Rapid Microbial Profiling through Multimodal Biosensors for Transversal Analysis.

The intricate interactions between host and microbial communities hold significant implications for biology and medicine. However, traditional microbial profiling methods face limitations in processing time, measurement of absolute abundance, detection of low biomass, discrimination between live and dead cells, and functional analysis. This study introduces a rapid multimodal microbial characterization platform, Multimodal Biosensors for Transversal Analysis (MBioTA), for capturing the taxonomy, viability, and functional genes of the microbiota. The platform incorporates single cell biosensors, scalable microwell arrays, and automated image processing for rapid transversal analysis in as few as 2 h. The multimodal biosensors simultaneously characterize the taxon, viability, and functional gene expression of individual cells. By automating the image processing workflow, the single cell analysis techniques enable the quantification of bacteria with sensitivity down to 0.0075%, showcasing its capability in detecting low biomass samples. We illustrate the applicability of the MBioTA platform through the transversal analysis of the gut microbiota composition, viability, and functionality in a familial Alzheimer's disease mouse model. The effectiveness, rapid turnaround, and scalability of the MBioTA platform will facilitate its application from basic research to clinical diagnostics, potentially revolutionizing our understanding and management of diseases associated with microbe-host interactions.

Enhancing trabecular CT scans based on deep learning with multi-strategy fusion.

Trabecular bone analysis plays a crucial role in understanding bone health and disease, with applications like osteoporosis diagnosis. This paper presents a comprehensive study on 3D trabecular computed tomography (CT) image restoration, addressing significant challenges in this domain. The research introduces a backbone model, Cascade-SwinUNETR, for single-view 3D CT image restoration. This model leverages deep layer aggregation with supervision and capabilities of Swin-Transformer to excel in feature extraction. Additionally, this study also brings DVSR3D, a dual-view restoration model, achieving good performance through deep feature fusion with attention mechanisms and Autoencoders. Furthermore, an Unsupervised Domain Adaptation (UDA) method is introduced, allowing models to adapt to input data distributions without additional labels, holding significant potential for real-world medical applications, and eliminating the need for invasive data collection procedures. The study also includes the curation of a new dual-view dataset for CT image restoration, addressing the scarcity of real human bone data in Micro-CT. Finally, the dual-view approach is validated through downstream medical bone microstructure measurements. Our contributions open several paths for trabecular bone analysis, promising improved clinical outcomes in bone health assessment and diagnosis.

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.

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.

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.

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.

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.

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.

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.

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.

Diagnosis of Bloodstream Infections: An Evolution of Technologies towards Accurate and Rapid Identification and Antibiotic Susceptibility Testing.

Bloodstream infections (BSI) are a leading cause of death worldwide. The lack of timely and reliable diagnostic practices is an ongoing issue for managing BSI. The current gold standard blood culture practice for pathogen identification and antibiotic susceptibility testing is time-consuming. Delayed diagnosis warrants the use of empirical antibiotics, which could lead to poor patient outcomes, and risks the development of antibiotic resistance. Hence, novel techniques that could offer accurate and timely diagnosis and susceptibility testing are urgently needed. This review focuses on BSI and highlights both the progress and shortcomings of its current diagnosis. We surveyed clinical workflows that employ recently approved technologies and showed that, while offering improved sensitivity and selectivity, these techniques are still unable to deliver a timely result. We then discuss a number of emerging technologies that have the potential to shorten the overall turnaround time of BSI diagnosis through direct testing from whole blood-while maintaining, if not improving-the current assay's sensitivity and pathogen coverage. We concluded by providing our assessment of potential future directions for accelerating BSI pathogen identification and the antibiotic susceptibility test. While engineering solutions have enabled faster assay turnaround, further progress is still needed to supplant blood culture practice and guide appropriate antibiotic administration for BSI patients.

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.

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.

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. Locked nucleic acid (LNA) modification can be incorporated in the biosensors to enhance the binding affinity, specificity, and resistance to nuclease degradation. However, LNA monomers in the quencher sequence can also prevent the target-fluorophore probe binding, which reduces the signal of the dsLNA biosensor. This study investigates the influence of LNA modification on dsLNA biosensors by altering the position and amount of LNA monomers present in the quencher sequence. We characterize the fluorophore-quencher interaction, target detection, and specificity of the biosensor in free solution and evaluate the performance of the dsLNA biosensor in 2D monolayers and 3D spheroids. The data indicate that a large amount of LNA monomers in the quencher sequence can enhance the specificity of the biosensor, but prevents effective target binding. Together, our results provide guidelines for improving the performance of dsLNA biosensors in nucleic acid detection and gene expression analysis in live cells.

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.

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.

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.

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.