Biomass co-firing to improve the burn-out of unreactive coals in pulverised fuel combustion

Biomass utilisation as a fuel in power generation has become an increasingly attractive prospect due to legislation and consumer awareness surrounding fossil fuels and their effect on climate change. However, a large portion of the world relies upon energy produced from readily available local coal sources. Large quantities of these local coals have low combustion efficiencies and energy outputs. An investigation was conducted to improve the combustion performance of these unreactive coals through the addition of small quantities of biomass in co-fired pulverised fuel conditions. To assess whether unreactive coal co-fired with biomass produced improved combustion performance a study of slow heating interactions was undertaken. Through the use of laboratory thermal conversion techniques, thermogravimetric analysis and horizontal tube furnace, slow heating ramp rates were achieved. Samples blended on a 50% coal loading experienced these conditions. Slow heating pyrolysis on a 50% coal loading displayed no synergistic improvement to VM content of coal blended with biomass, whilst catalytic increases of char reactivity were observed for coal blended with high ash biomass species through burn-out testing, such as OC. Following the baseline observations for slow heating conditions, blended samples were subjected to fast heating ramp rate conditions, through the use of a drop tube furnace. At fast heating pyrolysis conditions on a 50% coal loading synergistic improvements of VM yield were observed for coal blends with low ash biomass species, such as W. High ash biomass species showed minimal evidence of synergetic increase to VM, instead displaying the catalytic improvements to char burn-out performance, as seen with slow heating rates. A trail of varying coal loading ratios was conducted to determine the quantity of biomass required to observe the greatest improvements and to ascertain the viability of findings at industrially relevant conditions. Synergistic improvements in VM yield were caused by a steam gasification mechanism during fast heating ramp rates whilst catalytic improvements were caused by the presence of high quantities of alkali and alkaline earth metals (AAEMs). Fast heating rate coal blend trials conducted with partially demineralised biomass fuels provided a deeper understanding of the influence that AAEMs had on char reactivity. A regression analysis provided a quadratic model that demonstrated a strong relationship between AAEMs and char reactivity, with a correlation coefficient R2 value in excess of 95%. A fast heating rate combustion test was conducted to determine whether improvement to ignition distance could be achieved through co-firing. Both qualitative and semi-quantitative analysis of the captured particle images were inconclusive as to improvements in ignition distance

Substratum selection in coral reef sponges and their interactions with other benthic organisms

Substratum preferences and contact interactions among sessile organisms can be a major determinant of biotic gradients in the structure of benthic communities on coral reefs. Sponges are a substantial component of these communities, but their substratum requirements and interactions with other benthic taxa are poorly understood. Here, we quantified sponge substratum preferences and interactions from 838 randomly selected photo-quadrats across different depths (5, 10 and 15 m), exposure (sheltered and exposed), and substratum topography (horizontal, inclined and vertical surfaces) on coastal coral reefs in Kimbe Bay. A high proportion (55%) of sponge colonies were associated with dead coral, unconsolidated coral rubble (7%) and calcium carbonate rock (CaCO3 rock) (7%), even though they represented only 10%, 4% and 1% of the available substratum, respectively. Sponges interacted most frequently with algae (~ 34%), corals (~ 30%) and crustose coralline algae (CCA ~ 19%) that represented ~ 46%, ~ 18% and ~ 14% of the substratum cover, respectively. The microhabitat preferences of sponges and frequency of interactions with other taxa were mostly consistent across various exposure, depth and substratum topography conditions. Most interactions appeared to be “stand-offs” (71%) which are interactions with no clear winner or loser. However, when overgrowth occurred, sponges were usually winners, overgrowing corals (92%), CCA (81%) and macroalgae (65%). Three sponge species Dysidea sp1, Lamellodysidea cf. chlorea and Lamellodysidea chlorea accounted for 51% to 96% of the overgrowth of sponges over algae, corals and CCA, but there was no one species found to always win or lose. Our results suggest that sponges avoid other biological substrata by preferentially settling on dead coral, coral rubble and CaCO3 rock, but when they do come into contact with algae and corals, they frequently overgrow their spacial competitors

Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating their downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and thus can be used to automatically annotate large datasets. As part of the effort to enrich public data available within NCI Imaging Data Commons (IDC), here we introduce AI-generated annotations for two collections of computed tomography images of the chest, NSCLC-Radiomics, and the National Lung Screening Trial. Using publicly available AI algorithms we derived volumetric annotations of thoracic organs at risk, their corresponding radiomics features, and slice-level annotations of anatomical landmarks and regions. The resulting annotations are publicly available within IDC, where the DICOM format is used to harmonize the data and achieve FAIR principles. The annotations are accompanied by cloud-enabled notebooks demonstrating their use. This study reinforces the need for large, publicly accessible curated datasets and demonstrates how AI can be used to aid in cancer imaging

Electrospun gelatin-based scaffolds as a novel 3D platform to study the function of contractile smooth muscle cells in vitro

Contractile dysfunction of smooth muscle (SM) is a feature of chronic cardiovascular, respiratory and gastro-intestinal diseases. Owing to the low availability of human ex vivo tissue for the assessment of SM contractile function, the aim of this study was to develop a novel in vitro SM model that possesses the ability to contract, and a method to measure its contractility. A range of electrospun scaffolds were produced from crosslinked gelatin and methacrylated gelatin (GelMA), generating highly aligned scaffolds with average fibre diameters ranging from 200 nm to several micrometres. Young's moduli of the scaffolds ranged from 1x105 to 1x107 Pa. Primary aortic smooth muscle cells (AoSMCs; rat) cells readily adhered to and proliferated on the fibrous scaffolds for up to 10 days. They formed highly aligned populations following the topographical cues of the aligned scaffolds and stained positive for SM markers, indicating a contractile phenotype. Cell-seeded GelMA scaffolds were able, upon stimulation with uridine 5'-triphosphate (UTP), to contract and their attachment to a force transducer allowed the force of contraction to be measured. Hence, these electrospun GelMA fibres can be used as biomimetic scaffolds for SM cell culture and in vitro model development, and enables the contractile forces generated by the aligned three-dimensional sheet of cells to be directly measured. This will supplement in vitro drug screening tools and facilitate discovery of disease mechanisms

Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology

Machine learning is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but their lack of interoperability has been a major barrier for clinical integration and evaluation. The DICOM a standard specifies Information Object Definitions and Services for the representation and communication of digital images and related information, including image-derived annotations and analysis results. However, the complexity of the standard represents an obstacle for its adoption in the ML community and creates a need for software libraries and tools that simplify working with data sets in DICOM format. Here we present the highdicom library, which provides a high-level application programming interface for the Python programming language that abstracts low-level details of the standard and enables encoding and decoding of image-derived information in DICOM format in a few lines of Python code. The highdicom library ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, highdicom achieves interoperability with the medical imaging systems that hold the data used to train and run ML models, and ultimately communicate and store model outputs for clinical use. We demonstrate through experiments with slide microscopy and computed tomography imaging, that, by bridging these two ecosystems, highdicom enables developers to train and evaluate state-of-the-art ML models in pathology and radiology while remaining compliant with the DICOM standard and interoperable with clinical systems at all stages. To promote standardization of ML research and streamline the ML model development and deployment process, we made the library available free and open-source

A Deep HST Search for Escaping Lyman Continuum Flux at z~1.3: Evidence for an Evolving Ionizing Emissivity

We have obtained deep Hubble Space Telescope far-UV images of 15 starburst galaxies at z~1.3 in the GOODS fields to search for escaping Lyman continuum photons. These are the deepest far-UV images m_{AB}=28.7, 3\sigma, 1" diameter) over this large an area (4.83 arcmin^2) and provide the best escape fraction constraints for any galaxy at any redshift. We do not detect any individual galaxies, with 3\sigma limits to the Lyman Continuum (~700 \AA) flux 50--149 times fainter (in f_nu) than the rest-frame UV (1500 \AA) continuum fluxes. Correcting for the mean IGM attenuation (factor ~2), as well as an intrinsic stellar Lyman Break (~3), these limits translate to relative escape fraction limits of f_{esc,rel}<[0.03,0.21]. The stacked limit is f_{esc,rel}(3\sigma)<0.02. We use a Monte Carlo simulation to properly account for the expected distribution of IGM opacities. When including constraints from previous surveys at z~1.3 we find that, at the 95% confidence level, no more than 8% of star--forming galaxies at z~1.3 can have relative escape fractions greater than 0.50. Alternatively, if the majority of galaxies have low, but non-zero, escaping Lyman Continuum, the escape fraction can not be more than 0.04. Both the stacked limits, and the limits from the Monte Carlo simulation suggest that the average ionizing emissivity (relative to non-ionizing UV emissivity) at z~1.3 is significantly lower than has been observed in Lyman Break Galaxies (LBGs) at z~3. If the ionizing emissivity of star-forming galaxies is in fact increasing with redshift, it would help to explain the high photoionization rates seen in the IGM at z>4 and reionization of the intergalactic medium at z>6. [Abridged]Comment: Submitted to ApJ (Nov. 6) Comments Welcome. 11 pages, 8 figure