Optimal Hyperspectral Band Selection for Tissue Oxygenation Mapping with Generative Adversarial Network.

Tissue oxygenation assessment using hyperspectral imaging is an emerging technique for the diagnosis and pre- and post-treatment monitoring of ischemic patients. However, the high spectral resolution of hyperspectral imaging leads to large data sizes and a long imaging time. In this study, we propose a method that utilizes multi-objective evolutionary algorithms to determine the optimal hyperspectral band combination when developing a deep learning model for predicting tissue oxygenation from hyperspectral images. Our results confirm that the deep learning model effectively predicts tissue oxygenation images for various oxygenation states. Moreover, we demonstrate that a high-performance prediction model can be developed using only a small number of spectral bands, indicating the potential for more efficient non-contact tissue oxygenation mapping with the proposed method.Clinical Relevance- The proposed method allows for the non-contact and efficient acquisition of two-dimensional tissue oxygenation information in various oxygenation states.

Unlocking the Power of Complement-Dependent Cytotoxicity: Engineering Strategies for the Development of Potent Therapeutic Antibodies for Cancer Treatments.

The complement system is a crucial part of the innate immune response, providing defense against invading pathogens and cancer cells. Recently, it has become evident that the complement system plays a significant role in anticancer activities, particularly through complement-dependent cytotoxicity (CDC), alongside antibody-dependent cell-mediated cytotoxicity (ADCC) and antibody-dependent cell-mediated phagocytosis (ADCP). With the discovery of new roles for serum complement molecules in the human immune system, various approaches are being pursued to develop CDC-enhanced antibody therapeutics. In this review, we focus on successful antibody engineering strategies for enhancing CDC, analyzing the lessons learned and the limitations of each approach. Furthermore, we outline potential pathways for the development of antibody therapeutics specifically aimed at enhancing CDC for superior therapeutic efficacy in the future.

Analysis and forecasting of national marine litter based on coastal data in South Korea from 2009 to 2021.

In this study, statistical analysis and forecasting were performed using coastal litter data of Korea. The analysis indicated that rope and vinyl accounted for the highest proportion of coastal litter items. The statistical analysis of the national coastal litter trends revealed that the greatest concentration of litter was observed during summer months (June-August). To predict the amount of coastal litter per meter, recurrent neural network (RNN)-based models were used. Neural basis expansion analysis for interpretable time series forecasting (N-BEATS) and neural hierarchical interpolation for time series forecasting (N-HiTS), an improved model of N-BEATS recently announced, were used for comparison with RNN-based models. When predictive performance and trend followability were evaluated, overall N-BEATS and N-HiTS outperformed RNN-based models. Furthermore, we found that average of N-BEATS and N-HiTS models yielded better results than using one model.

Fatigue Analysis of a 40 ft LNG ISO Tank Container.

The demand for Liquefied natural gas (LNG) has rapidly increased over the past few years. This is because of increasingly stringent environmental regulations to curb harmful emissions from fossil fuels. LNG is one of the clean energy sources that has attracted a great deal of research. In the Republic of Korea, the use of LNG has been implemented in various sectors, including public transport buses, domestic applications, power generation, and in huge marine engines. Therefore, a proper, flexible, and safe transport system should be put in place to meet the high demand. In this work, finite element analysis (FEA) was performed on a domestically developed 40 ft ISO LNG tank using Ansys Mechanical software under low- and high-cycle conditions. The results showed that the fatigue damage factor for all the test cases was much lower than 1. The maximum principal stress generated in the 40 ft LNG ISO tank container did not exceed the yield strength of the calculated material (carbon steel). Maximum principal stress of 123.2 MPa and 107.61 MPa was obtained with low-cycle and high-cycle analysis, respectively, which is 50.28% less than the yield strength of carbon steel. The total number of cycles was greater than the total number of design cycles, and the 40 ft LNG ISO tank container was satisfied with a fatigue life of 20 years.

Anomaly Detection Based on Time Series Data of Hydraulic Accumulator.

Although hydraulic accumulators play a vital role in the hydraulic system, they face the challenges of being broken by continuous abnormal pulsating pressure which occurs due to the malfunction of hydraulic systems. Hence, this study develops anomaly detection algorithms to detect abnormalities of pulsating pressure for hydraulic accumulators. A digital pressure sensor was installed in a hydraulic accumulator to acquire the pulsating pressure data. Six anomaly detection algorithms were developed based on the acquired data. A threshold averaging algorithm over a period based on the averaged maximum/minimum thresholds detected anomalies 2.5 h before the hydraulic accumulator failure. In the support vector machine (SVM) and XGBoost model that distinguish normal and abnormal pulsating pressure data, the SVM model had an accuracy of 0.8571 on the test set and the XGBoost model had an accuracy of 0.8857. In a convolutional neural network (CNN) and CNN autoencoder model trained with normal and abnormal pulsating pressure images, the CNN model had an accuracy of 0.9714, and the CNN autoencoder model correctly detected the 8 abnormal images out of 11 abnormal images. The long short-term memory (LSTM) autoencoder model detected 36 abnormal data points in the test set.

Constrained Multiple Planar Reconstruction for Automatic Camera Calibration of Intelligent Vehicles.

In intelligent vehicles, extrinsic camera calibration is preferable to be conducted on a regular basis to deal with unpredictable mechanical changes or variations on weight load distribution. Specifically, high-precision extrinsic parameters between the camera coordinate and the world coordinate are essential to implement high-level functions in intelligent vehicles such as distance estimation and lane departure warning. However, conventional calibration methods, which solve a Perspective-n-Point problem, require laborious work to measure the positions of 3D points in the world coordinate. To reduce this inconvenience, this paper proposes an automatic camera calibration method based on 3D reconstruction. The main contribution of this paper is a novel reconstruction method to recover 3D points on planes perpendicular to the ground. The proposed method jointly optimizes reprojection errors of image features projected from multiple planar surfaces, and finally, it significantly reduces errors in camera extrinsic parameters. Experiments were conducted in synthetic simulation and real calibration environments to demonstrate the effectiveness of the proposed method.

Application of soot discharged from the combustion of marine gas oil as an anode material for lithium ion batteries.

Many studies have recently investigated the characteristics of combustion products emitted from ships and onshore plant facilities for use as energy sources. Most combustion products that have been reported until now are from heavy oils, however, no studies on those from light oils have been published. This study attempted to use the combustion products from the light oils from naval ships as anode materials for lithium ion batteries (LIBs). These products have a carbon black morphology and were transformed into highly crystalline carbon structures through a simple heat treatment. These new structured materials showed reversible capacities of 544, 538, 510, 485, 451 and 395 mA h g-1 at C-rates of 0.1, 0.2, 0.5, 1.0, 2.0 and 5.0C, respectively, and excellent rate performance. These findings were the result of a combination hierarchical pores ranging from the meso- to macroscale and the high capacitive charge storage behavior of the soot. The results of this study prove that annealed soot with a unique multilayer graphite structure shows promising electrochemical performance suitable for the production of low-cost, high-performance LIB anode materials.

Correction: Application of soot discharged from the combustion of marine gas oil as an anode material for lithium ion batteries.

[This corrects the article DOI: 10.1039/D0RA07195A.].

Conversion of Black Carbon Emitted from Diesel-Powered Merchant Ships to Novel Conductive Carbon Black as Anodic Material for Lithium Ion Batteries.

Waste soot generated from diesel engine of merchant ships has ≥ 2 µm agglomerates consisting of 30-50 nm spherical particles, whose morphology is identical to that of carbon black (CB) used in many industrial applications. In this study, we crystallized waste soot by heat treatment to transform it into a unique completely graphitic nano-onion structure, which is considerably different from that of commercial conductive CB. While commercial CB has a large specific surface area because of many surface micropores generated due to quenching by water-spraying in the production process, the heat-treated waste soot has a smooth micropore-free surface. Thus, the treated waste soot acquires the shape of CB but has a much smaller specific surface area. When the treated soot is used as a conductive material in lithium ion battery (LIB) half cells, the Coulombic efficiency of the entire anode is improved significantly owing to its low specific surface area; the electrochemical performance of the LIB is considerably enhanced compared to that of conventional conductive materials. Thus, polluting soot generated in marine propulsion can be transformed into a new class of CB with a unique structure by simple heat treatment; this soot can also be used as an inexpensive conductive material to enhance the LIB performance.

Recycling Waste Soot from Merchant Ships to Produce Anode Materials for Rechargeable Lithium-Ion Batteries.

In this study, the waste soot generated by ships was recycled to produce an active material for use in lithium-ion batteries (LIBs). Soot collected from a ship was graphitized by a heat treatment process and used as an anode active material. It was confirmed that the graphitized soot was converted into a highly crystalline graphite, and was found to form carbon nano-onions with an average diameter of 70 nm. The graphitized soot showed a high discharge capacity and an excellent cycle life, with a reversible capacity of 260 mAhg-1 even after 150 cycles at a rate of 1 C. This study demonstrates that the annealed soot with a unique graphitic multilayer structure has an electrochemical performance that renders it suitable as a candidate for the production of low-cost anode materials for use in LIBs.

Carbon Nanostructure of Diesel Soot Particles Emitted from 2 and 4 Stroke Marine Engines Burning Different Fuels.

Diesel soot particles were sampled from 2-stroke and 4-stroke engines that burned two different fuels (Bunker A and C, respectively), and the effects of the engine and fuel types on the structural characteristics of the soot particle were analyzed. The carbon nanostructures of the sampled particles were characterized using various techniques. The results showed that the soot sample collected from the 4-stroke engine, which burned Bunker C, has a higher degree of order of the carbon nanostructure than the sample collected from the 2-stroke engine, which burned Bunker A. Furthermore, the difference in the exhaust gas temperatures originating from the different engine and fuel types can affect the nanostructure of the soot emitted from marine diesel engines.

Plasmon-Coupled Whispering Gallery Modes on Nanodisk Arrays for Signal Enhancements.

Metallic nanostructures including single and double nanodisks are successfully used to enhance the localized electric field in vicinity of microcavity in whispering gallery mode (WGM) sensor. We demonstrate numerical calculations of plasmonic coupling of WGMs to single and double nanodisk arrays on a planar substrate. We then experimentally confirmed that the resonance wavelength of WGM sensor was dramatically shifted by adoption of single and double nanodisks on the surface of microcavity in the WGM sensor. Thus, our approach provides the tunable sensitivity of WGM sensor, and has a great potential to be used in numerous areas where the single biomolecule, protein-protein folding and biomolecular interactions are involved.

Molecular overlap with optical near-fields based on plasmonic nanolithography for ultrasensitive label-free detection by light-matter colocalization.

In this work, we investigate the detection sensitivity of surface plasmon resonance (SPR) biosensors by engineering spatial distribution of electromagnetic near-fields for colocalization with molecular distribution. The light-matter colocalization was based on plasmonic nanolithography, the concept of which was confirmed by detecting streptavidin biotin interactions on triangular nanoaperture arrays after the structure of the aperture arrays was optimized for colocalization efficiency. The colocalization was shown to amplify optical signature significantly and thereby to achieve detection on the order of 100 streptavidin molecules with a binding capacity below 1fg/mm2, an enhancement by more than three orders of magnitude over conventional SPR detection.

Ultra-Sensitive Surface Plasmon Resonance Detection by Colocalized 3D Plasmonic Nanogap Arrays.

Ultra-sensitive detection based on surface plasmon resonance (SPR) was investigated using 3D nanogap arrays for colocalization of target molecular distribution and localized plasmon wave in the near-field. Colocalization was performed by oblique deposition of a dielectric mask layer to create nanogap at the side of circular and triangular nanoaperture, where fields localized by surface plasmon localization coincide with the spatial distribution of target molecular interactions. The feasibility of ultra-sensitivity was experimentally verified by measuring DNA hybridization. Triangular nanopattern provided an optimum to achieve highly amplified angular shifts and led to enhanced detection sensitivity on the order of 1 fg/mm2 in terms of molecular binding capacity. We confirmed improvement of SPR sensitivity by three orders of magnitude, compared with conventional SPR sensors, using 3D plasmonic nanogap arrays.

Three-Dimensional Superlocalization Imaging of Gliding Mycoplasma mobile by Extraordinary Light Transmission through Arrayed Nanoholes.

In this paper, we describe super-resolved sampling of live bacteria based on extraordinary optical transmission (EOT) of light. EOT is produced by surface plasmon confinement and coupling with nanostructures. Bacterial fluorescence is excited by the localized fields for subdiffraction-limited sampling. The concept was applied to elucidating bacterial dynamics of gliding Mycoplasma mobile (M. mobile). The results analyzed with multiple M. mobile bacteria show individual characters and reveal that M. mobile undergoes a significant axial variation at 94 nm. The sampling error of the method is estimated to be much smaller than 1/10 of the diffraction limit both in the lateral and depth axis. The method provides a powerful tool for investigation of biomolecular dynamics at subwavelength precision.

Localization-based full-field microscopy: how to attain super-resolved images.

In this study, we have investigated localization-based microscopy to achieve full-field super-resolution. For localized sampling, we have considered combs consisting of unit pulses and near-fields localized by surface nanoapertures. Achievable images after reconstruction were assessed in terms of peak signal-to-noise ratio (PSNR). It was found that spatial switching of individual pulses may be needed to break the diffraction limit. Among the parameters, the resolution was largely determined by sampling period while the effect of width of a sampling pulse on PSNR was relatively limited. For the range of sampling parameters that we considered, the highest resolution achievable is estimated to be 70 nm, which can further be enhanced by optimizing the localization parameters.

Surface plasmon-enhanced nanoscopy of intracellular cytoskeletal actin filaments using random nanodot arrays.

The feasibility of super-resolution microscopy has been investigated based on random localization of surface plasmon using blocked random nanodot arrays. The resolution is mainly determined by the size of localized fields in the range of 100-150 nm. The concept was validated by imaging FITC-conjugated phalloidin that binds to cellular actin filaments. The experimental results confirm improved resolution in reconstructed images. Effect of far-field registration on image reconstruction was also analyzed. Correlation between reconstructed images was maintained to be above 81% after registration. Nanodot arrays are synthesized by temperature-annealing without sophisticated lithography and thus can be mass-produced in an extremely large substrate. The results suggest a super-resolution imaging technique that can be accessible and available in large amounts.

Probabilistic evaluation of surface-enhanced localized surface plasmon resonance biosensing.

In this paper, we investigate detection characteristics of localized surface plasmon resonance biosensing based on a probabilistic Poisson distribution of target molecules. The model uses random nanoislands for localization of near-fields in three detection scenarios of non-specific, non-colocalized, and colocalized detection. Optical signatures were found to increase monotonically with target concentration and size regardless of the detection scenarios. The signatures were largest in colocalized detection of target interactions to localized fields, followed by non-colocalized and non-specific detection. The confidence interval was the narrowest in the colocalized detection due to the increased spatial certainty by localization. Based on the relative confidence interval, it was found that limit of detection can be enhanced by more than four orders of magnitude through colocalization.

Self-aligned colocalization of 3D plasmonic nanogap arrays for ultra-sensitive surface plasmon resonance detection.

We report extremely sensitive plasmonic detection that was performed label-free based on the colocalization of target DNA molecules and electromagnetic hot spots excited at 3D nanogap arrays. The colocalization was self-aligned by oblique evaporation of a dielectric mask over the 3D nanopatterns, which creates nanogaps for spatially selective target binding. The feasibility was experimentally confirmed by measuring hybridization of 24-mer single-stranded DNA oligonucleotides on triangular and circular 3D nanogap arrays. We were able to achieve significantly amplified optical signatures that lead to sensitivity enhancement in terms of detectable binding capacity in reference to conventional thin film-based surface plasmon resonance detection on the order of 1 fg/mm(2).

Best basis selection method using learning weights for face recognition.

In the face recognition field, principal component analysis is essential to the reduction of the image dimension. In spite of frequent use of this analysis, it is commonly believed that the basis faces with large eigenvalues are chosen as the best subset in the nearest neighbor classifiers. We propose an alternative that can predict the classification error during the training steps and find the useful basis faces for the similarity metrics of the classical pattern algorithms. In addition, we also show the need for the eye-aligned dataset to have the pure face. The experiments using face images verify that our method reduces the negative effect on the misaligned face images and decreases the weights of the useful basis faces in order to improve the classification accuracy.