A Bidirectional Subsurface Remote Sensing Reflectance Model Explicitly Accounting for Particle Backscattering Shapes

The subsurface remote sensing reflectance (rrs, sr−1), particularly its bidirectional reflectance distribution function (BRDF), depends fundamentally on the angular shape of the volume scattering functions (VSFs, m−1 sr−1). Recent technological advancement has greatly expanded the collection, and the knowledge of natural variability, of the VSFs of oceanic particles. This allows us to test the Zaneveld\u27s theoretical rrs model that explicitly accounts for particle VSF shapes. We parameterized the rrs model based on HydroLight simulations using 114 VSFs measured in three coastal waters around the United States and in oceanic waters of North Atlantic Ocean. With the absorption coefficient (a), backscattering coefficient (bb), and VSF shape as inputs, the parameterized model is able to predict rrs with a root mean square relative error of ∼4% for solar zenith angles from 0 to 75°, viewing zenith angles from 0 to 60°, and viewing azimuth angles from 0 to 180°. A test with the field data indicates the performance of our model, when using only a and bb as inputs and selecting the VSF shape using bb, is comparable to or slightly better than the currently used models by Morel et al. and Lee et al. Explicitly expressing VSF shapes in rrs modeling has great potential to further constrain the uncertainty in the ocean color studies as our knowledge on the VSFs of natural particles continues to improve. Our study represents a first effort in this direction

Calibration of the LISST-VSF to Derive the Volume Scattering Functions In Clear Waters

The recently commercialized LISST-VSF instrument measures the volume scattering function (VSF) from 0.1° to 15° with a traditional laser diffraction unit (LISST) and from 15° to 155° with an eyeball component. Between these two optical components, only the LISST unit is calibrated. The eyeball measurements are scaled using the VSFs at 15° that are measured by both components. As this relative calibration relies on a valid measurement at 15° by the LISST, it might fail in clear oceanic waters, where the forward scattering is relative weak either due to a lack of large particles or an overall low concentration of all particles. In this study, we calibrated the LISST-VSF eyeball component through a series of lab experiments using standard polystyrene beads. Validation with the beads of two different sizes showed a median difference of 11.1% between theoretical and calibrated values. Further evaluations with in situ data collected by the LISST-VSF and an ECO-BB3 meter indicated that the new calibration worked well in both turbid and clear waters, while the relative calibration method tended to overestimate VSFs in clear waters

Ultrasonic Image's Annotation Removal: A Self-supervised Noise2Noise Approach

Accurately annotated ultrasonic images are vital components of a high-quality medical report. Hospitals often have strict guidelines on the types of annotations that should appear on imaging results. However, manually inspecting these images can be a cumbersome task. While a neural network could potentially automate the process, training such a model typically requires a dataset of paired input and target images, which in turn involves significant human labour. This study introduces an automated approach for detecting annotations in images. This is achieved by treating the annotations as noise, creating a self-supervised pretext task and using a model trained under the Noise2Noise scheme to restore the image to a clean state. We tested a variety of model structures on the denoising task against different types of annotation, including body marker annotation, radial line annotation, etc. Our results demonstrate that most models trained under the Noise2Noise scheme outperformed their counterparts trained with noisy-clean data pairs. The costumed U-Net yielded the most optimal outcome on the body marker annotation dataset, with high scores on segmentation precision and reconstruction similarity. We released our code at https://github.com/GrandArth/UltrasonicImage-N2N-Approach.Comment: 10 pages, 7 figure

Enhancing Digestibility and Ethanol Yield of Populus Wood via Expression of an Engineered Monolignol 4-O-Methyltransferase

Producing cellulosic biofuels and bio-based chemicals from woody biomass is impeded by the presence of lignin polymer in the plant cell wall. Manipulating the monolignol biosynthetic pathway offers a promising approach to improved processability, but often impairs plant growth and development. Here, we show that expressing an engineered 4-O-methyltransferase that chemically modifies the phenolic moiety of lignin monomeric precursors, thus preventing their incorporation into the lignin polymer, substantially alters hybrid aspens’ lignin content and structure. Woody biomass derived from the transgenic aspens shows a 62% increase in the release of simple sugars and up to a 49% increase in the yield of ethanol when the woody biomass is subjected to enzymatic digestion and yeast-mediated fermentation. Moreover, the cell wall structural changes do not affect growth and biomass production of the trees. Our study provides a useful strategy for tailoring woody biomass for bio-based applications

Adaptation of Dominant Species to Drought in the Inner Mongolia Grassland – Species Level and Functional Type Level Analysis

The adaptation of plants to drought through the adjustment of their leaf functional traits is a hot topic in plant ecology. However, while there is a good understanding of how individual species adapt to drought in this way, the way in which different functional types adapt to drought along a precipitation gradient remains poorly understood. In this study, we sampled 22 sites along a precipitation gradient in the Inner Mongolia grassland and measured eight leaf functional traits across 39 dominant species to determine the adaptive strategies of plant leaves to drought at the species and plant functional type levels. We found that leaf functional traits were mainly influenced by both aridity and phylogeny at the species level. There were four types of leaf adaptations to drought at the functional type level: adjusting the carbon-nitrogen ratio, the specific leaf area, the nitrogen content, and the specific leaf area and leaf nitrogen content simultaneously. These findings indicate that there is the trade-offs relationship between water and nitrogen acquisition as the level of drought increases, which is consistent with the worldwide leaf economics spectrum. In this study, we highlighted that the leaf economic spectrum can be adopted to reveal the adaptations of plants to drought in the Inner Mongolia grassland

Joint Hypergraph Learning and Sparse Regression for Feature Selection

In this paper, we propose a unified framework for improved structure estimation and feature selection. Most existing graph-based feature selection methods utilise a static representation of the structure of the available data based on the Laplacian matrix of a simple graph. Here on the other hand, we perform data structure learning and feature selection simultaneously. To improve the estimation of the manifold representing the structure of the selected features, we use a higher order description of the neighbour- hood structures present in the available data using hypergraph learning. This allows those features which participate in the most significant higher order relations to be se- lected, and the remainder discarded, through a sparsification process. We formulate a single objective function to capture and regularise the hypergraph weight estimation and feature selection processes. Finally, we present an optimization algorithm to re- cover the hyper graph weights and a sparse set of feature selection indicators. This process offers a number of advantages. First, by adjusting the hypergraph weights, we preserve high-order neighborhood relations reflected in the original data, which cannot be modeled by a simple graph. Moreover, our objective function captures the global discriminative structure of the features in the data. Comprehensive experiments on 9 benchmark data sets show that our method achieves statistically significant improve- ment over state-of-art feature selection methods, supporting the effectiveness of the proposed method

Divergent evolution of extreme production of variant plant monounsaturated fatty acids

Metabolic extremes provide opportunities to understand enzymatic and metabolic plasticity and biotechnological tools for novel biomaterial production. We discovered that seed oils of many Thunbergia species contain up to 92% of the unusual monounsaturated petroselinic acid (18:1Δ6), one of the highest reported levels for a single fatty acid in plants. Supporting the biosynthetic origin of petroselinic acid, we identified a Δ6-stearoyl-acyl carrier protein (18:0-ACP) desaturase from Thunbergia laurifolia, closely related to a previously identified Δ6-palmitoyl-ACP desaturase that produces sapienic acid (16:1Δ6)- rich oils in Thunbergia alata seeds. Guided by a T. laurifolia desaturase crystal structure obtained in this study, enzyme mutagenesis identified key amino acids for functional divergence of Δ6 desaturases from the archetypal Δ9-18:0-ACP desaturase and mutations that result in nonnative enzyme regiospecificity. Furthermore, we demonstrate the utility of the T. laurifolia desaturase for the production of unusual monounsaturated fatty acids in engineered plant and bacterial hosts. Through stepwise metabolic engineering, we provide evidence that divergent evolution of extreme petroselinic acid and sapienic acid production arises from biosynthetic and metabolic functional specialization and enhanced expression of specific enzymes to accommodate metabolism of atypical substrates

Gut-joint axis in knee synovitis: gut fungal dysbiosis and altered fungi–bacteria correlation network identified in a community-based study

Objectives: Knee synovitis is a highly prevalent and potentially curable condition for knee pain; however, its pathogenesis remains unclear. We sought to assess the associations of the gut fungal microbiota and the fungi–bacteria correlation network with knee synovitis. Methods: Participants were derived from a community-based cross-sectional study. We performed an ultrasound examination of both knees. A knee was defined as having synovitis if its synovium was ≥4 mm and/or Power Doppler (PD) signal was within the knee synovium area (PD synovitis). We collected faecal specimens from each participant and assessed gut fungal and bacterial microbiota using internal transcribed spacer 2 and shotgun metagenomic sequencing. We examined the relation of α-diversity, β-diversity, the relative abundance of taxa and the interkingdom correlations to knee synovitis. Results: Among 977 participants (mean age: 63.2 years; women: 58.8%), 191 (19.5%) had knee synovitis. β-diversity of the gut fungal microbiota, but not α-diversity, was significantly associated with prevalent knee synovitis. The fungal genus Schizophyllum was inversely correlated with the prevalence and activity (ie, control, synovitis without PD signal and PD synovitis) of knee synovitis. Compared with those without synovitis, the fungi–bacteria correlation network in patients with knee synovitis was smaller (nodes: 93 vs 153; edges: 107 vs 244), and the average number of neighbours was fewer (2.3 vs 3.2). Conclusion: Alterations of gut fungal microbiota and the fungi–bacteria correlation network are associated with knee synovitis. These novel findings may help understand the mechanisms of the gut-joint axis in knee synovitis and suggest potential targets for future treatment