Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images

Urban land cover (ULC) serves as fundamental environmental information for urban studies, while accurate and timely ULC mapping remains challenging due to cloud contamination in tropical and subtropical areas. Synthetic aperture radar (SAR) has excellent all-weather working capability to overcome the challenge, while optical SAR data fusion is often required due to the limited land surface information provided by SAR. However, the mechanism by which SAR can compensate optical images, given the occurrence of clouds, in order to improve the ULC mapping, remains unexplored. To address the issue, this study proposes a framework, through various sampling strategies and three typical supervised classification methods, to quantify the ULC classification accuracy using optical and SAR data with various cloud levels. The land cover confusions were investigated in detail to understand the role of SAR in distinguishing land cover under different types of cloud coverage. Several interesting experimental results were found. First, 50% cloud coverage over the optical images decreased the overall accuracy by 10–20%, while the incorporation of SAR images was able to improve the overall accuracy by approximately 4%, by increasing the recognition of cloud-covered ULC information, particularly the water bodies. Second, if all the training samples were not contaminated by clouds, the cloud coverage had a higher impact with a reduction of 35% in the overall accuracy, whereas the incorporation of SAR data contributed to an increase of approximately 5%. Third, the thickness of clouds also brought about different impacts on the results, with an approximately 10% higher reduction from thick clouds compared with that from thin clouds, indicating that certain spectral information might still be available in the areas covered by thin clouds. These findings provide useful references for the accurate monitoring of ULC over cloud-prone areas, such as tropical and subtropical cities, where cloud contamination is often unavoidable.

Sensitive identification of known and unknown protease activities by unsupervised linear motif deconvolution

The cleavage-site specificities for many proteases are not well-understood, restricting the utility of supervised classification methods. We present an algorithm and web interface to overcome this limitation through the unsupervised detection of overrepresented patterns in protein sequence data, providing insight into the mixture of protease activities contributing to a complex system. Here, we apply the RObust LInear Motif Deconvolution (RoLiM) algorithm to confidently detect substrate cleavage patterns for SARS-CoV-2 Mpro protease in N terminome data of an infected human cell line. Using mass spectrometry-based peptide data from a case-control comparison of 341 primary urothelial bladder cancer cases and 110 controls, we identified distinct sequence motifs indicative of increased MMP activity in urine from cancer patients. Evaluation of N terminal peptides from patient plasma post-chemotherapy detected novel Granzyme B/Corin activity. RoLiM will enhance unbiased investigation of peptide sequences to establish the composition of known and uncharacterized protease activities in biological systems.

UTILIZATION OF SENTINEL-2 IMAGERY IN THE ESTIMATION OF PLASTICS AMONG FLOATING DEBRIS ALONG THE COAST OF MANILA BAY

With the Philippines ranking as the third largest source of plastics that end up in the oceans, there is a need to further explore methodologies that will become an aid in plastic waste removal from the ocean. Manila Bay is a natural harbor in the Philippines that serves as the center of different economic activities. However, the bay is also threatened with plastic pollution due to increasing population and industrial activities. BASECO is one of the areas in Manila Bay where clean-up activities are focused as this is where trash accumulates. Sentinel-2 images are provided free of charge by the European Commission's Copernicus Programme. Satellite images from June 2019 to May 2020 were inspected, then cloud-free images were downloaded. After downloading and pre-processing, spectral data of different types of plastic such as shipping pouch, bubble wrap, styrofoam, PET bottle, sando bag and snack packaging that were measured by a spectrometer during a fieldwork by the Development of Integrated Mapping, Monitoring, and Analytical Network System for Manila Bay and Linked Environments (project MapABLE) were utilized in the selection of training data. Then, indices such as the Normalized Vegetation Index (NDVI), Floating Debris Index (FDI) and Plastic Index (PI) from previous studies were analyzed for further separation of classes used as training data. These training data served as an input to the two supervised classification methods, Naive Bayes and Mixture Tuned Matched Filtering (MTMF). Both methods were validated by reports and articles from Philippine agencies indicating the spots where trash frequently accumulates.

Ionogels Based on a Single Ionic Liquid for Electronic Nose Application

Ionogel are versatile materials, as they present the electrical properties of ionic liquids and also dimensional stability, since they are trapped in a solid matrix, allowing application in electronic devices such as gas sensors and electronic noses. In this work, ionogels were designed to act as a sensitive layer for the detection of volatiles in a custom-made electronic nose. Ionogels composed of gelatin and a single imidazolium ionic liquid were doped with bare and functionalized iron oxide nanoparticles, producing ionogels with adjustable target selectivity. After exposing an array of four ionogels to 12 distinct volatile organic compounds, the collected signals were analyzed by principal component analysis (PCA) and by several supervised classification methods, in order to assess the ability of the electronic nose to distinguish different volatiles, which showed accuracy above 98%.

Metabolic and Immunological Subtypes of Esophageal Cancer Reveal Potential Therapeutic Opportunities

BackgroundEsophageal cancer has the sixth highest rate of cancer-associated deaths worldwide, with many patients displaying metastases and chemotherapy resistance. We sought to find subtypes to see if precision medicine could play a role in finding new potential targets and predicting responses to therapy. Since metabolism not only drives cancers but also serves as a readout, metabolism was examined as a key reporter for differences.MethodsUnsupervised and supervised classification methods, including hierarchical clustering, partial least squares discriminant analysis, k-nearest neighbors, and machine learning techniques, were used to discover and display two major subgroups. Genes, pathways, gene ontologies, survival, and immune differences between the groups were further examined, along with biomarkers between the groups and against normal tissue.ResultsEsophageal cancer had two major unique metabolic profiles observed between the histological subtypes esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). The metabolic differences suggest that ESCC depends on glycolysis, whereas EAC relies more on oxidative metabolism, catabolism of glycolipids, the tricarboxylic acid (TCA) cycle, and the electron transport chain. We also noted a robust prognostic risk associated with COQ3 expression. In addition to the metabolic alterations, we noted significant alterations in key pathways regulating immunity, including alterations in cytokines and predicted immune infiltration. ESCC appears to have increased signature associated with dendritic cells, Th17, and CD8 T cells, the latter of which correlate with survival in ESCC. We bioinformatically observed that ESCC may be more responsive to checkpoint inhibitor therapy than EAC and postulate targets to enhance therapy further. Lastly, we highlight correlations between differentially expressed enzymes and the potential immune status.ConclusionOverall, these results highlight the extreme differences observed between the histological subtypes and may lead to novel biomarkers, therapeutic strategies, and differences in therapeutic response for targeting each esophageal cancer subtype.

Short duration Vectorcardiogram based inferior myocardial infarction detection: class and subject-oriented approach

Myocardial infarction (MI) happens when blood stops circulating to an explicit segment of the heart causing harm to the heart muscles. Vectorcardiography (VCG) is a technique of recording direction and magnitude of the signals that are produced by the heart in a 3-lead representation. In this work, we present a technique for detection of MI in the inferior portion of heart using short duration VCG signals. The raw signal was pre-processed using the median and Savitzky–Golay (SG) filter. The Stationary Wavelet Transform (SWT) was used for time-invariant decomposition of the signal followed by feature extraction. The selected features using minimum-redundancy-maximum-relevance (mRMR) based feature selection method were applied to the supervised classification methods. The efficacy of the proposed method was assessed under both class-oriented and a more real-life subject-oriented approach. An accuracy of 99.14 and 89.37% were achieved respectively. Results of the proposed technique are better than existing state-of-art methods and used VCG segment is shorter. Thus, a shorter segment and a high accuracy can be helpful in the automation of timely and reliable detection of MI. The satisfactory performance achieved in the subject-oriented approach shows reliability and applicability of the proposed technique.

Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method

Previous studies have posited the use of acoustics-based surveys to monitor population size and estimate their density. However, decreasing the bias in population estimations, such as by using Capture–Mark–Recapture, requires the identification of individuals using supervised classification methods, especially for sparsely populated species like the wolf which may otherwise be counted repeatedly. The cryptic behaviour of Indian wolf (Canis lupus pallipes) poses serious challenges to survey efforts, and thus, there is no reliable estimate of their population despite a prominent role in the ecosystem. Like other wolves, Indian wolves produce howls that can be detected over distances of more than 6 km, making them ideal candidates for acoustic surveys. Here, we explore the use of a supervised classifier to identify unknown individuals. We trained a supervised Agglomerative Nesting hierarchical clustering (AGNES) model using 49 howls from five Indian wolves and achieved 98% individual identification accuracy. We tested our model’s predictive power using 20 novel howls from a further four individuals (test dataset) and resulted in 75% accuracy in classifying howls to individuals. The model can reduce bias in population estimations using Capture-Mark-Recapture and track individual wolves non-invasively by their howls. This has potential for studies of wolves’ territory use, pack composition, and reproductive behaviour. Our method can potentially be adapted for other species with individually distinctive vocalisations, representing an advanced tool for individual-level monitoring.