights into mechanotransduction pathways when integrated with state-of-the-art high-speed and high-resolution microscopy techniques.

Intratumoral Heterogeneity Promotes Collective Cancer Invasion through NOTCH1 Variation

Cellular and molecular heterogeneity within tumors has long been associated with the progression of cancer to an aggressive phenotype and a poor prognosis. However, how such intratumoral heterogeneity contributes to the invasiveness of cancer is largely unknown. Here, using a tumor bioengineering approach, we investigate the interaction between molecular subtypes within bladder microtumors and the corresponding effects on their invasiveness. Our results reveal heterogeneous microtumors formed by multiple molecular subtypes possess enhanced invasiveness compared to individual cells, even when both cells are not invasive individually. To examine the molecular mechanism of intratumoral heterogeneity mediated invasiveness, live single cell biosensing, RNA interference, and CRISPR-Cas9 gene editing approaches were applied to investigate and control the composition of the microtumors. An agent-based computational model was also developed to evaluate the influence of NOTCH1 variation on DLL4 expression within a microtumor. The data indicate that intratumoral variation in NOTCH1 expression can lead to upregulation of DLL4 expression within the microtumor and enhancement of microtumor invasiveness. Overall, our results reveal a novel mechanism of heterogeneity mediated invasiveness through intratumoral variation of gene expression.

Decoding leader cells in collective cancer invasion

Collective cancer invasion with leader-follower organization is increasingly recognized as a predominant mechanism in the metastatic cascade. Leader cells support cancer invasion by creating invasion tracks, sensing environmental cues and coordinating with follower cells biochemically and biomechanically. With the latest developments in experimental and computational models and analysis techniques, the range of specific traits and features of leader cells reported in the literature is rapidly expanding. Yet, despite their importance, there is no consensus on how leader cells arise or their essential characteristics. In this Perspective, we propose a framework for defining the essential aspects of leader cells and provide a unifying perspective on the varying cellular and molecular programmes that are adopted by each leader cell subtype to accomplish their functions. This Perspective can lead to more effective strategies to interdict a major contributor to metastatic capability.

Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A review

Upper gastrointestinal (GI) cancers are the leading cause of cancer-related deaths worldwide. Early identification of precancerous lesions has been shown to minimize the incidence of GI cancers and substantiate the vital role of screening endoscopy. However, unlike GI cancers, precancerous lesions in the upper GI tract can be subtle and difficult to detect. Artificial intelligence techniques, especially deep learning algorithms with convolutional neural networks, might help endoscopists identify the precancerous lesions and reduce interobserver variability. In this review, a systematic literature search was undertaken of the Web of Science, PubMed, Cochrane Library and Embase, with an emphasis on the deep learning-based diagnosis of precancerous lesions in the upper GI tract. The status of deep learning algorithms in upper GI precancerous lesions has been systematically summarized. The challenges and recommendations targeting this field are comprehensively analyzed for future research.

Digital electrical impedance analysis for single bacterium sensing and antimicrobial susceptibility testing

Single-molecule and single-cell analysis techniques have opened new opportunities for characterizing and analyzing heterogeneity within biological samples. These detection methods are often referred to as digital assays because the biological sample is partitioned into many small compartments and each compartment contains a discrete number of targets (e.g. cells). Using digital assays, researchers can precisely detect and quantify individual targets, and this capability has made digital techniques the basis for many modern bioanalytical tools (including digital PCR, single cell RNA sequencing, and digital ELISA). However, digital assays are dominated by optical analysis systems that typically utilize microscopy to analyze partitioned samples. The utility of digital assays may be dramatically enhanced by implementing cost-efficient and portable electrical detection capabilities. Herein, we describe a digital electrical impedance sensing platform that enables direct multiplexed measurement of single cell bacterial cells. We outline our solutions to the challenge of multiplexing impedance sensing across many culture compartments and demonstrate the potential for rapidly differentiating antimicrobial resistant versus susceptible strains of bacteria.

Nonlinear Ride Height Control of Active Air Suspension System with Output Constraints and Time-Varying Disturbances

This paper addresses the problem of nonlinear height tracking control of an automobile active air suspension with the output state constraints and time-varying disturbances. The proposed control strategy guarantees that the ride height stays within a predefined range, and converges closely to an arbitrarily small neighborhood of the desired height, ensuring uniform ultimate boundedness. The designed nonlinear observer is able to compensate for the time-varying disturbances caused by external random road excitation and perturbations, achieving robust performance. Simulation results obtained from the co-simulation (AMESim-Matlab/Simulink) are given and analyzed, demonstrating the efficiency of the proposed control methodology.

Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans

The COVID-19 pneumonia is a global threat since it emerged in early December 2019. Driven by the desire to develop a computer-aided system for the rapid diagnosis of COVID-19 to assist radiologists and clinicians to combat with this pandemic, we retrospectively collected 206 patients with positive reverse-transcription polymerase chain reaction (RT-PCR) for COVID-19 and their 416 chest computed tomography (CT) scans with abnormal findings from two hospitals, 412 non-COVID-19 pneumonia and their 412 chest CT scans with clear sign of pneumonia are also retrospectively selected from participating hospitals. Based on these CT scans, we design an artificial intelligence (AI) system that uses a multi-scale convolutional neural network (MSCNN) and evaluate its performance at both slice level and scan level. Experimental results show that the proposed AI has promising diagnostic performance in the detection of COVID-19 and differentiating it from other common pneumonia under limited number of training data, which has great potential to assist radiologists and physicians in performing a quick diagnosis and mitigate the heavy workload of them especially when the health system is overloaded. The data is publicly available for further research at https://data.mendeley.com/datasets/3y55vgckg6/1https://data.mendeley.com/datasets/3y55vgckg6/1.

Intelligent diagnosis of gastric intestinal metaplasia based on convolutional neural network and limited number of endoscopic images

BACKGROUND

Gastric intestinal metaplasia (GIM) is a precancerous lesion of gastric cancer. Currently, diagnosis of GIM is based on the experience of a physician, which is liable to interobserver variability. Thus, an intelligent diagnostic (ID) system, based on narrow-band and magnifying narrow-band images, was constructed to provide objective assistance in the diagnosis of GIM.

METHOD

We retrospectively collected 1880 endoscopic images (1048 GIM and 832 non-GIM) via biopsy from 336 patients confirmed histologically as GIM or non-GIM, from the Kiang Wu Hospital, Macau. We developed an ID system with these images using a modified convolutional neural network algorithm. A separate test dataset containing 477 pathologically confirmed images (242 GIM and 235 non-GIM) from 80 patients was used to test the performance of the ID system. Experienced endoscopists also examined the same test dataset, for comparison with the ID system. One of the challenges faced in this study was that it was difficult to obtain a large number of training images. Thus, data augmentation and transfer learning were applied together.

RESULTS

The area under the receiver operating characteristic curve was 0.928 for the pre-patient analysis of the ID system, while the sensitivities, specificities, and accuracies of the ID system against those of the human experts were (91.9% vs. 86.5%, p-value = 1.000) (86.0% vs. 81.4%, p-value = 0.754), and (88.8% vs. 83.8%, p-value = 0.424), respectively. Even though the three indices of the ID system were slightly higher than those of the human experts, there were no significant differences.

CONCLUSIONS

In this pilot study, a novel ID system was developed to diagnose GIM. This system exhibits promising diagnostic performance. It is believed that the proposed system has the potential for clinical application in the future.

Advances in Viral Diagnostic Technologies for Combating COVID-19 and Future Pandemics

The emergence of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) threatens the health of the global population and challenges our preparedness for pandemic threats. Previous outbreaks of coronaviruses and other viruses have suggested the importance of diagnostic technologies in fighting viral outbreaks. Nucleic acid detection techniques are the gold standard for detecting SARS-CoV-2. Viral antigen tests and serological tests that detect host antibodies have also been developed for studying the epidemiology of COVID-19 and estimating the population that may have immunity to SARS-CoV-2. Nevertheless, the availability, cost, and performance of existing viral diagnostic technologies limit their practicality, and novel approaches are required for improving our readiness for global pandemics. Here, we review the principles and limitations of major viral diagnostic technologies and highlight recent advances of molecular assays for COVID-19. In addition, we discuss emerging technologies, such as clustered regularly interspaced short palindromic repeats (CRISPR) systems, high-throughput sequencing, and single-cell and single-molecule analysis, for improving our ability to understand, trace, and contain viral outbreaks. The prospects of viral diagnostic technologies for combating future pandemic threats are presented.

Three-Dimensional Microtumors for Probing Heterogeneity of Invasive Bladder Cancer

Bladder cancer is an increasingly common malignancy, and muscle invasive bladder cancer is associated with particularly high rates of morbidity and mortality. The morphologic and molecular diversity of bladder cancer poses significant challenges in elucidating the invasion mechanisms responsible for disease progression. Furthermore, conventional invasion assays do not provide a physiological context for studying bladder cancer invasion within 3D microenvironments and have limited ability to capture the contribution of cellular phenotypic heterogeneity to disease progression. Here, we describe the development of a 3D microtumor invasion model suitable for the analysis of cellular phenotypic heterogeneity in cell lines and primary tumor cells from bladder cancer patients. This model incorporates a self-assembly approach for recapitulating features of bladder cancer invasion in 3D microenvironments and probing the invasive cell subpopulations. The gene expression profiles of invading microtumors were analyzed by incorporating a gold nanorod-locked nucleic acid biosensor. The incorporation of the single cell biosensor and transient gene knockdown into the system revealed the formation of invasive leader cells with upregulated Delta-like ligand 4 (DLL4) expression as well as the role of NOTCH1-DLL4 signaling in collective bladder cancer invasion. The involvement of DLL4 expressing cells in bladder cancer invasion was also observed in patient samples obtained from transurethral resection. Collectively, our study demonstrates a 3D microtumor invasion model for investigating intracellular heterogeneity of bladder cancer invasion and analyzing patient derived samples toward personalized medicine applications.

Non-polar organic compounds, volatility and oxidation reactivity of particulate matter emitted from diesel engine fueled with ternary fuels in blended and fumigation modes

The present experimental study aims to examine the impacts of various fueling modes of operation on the particle-phase polycyclic aromatic hydrocarbons (PAHs) and n-alkanes (C16-C30), and volatility and oxidation reactivity of particulate matter (PM) emitted from a diesel engine fueled with a ternary fuel (80% diesel, 5% biodiesel and 15% ethanol (D80B5E15, volume %)) under four engine operating conditions. Four fueling modes, including diesel, blended, fumigation and combined fumigation + blended (F + B) modes were tested using pure diesel fuel for diesel mode and a constant fuel content of D80B5E15 for the blended, fumigation and F + B modes to create the same condition for comparing their impacts on the parameters investigated. The average results illustrate that both blended and fumigation modes can reduce the PAHs (-78.4% and -31.3%), benzo[a]pyrene equivalent (-81.7% and -38.9%), n-alkanes (-46.5% and -21.5%) and non-volatile substance fraction (-25.1% and -11.1%), but increase the high-volatile substance fraction (12.8% and 6.9%) and oxidation reactivity rate (34.0% and 4.9%), respectively compared to those of the diesel mode. While the effect of the blended mode on the parameters investigated is stronger than the fumigation mode. And the F + B mode has the effects in between the results of the blended and fumigation modes.

SLIPS-LAB-A bioinspired bioanalysis system for metabolic evaluation of urinary stone disease

Urinary stone disease is among the most common medical conditions. Standard evaluation of urinary stone disease involves a metabolic workup of stone formers based on measurement of minerals and solutes excreted in 24-hour urine samples. Nevertheless, 24-hour urine testing is slow, expensive, and inconvenient for patients, which has hindered widespread adoption in clinical practice. Here, we demonstrate SLIPS-LAB (Slippery Liquid-Infused Porous Surface Laboratory), a droplet-based bioanalysis system, for rapid measurement of urinary stone-associated analytes. The ultra-repellent and antifouling properties of SLIPS, which is a biologically inspired surface technology, allow autonomous liquid handling and manipulation of physiological samples without complicated sample preparation procedures and supporting equipment. We pilot a study that examines key urinary analytes in clinical samples from patients with urinary stone. The simplicity and speed of SLIPS-LAB hold the potential to provide actionable diagnostic information for patients with urinary stone disease and rapid feedback for responses to dietary and pharmacologic treatments.

Rapid Single-Cell Microbiological Analysis: Toward Precision Management of Infections and Dysbiosis

Bacterial infection is a leading cause of morbidity and mortality (from infants to the elderly) and accounts for more than $20 billion in healthcare costs in the United States each year. The pathogens responsible for many of the common infectious diseases, such as urinary tract infection (UTI) and ventilator-associated infections (VAIs), have proven to be highly adept in acquiring mechanisms of antimicrobial resistance. The use of broad-spectrum antibiotics by healthcare providers and the infiltration of antibiotics in the environment have accelerated the selection and growth of resistant pathogens. To further exacerbate the problem, the need for new antibiotics has far outpaced the development of new classes of antibiotics by the pharmaceutical industry (only two new classes of antibiotics have reached the market in the last 20 years), in large part due to prohibitive cost and historically poor return on investment to develop new antibiotics. Consequently, clinicians have limited treatment options, particularly in the neediest patients. To tackle this major global health issue, we are developing novel technological approaches for rapid definitive clinical microbiological analysis. These technologies will improve the clinical management of bacterial infections and reduce the improper use of antibiotics in current practice, hopefully limiting the spread of drug-resistant organisms.

A Multiwell Microfluidic Device for Analyzing and Screening Nonhormonal Contraceptive Agents

Birth control and family planning play pivotal roles in the economic growth and reduction of maternal, infant, and child mortality. Current contraceptives, such as hormonal agents and intrauterine devices, target only a small subset of reproductive processes and can have serious side effects on the health of women. To develop novel contraceptive agents, a scalable microfluidic device is established for analyzing and screening the effects of potential contraceptive agents on the maturation of the cumulus-oocyte complex. The microfluidic device performs on-chip incubation for studying oocyte maturation and cumulus expansion and isolates the microwells by oil-water interfaces to avoid crosstalk between the wells. A filter membrane is incorporated in the device to simplify incubation, medium exchange, washing, and fluorescence staining of oocytes. Cumulus expansion can be monitored directly in the device and oocyte maturation can be examined after enzymatic removal of cumulus cells and on-chip fluorescence staining. The performance of the device is evaluated by studying the influence of three drugs known to block oocyte maturation and/or cumulus expansion.

NRF2 activates a partial epithelial-mesenchymal transition and is maximally present in a hybrid epithelial/mesenchymal phenotype

The epithelial-mesenchymal transition (EMT) is a key process implicated in cancer metastasis and therapy resistance. Recent studies have emphasized that cells can undergo partial EMT to attain a hybrid epithelial/mesenchymal (E/M) phenotype - a cornerstone of tumour aggressiveness and poor prognosis. These cells can have enhanced tumour-initiation potential as compared to purely epithelial or mesenchymal ones and can integrate the properties of cell-cell adhesion and motility that facilitates collective cell migration leading to clusters of circulating tumour cells (CTCs) - the prevalent mode of metastasis. Thus, identifying the molecular players that can enable cells to maintain a hybrid E/M phenotype is crucial to curb the metastatic load. Using an integrated computational-experimental approach, we show that the transcription factor NRF2 can prevent a complete EMT and instead stabilize a hybrid E/M phenotype. Knockdown of NRF2 in hybrid E/M non-small cell lung cancer cells H1975 and bladder cancer cells RT4 destabilized a hybrid E/M phenotype and compromised the ability to collectively migrate to close a wound in vitro. Notably, while NRF2 knockout simultaneously downregulated E-cadherin and ZEB-1, overexpression of NRF2 enriched for a hybrid E/M phenotype by simultaneously upregulating both E-cadherin and ZEB-1 in individual RT4 cells. Further, we predict that NRF2 is maximally expressed in hybrid E/M phenotype(s) and demonstrate that this biphasic dynamic arises from the interconnections among NRF2 and the EMT regulatory circuit. Finally, clinical records from multiple datasets suggest a correlation between a hybrid E/M phenotype, high levels of NRF2 and its targets and poor survival, further strengthening the emerging notion that hybrid E/M phenotype(s) may occupy the 'metastatic sweet spot'.

Collective Cell Migration in 3D Epithelial Wound Healing

Collective cell migration plays a pivotal role in development, wound healing, and metastasis, but little is known about the mechanisms and coordination of cell migration in 3D microenvironments. Here, we demonstrate a 3D wound healing assay by photothermal ablation for investigating collective cell migration in epithelial tissue structures. The nanoparticle-mediated photothermal technique creates local hyperthermia for selective cell ablation and induces collective cell migration of 3D tissue structures. By incorporating dynamic single cell gene expression analysis, live cell actin staining, and particle image velocimetry, we show that the wound healing response consists of 3D vortex motion moving toward the wound followed by the formation of multicellular actin bundles and leader cells with active actin-based protrusions. Inhibition of ROCK signaling disrupts the multicellular actin bundle and enhances the formation of leader cells at the leading edge. Furthermore, single cell gene expression analysis, pharmacological perturbation, and RNA interference reveal that Notch1-Dll4 signaling negatively regulates the formation of multicellular actin bundles and leader cells. Taken together, our study demonstrates a platform for investigating 3D collective cell migration and underscores the essential roles of ROCK and Notch1-Dll4 signaling in regulating 3D epithelial wound healing.

Optimizing peptide nucleic acid probes for hybridization-based detection and identification of bacterial pathogens

Point-of-care (POC) diagnostics for infectious diseases have the potential to improve patient care and antibiotic stewardship. Nucleic acid hybridization is at the core of many amplification-free molecular diagnostics and detection probe configuration is key to diagnostic performance. Modified nucleic acids such as peptide nucleic acid (PNA) offer advantages compared to conventional DNA probes allowing for faster hybridization, better stability and minimal sample preparation for direct detection of pathogens. Probes with tethered fluorophore and quencher allow for solution-based assays and eliminate the need for washing steps thereby facilitating integration into microfluidic devices. Here, we compared the sensitivity and specificity of double stranded PNA probes (dsPNA) and PNA molecular beacons targeting E. coli and P. aeruginosa for direct detection of bacterial pathogens. In bulk fluid assays, the dsPNAs had an overall higher fluorescent signal and better sensitivity and specificity than the PNA beacons for pathogen detection. We further designed and tested an expanded panel of dsPNA probes for detection of a wide variety of pathogenic bacteria including probes for universal detection of eubacteria, Enterobacteriaceae family, and P. mirablis. To confirm that the advantage translated to other assay types we compared the PNA beacon and dsPNA in a prototype droplet microfluidic device. Beyond the bulk fluid assay and droplet devices, use of dsPNA probes may be advantageous in a wide variety of assays that employ homogenous nucleic acid hybridization.

Nanotube assisted microwave electroporation for single cell pathogen identification and antimicrobial susceptibility testing

A nanotube assisted microwave electroporation (NAME) technique is demonstrated for delivering molecular biosensors into viable bacteria for multiplex single cell pathogen identification to advance rapid diagnostics in clinical microbiology. Due to the small volume of a bacterial cell (~femtoliter), the intracellular concentration of the target molecule is high, which results in a strong signal for single cell detection without amplification. The NAME procedure can be completed in as little as 30 minutes and can achieve over 90% transformation efficiency. We demonstrate the feasibility of NAME for identifying clinical isolates of bloodborne and uropathogenic pathogens and detecting bacterial pathogens directly from patient's samples. In conjunction with a microfluidic single cell trapping technique, NAME allows single cell pathogen identification and antimicrobial susceptibility testing concurrently. Using this approach, the time for microbiological analysis reduces from days to hours, which will have a significant impact on the clinical management of bacterial infections.

Rapid Microbiology Screening in Pharmaceutical Workflows

Recently advances in miniaturization and automation have been utilized to rapidly decrease the time to result for microbiology testing in the clinic. These advances have been made due to the limitations of conventional culture-based microbiology methods, including agar plate and microbroth dilution, which have long turnaround times and require physicians to treat patients empirically with antibiotics before test results are available. Currently, there exist similar limitations in pharmaceutical sterility and bioburden testing, where the long turnaround times associated with standard microbiology testing drive costly inefficiencies in workflows. These include the time lag associated with sterility screening within drug production lines and the warehousing cost and time delays within supply chains during product testing. Herein, we demonstrate a proof-of-concept combination of a rapid microfluidic assay and an efficient cell filtration process that enables a path toward integrating rapid tests directly into pharmaceutical microbiological screening workflows. We demonstrate separation and detection of Escherichia coli directly captured and analyzed from a mammalian (i.e., CHO) cell culture with a 3.0 h incubation. The demonstration is performed using a membrane filtration module that is compatible with sampling from bioreactors, enabling in-line sampling and process monitoring.

Development of DNA Pair Biosensor for Quantization of Nuclear Factor Kappa B

Nuclear factor kappa B (NF-κB), regulating the expression of several genes that mediate the inflammatory responses and cell proliferation, is one of the therapeutic targets for chronic inflammatory disease and cancer. A novel molecular binding scheme for the detection of NF-κB was investigated for its affinity to Ig-κB DNA composed by dye and quencher fluorophores, and this specificity is confirmed by competing with the DNA sequence that is complementary to the Ig-κB DNA. We create a normalization equation to remove the negative effects from the various initial fluorophore concentrations and the background noise. We also found that a periodic shaking at a frequency could help to stabilize the DNA⁻protein binding. The calibration experiment, using purified p50 (NF-κB), shows that this molecular probe biosensor has a detection limit on the order of nanomolar. The limit of detection is determined by the binding performance of dye and quencher oligonucleotides, and only a small portion of probes are stabilized by DNA-binding protein NF-κB. The specificity experiment also shows that p50/p65 heterodimer has the highest affinity for Ig-κB DNA; p65 homodimer binds with intermediate affinity, whereas p50 shows the lowest binding affinity, and Ig-κB DNA is not sensitive to BSA (bovine albumin serum). The experiment of HeLa nuclear extract shows that TNF-α stimulated HeLa nuclear extract has higher affinity to Ig-κB DNA than non-TNF-stimulated HeLa nuclear extract (4-h serum response). Therefore, the molecular binding scheme provides a rapid, quantitative, high throughput, and automated measurement of the DNA-binding protein NF-κB at low cost, which is beneficial for automated drug screening systems.