BM-BronchoLC - A rich bronchoscopy dataset for anatomical landmarks and lung cancer lesion recognition.

Flexible bronchoscopy has revolutionized respiratory disease diagnosis. It offers direct visualization and detection of airway abnormalities, including lung cancer lesions. Accurate identification of airway lesions during flexible bronchoscopy plays an important role in the lung cancer diagnosis. The application of artificial intelligence (AI) aims to support physicians in recognizing anatomical landmarks and lung cancer lesions within bronchoscopic imagery. This work described the development of BM-BronchoLC, a rich bronchoscopy dataset encompassing 106 lung cancer and 102 non-lung cancer patients. The dataset incorporates detailed localization and categorical annotations for both anatomical landmarks and lesions, meticulously conducted by senior doctors at Bach Mai Hospital, Vietnam. To assess the dataset's quality, we evaluate two prevalent AI backbone models, namely UNet++ and ESFPNet, on the image segmentation and classification tasks with single-task and multi-task learning paradigms. We present BM-BronchoLC as a reference dataset in developing AI models to assist diagnostic accuracy for anatomical landmarks and lung cancer lesions in bronchoscopy data.

Antibiofilm Activity of Eugenol-Loaded Chitosan Coatings against Common Medical-Device-Contaminating Bacteria.

The formation of pathogenic biofilms on medical devices is a major public health concern accounting for over 65% of healthcare-associated infections and causing high infection morbidity, mortality, and a great burden to patients and the healthcare system due to its resistance to treatment. In this study, we developed a chitosan-based antimicrobial coating with embedded mesoporous silica nanoparticles (MSNs) to load and deliver eugenol, an essential oil component, to inhibit the biofilm formation of common bacteria in medical-device-related infections. The eugenol-loaded MSNs were dispersed in a chitosan solution, which was then cross-linked with glutaraldehyde and drop-casted to obtain coatings. The MSNs and coatings were characterized by dynamic light scattering, Brunauer-Emmett-Teller analysis, attenuated-total-reflectance Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, 3D optical profilometry, and scanning electron microscopy. The release behavior of eugenol-loaded MSNs and coatings and the antibiofilm and antimicrobial activity of the coatings against adherent Staphylococcus aureus, methicillin-resistant S. aureus, and Pseudomonas aeruginosa were investigated. Eugenol was released from the MSNs and coatings in aqueous conditions in a controlled manner with an initial low release, followed by a peak release, a decrease, and a plateau. While the chitosan coatings alone or with unloaded MSNs demonstrated limited antimicrobial effects and still supported biofilm formation after 24 h, the coating containing eugenol not only reduced biofilm formation but also killed the majority of the attached bacteria. It also showed biocompatibility in indirect contact with NIH/3T3 fibroblasts and a high percentage of live cells in direct contact. However, further investigations into cell proliferation in direct contact are recommended. The findings indicated that the chitosan-based coating with eugenol-loaded MSNs could be developed into an effective strategy to inhibit biofilm formation on medical devices.

A finite element study on the interactive effect between the damage of the cup-bone interface and the bone strain of hip implants under various fixation conditions.

Interfacial damage has a high impact on the loosening of the acetabular cup. However, monitoring this damage induced by the variations in loading conditions, such as the angle, amplitude, and frequency in vivo, is challenging. In this study, we evaluated the risk of loosening of the acetabular cup due to interfacial damages induced by the deviation in loading conditions and amplitudes. A three-dimensional model of the acetabular cup component was developed, and the interfacial crack growth between the cup and the bone was modeled using a fracture mechanics approach, which simulated the extent of interfacial damage and associated cup displacement. The interfacial delamination mechanism changed with the increasing inclination angle, wherein a fixation angle of 60° exhibited the largest area of contact loss. The compressive strain of embedding the simulated bone at the remaining bonding area accumulated as the lost contact area widened. Such interfacial damages, namely, the growth of the lost contact area and accumulated compressive strain in the simulated bone, promoted both embedding and rotational displacement of the acetabular cup. In the worst case of a fixation angle of 60°, the total displacement of the acetabular cup exceeded the limit of the modified safe zone, suggesting a quantitative risk of dislocation of the acetabular cup induced by the accumulated interfacial damage. Furthermore, nonlinear regression analyses between the degree of displacement of the acetabular cup and the extent of the two types of interfacial damage demonstrated that the interactive effect of the fixation angle with the loading amplitude showed a significant effect on increasing cup displacement. These findings suggest that proper control of the fixation angle during operation is useful in preventing the loosening of the hip joint.

PEg TRAnsfer Workflow recognition challenge report: Do multimodal data improve recognition?

BACKGROUND AND OBJECTIVE: In order to be context-aware, computer-assisted surgical systems require accurate, real-time automatic surgical workflow recognition. In the past several years, surgical video has been the most commonly-used modality for surgical workflow recognition. But with the democratization of robot-assisted surgery, new modalities, such as kinematics, are now accessible. Some previous methods use these new modalities as input for their models, but their added value has rarely been studied. This paper presents the design and results of the "PEg TRAnsfer Workflow recognition" (PETRAW) challenge with the objective of developing surgical workflow recognition methods based on one or more modalities and studying their added value. METHODS: The PETRAW challenge included a data set of 150 peg transfer sequences performed on a virtual simulator. This data set included videos, kinematic data, semantic segmentation data, and annotations, which described the workflow at three levels of granularity: phase, step, and activity. Five tasks were proposed to the participants: three were related to the recognition at all granularities simultaneously using a single modality, and two addressed the recognition using multiple modalities. The mean application-dependent balanced accuracy (AD-Accuracy) was used as an evaluation metric to take into account class balance and is more clinically relevant than a frame-by-frame score. RESULTS: Seven teams participated in at least one task with four participating in every task. The best results were obtained by combining video and kinematic data (AD-Accuracy of between 93% and 90% for the four teams that participated in all tasks). CONCLUSION: The improvement of surgical workflow recognition methods using multiple modalities compared with unimodal methods was significant for all teams. However, the longer execution time required for video/kinematic-based methods(compared to only kinematic-based methods) must be considered. Indeed, one must ask if it is wise to increase computing time by 2000 to 20,000% only to increase accuracy by 3%. The PETRAW data set is publicly available at www.synapse.org/PETRAW to encourage further research in surgical workflow recognition.

Engineering plant immune circuit: walking to the bright future with a novel toolbox.

Plant pathogens destroy crops and cause severe yield losses, leading to an insufficient food supply to sustain the human population. Apart from relying on natural plant immune systems to combat biological agents or waiting for the appropriate evolutionary steps to occur over time, researchers are currently seeking new breakthrough methods to boost disease resistance in plants through genetic engineering. Here, we summarize the past two decades of research in disease resistance engineering against an assortment of pathogens through modifying the plant immune components (internal and external) with several biotechnological techniques. We also discuss potential strategies and provide perspectives on engineering plant immune systems for enhanced pathogen resistance and plant fitness.

AvrRps4 effector family processing and recognition in lettuce.

During pathogenesis, effector proteins are secreted from the pathogen to the host plant to provide virulence activity for invasion of the host. However, once the host plant recognizes one of the delivered effectors, effector-triggered immunity activates a robust immune and hypersensitive response (HR). In planta, the effector AvrRps4 is processed into the N-terminus (AvrRps4N ) and the C-terminus (AvrRps4C ). AvrRps4C is sufficient to trigger HR in turnip and activate AtRRS1/AtRPS4-mediated immunity in Arabidopsis; on the other hand, AvrRps4N induces HR in lettuce. Furthermore, AvrRps4N -mediated HR requires a conserved arginine at position 112 (R112), which is also important for full-length AvrRps4 (AvrRps4F ) processing. Here, we show that effector processing and effector recognition in lettuce are uncoupled for the AvrRps4 family. In addition, we compared effector recognition by lettuce of AvrRps4 and its homologues, HopK1 and XopO. Interestingly, unlike for AvrRps4 and HopK1, mutation of the conserved R111 in XopO by itself was insufficient to abolish recognition. The combination of amino acid substitutions arginine 111 to leucine with glutamate 114 to lysine abolished the XopO-mediated HR, suggesting that AvrRps4 family members have distinct structural requirements for perception by lettuce. Together, our results provide an insight into the processing and recognition of AvrRps4 and its homologues.

Load-Balancing of Kubernetes-Based Edge Computing Infrastructure Using Resource Adaptive Proxy.

Kubernetes (K8s) is expected to be a key container orchestration tool for edge computing infrastructures owing to its various features for supporting container deployment and dynamic resource management. For example, its horizontal pod autoscaling feature provides service availability and scalability by increasing the number of replicas. kube-proxy provides traffic load-balancing between replicas by distributing client requests equally to all pods (replicas) of an application in a K8s cluster. However, this approach can result in long delays when requests are forwarded to remote workers, especially in edge computing environments where worker nodes are geographically dispersed. Moreover, if the receiving worker is overloaded, the request-processing delay can increase significantly. To overcome these limitations, this paper proposes an enhanced load balancer called resource adaptive proxy (RAP). RAP periodically monitors the resource status of each pod and the network status among worker nodes to aid in load-balancing decisions. Furthermore, it preferentially handles requests locally to the maximum extent possible. If the local worker node is overloaded, RAP forwards its requests to the best node in the cluster while considering resource availability. Our experimental results demonstrated that RAP could significantly improve throughput and reduce request latency compared with the default load-balancing mechanism of K8s.

Insight into the properties of MnO2-Co3O4-CeO2 catalyst series for the selective catalytic reduction of NOx by C3H6 and NH3.

A series of MnO2-Co3O4-CeO2 catalysts with different ceria loading (0.75, 1.26 and 1.88 Ce/Mn molar ratio) were synthesized by a co-precipitation technique and the catalytic activity was tested for selective catalytic reduction of NOx by C3H6 or NH3. The catalysts were characterized by various physicochemical techniques to examine the effect of ceria loading on the properties of catalysts, such as crystallinity of metal species, surface area, porosity, and acidity using physical adsorption analysis, SEM-EDX, H2-TPR, XRD, NH3-TPD and in-situ FTIR spectroscopy. Ceria loading had a significant effect on the reduction of NOx, with the catalyst having low amount of ceria loading (Ce/Mn = 0.75) showing excellent performance at low-temperature conditions, but the activity declined at higher temperature. The high ceria loading (Ce/Mn = 1.88) catalyst showed poor activity compared to the counterparts owing to the lower number of acid sites and the resulting lower adsorption capacity.

Recent Advances in Effector-Triggered Immunity in Plants: New Pieces in the Puzzle Create a Different Paradigm.

Plants rely on multiple immune systems to protect themselves from pathogens. When pattern-triggered immunity (PTI)-the first layer of the immune response-is no longer effective as a result of pathogenic effectors, effector-triggered immunity (ETI) often provides resistance. In ETI, host plants directly or indirectly perceive pathogen effectors via resistance proteins and launch a more robust and rapid defense response. Resistance proteins are typically found in the form of nucleotide-binding and leucine-rich-repeat-containing receptors (NLRs). Upon effector recognition, an NLR undergoes structural change and associates with other NLRs. The dimerization or oligomerization of NLRs signals to downstream components, activates "helper" NLRs, and culminates in the ETI response. Originally, PTI was thought to contribute little to ETI. However, most recent studies revealed crosstalk and cooperation between ETI and PTI. Here, we summarize recent advancements in our understanding of the ETI response and its components, as well as how these components cooperate in the innate immune signaling pathways. Based on up-to-date accumulated knowledge, this review provides our current perspective of potential engineering strategies for crop protection.

Nuclear Localization of HopA1Pss61 Is Required for Effector-Triggered Immunity.

Plant resistance proteins recognize cognate pathogen avirulence proteins (also named effectors) to implement the innate immune responses called effector-triggered immunity. Previously, we reported that hopA1 from Pseudomonas syringae pv. syringae strain 61 was identified as an avr gene for Arabidopsis thaliana. Using a forward genetic screen approach, we cloned a hopA1-specific TIR-NBS-LRR class disease resistance gene, RESISTANCE TO PSEUDOMONAS SYRINGAE6 (RPS6). Many resistance proteins indirectly recognize effectors, and RPS6 is thought to interact with HopA1Pss61 indirectly by surveillance of an effector target. However, the involved target protein is currently unknown. Here, we show RPS6 is the only R protein that recognizes HopA1Pss61 in Arabidopsis wild-type Col-0 accession. Both RPS6 and HopA1Pss61 are co-localized to the nucleus and cytoplasm. HopA1Pss61 is also distributed in plasma membrane and plasmodesmata. Interestingly, nuclear localization of HopA1Pss61 is required to induce cell death as NES-HopA1Pss61 suppresses the level of cell death in Nicotiana benthamiana. In addition, in planta expression of hopA1Pss61 led to defense responses, such as a dwarf morphology, a cell death response, inhibition of bacterial growth, and increased accumulation of defense marker proteins in transgenic Arabidopsis. Functional characterization of HopA1Pss61 and RPS6 will provide an important piece of the ETI puzzle.

The Conserved Arginine Required for AvrRps4 Processing Is Also Required for Recognition of Its N-Terminal Fragment in Lettuce.

Pathogens utilize a repertoire of effectors to facilitate pathogenesis, but when the host recognizes one of them, it causes effector-triggered immunity. The Pseudomonas type III effector AvrRps4 is a bipartite effector that is processed in planta into a functional 133-amino acid N-terminus (AvrRps4-N) and 88-amino acid C-terminus (AvrRps4-C). Previous studies found AvrRps4-C to be sufficient to trigger the hypersensitive response (HR) in turnip. In contrast, our recent work found that AvrRps4-N but not AvrRps4-C triggered HR in lettuce, whereas both were required for resistance induction in Arabidopsis. Here, we initially compared AvrRps4 recognition by turnip and lettuce using transient expression. By serial truncation, we identified the central conserved region consisting of 37 amino acids as essential for AvrRps4-N recognition, whereas the putative type III secretion signal peptide or the C-terminal 13 amino acids were dispensable. Surprisingly, the conserved arginine at position 112 (R112) that is required for full-length AvrRps4 processing is also required for the recognition of AvrRps4-N by lettuce. Mutating R112 to hydrophobic leucine or negatively charged glutamate abolished the HR-inducing capacity of AvrRps4-N, while a positively charged lysine at this position resulted in a slow and weak HR. Together, our results suggest an AvrRps4-N recognition-specific role of R112 in lettuce.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Global Sensitivity Analysis of Metabolic Models for Phosphorus Accumulating Organisms in Enhanced Biological Phosphorus Removal.

The aim of this study was to identify, quantify and prioritize for the first time the sources of uncertainty in a mechanistic model describing the anaerobic-aerobic metabolism of phosphorus accumulating organisms (PAO) in enhanced biological phosphorus removal (EBPR) systems. These wastewater treatment systems play an important role in preventing eutrophication and metabolic models provide an advanced tool for improving their stability via system design, monitoring and prediction. To this end, a global sensitivity analysis was conducted using standard regression coefficients and Sobol sensitivity indices, taking into account the effect of 39 input parameters on 10 output variables. Input uncertainty was characterized with data in the literature and propagated to the output using the Monte Carlo method. The low degree of linearity between input parameters and model outputs showed that model simplification by linearization can be pursued only in very well defined circumstances. Differences between first and total-order sensitivity indices showed that variance in model predictions was due to interactions between combinations of inputs, as opposed to the direct effect of individual inputs. The major sources of uncertainty affecting the prediction of liquid phase concentrations, as well as intra-cellular glycogen and poly-phosphate was due to 64% of the input parameters. In contrast, the contribution to variance in intra-cellular PHA constituents was uniformly distributed among all inputs. In addition to the intra-cellular biomass constituents, notably PHB, PH2MV and glycogen, uncertainty with respect to input parameters directly related to anaerobic propionate uptake, aerobic poly-phosphate formation, glycogen formation and temperature contributed most to the variance of all model outputs. Based on the distribution of total-order sensitivities, characterization of the influent stream and intra-cellular fractions of PHA can be expected to significantly improve model reliability. The variance of EBPR metabolic model predictions was quantified. The means to account for this variance, with respect to each quantity of interest, given knowledge of the corresponding input uncertainties, was prescribed. On this basis, possible avenues and pre-requisite requirements to simplify EBPR metabolic models for PAO, both structurally via linearization, as well as by reduction of the number of non-influential variables were outlined.

Correction: New framework for automated article selection applied to a literature review of Enhanced Biological Phosphorus Removal.

[This corrects the article DOI: 10.1371/journal.pone.0216126.].

A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data.

Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg⁻Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility.