Artificial Enrichment Research Articles

Probing Short-Range Interactions in Isostructural Hydrate and Perhydrate of Dabrafenib by Magic-Angle Spinning Solid-State NMR.

Dabrafenib (DBF), an anticancer drug, exhibits isostructural properties in its hydrate (DBF⊃H2O) and perhydrate (DBF⊃H2O2) forms, as revealed by single-crystal X-ray diffraction. Despite the H2O and H2O2 solvent molecules occupying identical locations, the two polymorphs have different thermal behaviors. In general, determination of stoichiometry of H2O in the perhydrate crystals is difficult due to the presence of both H2O and H2O2 in the same crystal voids. This study utilizes magic-angle spinning (MAS) solid-state NMR (SSNMR) combined with gauge-included projector augmented wave calculations to characterize the influence of solvent molecules on the local environment in pseudopolymorphs. SSNMR experiments were employed to assign 1H, 13C, and 15N peaks and identify spectral differences in the isostructural pseudopolymorphs. Proton spectroscopy at fast MAS was used to identify and quantify H2O2/H2O in DBF⊃H2O2 (mixed hydrate/perhydrate). 1H-1H dipolar-coupling-based experiments were recruited to confirm the 3D molecular packing of solvent molecules in DBF⊃H2O and DBF⊃H2O2. Homonuclear (1H-1H) and heteronuclear (1H-14N) distance measurements, in conjunction with diffraction structures and optimized hydrogen atom positions by density functional theory, helped decipher local interactions of H2O2 with DBF and their geometry in DBF⊃H2O2. This integrated X-ray structure, quantum chemical calculations, and NMR study of pseudopolymorphs offer a practical approach to scrutinizing crystallized solvent interactions in the crystal lattice even without high-resolution crystal structures or artificial sample enrichment.

Deep reinforcement learning enables better bias control in benchmark for virtual screening

Virtual screening (VS) has been incorporated into the paradigm of modern drug discovery. This field is now undergoing a new wave of revolution driven by artificial intelligence and more specifically, machine learning (ML). In terms of those out-of-the-box datasets for model training or benchmarking, their data volume and applicability domain are limited. They are suffering from the biases constantly reported in the ML application. To address these issues, we present a novel benchmark named MUBDsyn. The utilization of synthetic decoys (i.e., presumed inactives) is the main feature of MUBDsyn, where deep reinforcement learning was leveraged for bias control during decoy generation. Then, we carried out extensive validations on this new benchmark. First, we confirmed that MUBDsyn was superior to the classical benchmarks in control of domain bias, artificial enrichment bias and analogue bias. Moreover, we found that the assessment of ML models based on MUBDsyn was less biased as revealed by the analysis of asymmetric validation embedding bias. In addition, MUBDsyn showed better setting of benchmarking challenge for deep learning models compared with NRLiSt-BDB. Overall, we have proven that MUBDsyn is the close-to-ideal benchmark for VS. The computational tool is publicly available for the easy extension of MUBDsyn.

Microbiological Parameters of Sod-Podzolic Soil and Rhizosphere in a Half-Century Field Experiment with Different Fertilizer Systems

The regular application of mineral and organic fertilizers is one of the essential components of the agricultural intensification. The applied fertilizers lead to artificial enrichment of the soil environment with readily available substrates and biophilic elements, which can have a significant impact on the soil and rhizosphere microbiome. The aim of the study was to investigate the effect of different fertilizer systems on the quantitative parameters of various microbial groups in soddy-podzolic soil (Umbric Albic Retisol) and the rhizosphere of potatoes and barley. The study was carried out on a long-term field experiment, in which mineral (NPK), organic (manure) and mixed (NPK + manure) fertilizer systems have been applied since 1968. The application of organic fertilizers increased the microbial biomass carbon (Cmic) in the bulk soil and the rhizosphere by 25–100% compared to the soil without fertilizers, while the use of mineral fertilizers, on the contrary, decreased it by 10–30%. Basal respiration and respiratory coefficient (qСО2) increased in the row: without fertilizers NPK NPK + manure manure. The gene copies number of bacteria, archaea, and fungi significantly increased (1.5–2.5 times) under organic fertilizers and decreased (2–2.5 times) under NPK. The varied from 32 to 100 and from 0.10 to 0.92 by the luminescent microscopy and quantitative PCR, respectively. The lowest fungi/bacteria ratios were revealed for variants with NPK, and the highest fungi/bacteria ratios were in variants with manure. Thus, the applied doses of mineral fertilizers must be compensated by the introduction of fresh organic matter in order to maintain the stability of the soil-microbe-plant system.

Abstract LB170: Artificial intelligence-assisted morphology-based detection and enrichment of malignant effusion tumor cells as a method for molecular profiling

Abstract Patients with metastatic breast cancer (MBC) can develop malignant effusions (MEs) when excess fluid and tumor cells accumulate in the pleural or peritoneal space. MEs are associated with significant morbidity and mortality. Tropism of tumor cells in these spaces is poorly understood and tumor cells residing in these fluids are difficult to study with traditional methods due to low abundance. To overcome this limitation and understand the pathobiology of MEs in MBCs, we applied a label-free method to isolate tumor cells based on morphology using an artificial intelligence (AI) model This method enabled isolation of live cells for downstream molecular analysis and characterization of morphological heterogeneity. We collected ME samples from 9 MBC patients with cytology-confirmed MEs, including 3 triple negative and 6 hormone receptor-positive/HER2-negative breast cancers. ME cells were imaged and isolated using a Deepcell AI model trained on a separate cohort of MEs to isolate and enrich tumor cells based on deep learning representations of morphological features. Quantitative morphological features were then extracted from cell images to perform cluster analysis and visualized by dimensionality reduction projections. In parallel, flow cytometry (FC) was used to quantify % EpCAM+/Claudin4+ tumor cells in ME samples. Copy number (CN) analysis was performed before and after enrichment with as few as 200 cells from each sample. Tumor cell fraction (TCF) estimates were calculated from the AI classifier, FC, and CN analyses. CN profiling of malignant cells sorted by the AI classifier revealed genomic alterations common in breast cancer in 7 of 9 patients. We observed increased amplitude of genomic alterations in tumor-enriched vs. unenriched samples, indicating successful tumor cell enrichment in 14 out of 16 samples. We observed a significant correlation in TCF between the AI classifier and FC with a Spearman test (rho=0.568, p0.008). In some cases, the AI classifier reported higher TCF than FC. This could indicate that the AI model detected tumor cells missed by antibody labeling or false positives from morphologically similar cell types, such as macrophages and mesothelial cells. Remarkably, analysis of serial ME samples from the same patient revealed morphological shifts in cell populations which were consistent with changes in TCF by CN analysis. We demonstrate the feasibility of label-free identification and isolation of tumor cells in MEs using an AI classifier. CN profiling of isolated tumor cells verified enrichment from low initial frequencies. Future work will include expression analysis of ME tumor and immune cells, and analysis of organoids derived from MEs. Combining molecular and morphological profiling of MEs will enable new insights into their pathobiology and inform targeted treatment strategies for patients with this condition. Citation Format: Mark J. Magbanua, Laura Huppert, Janifer Cruz, Jackson Goudreau, Shaopu Zhou, Keyi Yin, Pavel Stejskal, Ryan Carelli, Julie Kim, Linda Hsie, Maggie Wang, Zhouyang Lian, Michael Phelan, Manisha Ray, Chassidy Johnson, Amy L. Delson, Ron Balassanian, Jennifer Rosenbluth, Laura van't Veer, Michelle Melisko, Hope S. Rugo, Mahyar Salek, Maddison Masaeli, Hani Goodarzi. Artificial intelligence-assisted morphology-based detection and enrichment of malignant effusion tumor cells as a method for molecular profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB170.

Spawning substrates of cuttlefish: Type, function, and application

The fertilized eggs of cuttlefish are sticky eggs. Cuttlefish parents prefer to lay eggs on the attached substrates, which help increase the number of eggs and the hatching rate of fertilized eggs. If egg-attached substrates are sufficient, cuttlefish spawning will be reduced or even delayed. With the advances in the construction of marine nature reserves and research on artificial enrichment techniques, domestic and international experts have conducted research on different types and configurations of attachment substrates around cuttlefish resource enhancement. Based on the source of the substrates, we classified cuttlefish spawning substrates into two types, natural and artificial. By summarizing the differences, advantages, and disadvantages of the common economic cuttlefish spawning substrates in offshore areas worldwide, we sort out the functions of two different types of attachment bases, and discussed the practical applications of natural and artificial egg-attached substrates in spawning ground restoration and artificial enrichment. We proposed several thoughts on the future research directions of cuttlefish spawning attachment substrates, aiming to provide reasonable suggestions for cuttlefish habitat restoration, cuttlefish breeding and sustainable development of fishery resources.

Eutrophication of Arable Soil: The Comparative Effect of Mineral and Organic Fertilizer Systems

Agrogenic eutrophication refers to the artificial enrichment of soil with organic carbon and nutrients in result of applying mineral and organic fertilizers to increase soil fertility and plant productivity. Eutrophication of gray forest soils (Luvic Retic Greyzemic Phaeozems (Loamic)) was created by annual application of increasing doses of mineral (N 90–360, P2O5 75–300 and K2O 100–400 kg/ha) and organic (fresh cattle manure from 25 to 100 t/ha) fertilizers under plants of a 5–field crop rotation for 9 years in microplots experiment. The NPK amounts applied with the manure were approximately equal to the corresponding doses of mineral fertilizer. The rates of soil enrichment by Corg under mineral and organic fertilizer systems were, respectively, 0.29–0.38 and 0.76–1.56 g/kg per year, Ntot – 0.04–0.06 and 0.06–0.09 g/kg per year, available P2O5 – 4–57 and 11–55 mg/kg per year, available K2O – 5–44 and 6–31 mg/kg per year. The fertilizer doses under both systems were the most significant factor in the accumulation of nitrate nitrogen, available forms of phosphorus and potassium in the soil, while the Ntot contents was controlled by the duration of fertilizer application. The Corg content in the soil with organic and mineral fertilizers depended on the dose of manure and the duration of NPK application, respectively. Soil eutrophication with mineral fertilizers was accompanied by a decrease in soil pH, and eutrophication with organic fertilizers, on the contrary, led to an increase in pH. It is emphasized that over–fertilization and long–term use of fertilizers are the main factors in the development of soil eutrophication and the concomitant change in soil pH.

Systems biology and artificial intelligence analysis highlights the pleiotropic effect of IVIg therapy in autoimmune diseases with a predominant role on B cells and complement system.

Intravenous immunoglobulin (IVIg) is used as treatment for several autoimmune and inflammatory conditions, but its specific mechanisms are not fully understood. Herein, we aimed to evaluate, using systems biology and artificial intelligence techniques, the differences in the pathophysiological pathways of autoimmune and inflammatory conditions that show diverse responses to IVIg treatment. We also intended to determine the targets of IVIg involved in the best treatment response of the evaluated diseases. Our selection and classification of diseases was based on a previously published systematic review, and we performed the disease characterization through manual curation of the literature. Furthermore, we undertook the mechanistic evaluation with artificial neural networks and pathway enrichment analyses. A set of 26 diseases was selected, classified, and compared. Our results indicated that diseases clearly benefiting from IVIg treatment were mainly characterized by deregulated processes in B cells and the complement system. Indeed, our results show that proteins related to B-cell and complement system pathways, which are targeted by IVIg, are involved in the clinical response. In addition, targets related to other immune processes may also play an important role in the IVIg response, supporting its wide range of actions through several mechanisms. Although B-cell responses and complement system have a key role in diseases benefiting from IVIg, protein targets involved in such processes are not necessarily the same in those diseases. Therefore, IVIg appeared to have a pleiotropic effect that may involve the collaborative participation of several proteins. This broad spectrum of targets and ‘non-specificity’ of IVIg could be key to its efficacy in very different diseases.

Artificial Intelligence Based Zero Trust Network

Model-based security metrics are an emerging topic of cyber security research that focuses on assessing an information system's risk exposure. We propose an end-to-end solution with the deployment of a zero-trust network utilising Artificial Intelligence in this article to understand the security posture of a system before it is rolled out and as it matures. The major part contains a discussion about the key methods and techniques which was utilized in the development process and simplified operation principles of each developed process. Some developed processes were tested practically to evaluate the problems in the processes. Modules for automatic processing and data analysis were also developed. These modules can be connected in case it is needed. The most important data collection methods were benchmarked to detect problematic situations in the operation in different realistic situations. With the perception from the benchmark test, the problematic parts of the data collection were discovered and proposals for the solution were made which could be developed and tested in the next iterations of the development process. Working Artificial intelligence-based detection and data enrichment methods were created. The results of the article allow multiple continuous research and development projects related to data collection and data analysis with statistical and artificial intelligence-based methods.

Effects of Heavy Metal Content on Fungal Community Structure in Urban Soil

As an important part of the soil microbial system, fungi can clearly indicate changes in the soil environment.Human activities in the city can easily affect the soil condition, so the phenomenon of artificial heavy metal enrichment often appears in urban soil. The objective of this study was to analyze the fungal community structure in different urban functional areas and to determine the effect of heavy metal content in different urban functional areas on the soil fungal community structure. This study provides theoretical basis for protecting and repairing the urban soil ecosystem and transforming and improving urban environmental quality. Soil samples from eight sampling sites in five functional areas of Beibei District in Chongqing were taken as the research objects. The diversity and community structure of fungi in soil were studied using high-throughput sequencing technology. The content of Cd and Hg in the soil of different functional areas in Beibei District exceeded the environmental background value of Chongqing. The 20-40 cm and 40-60 cm soil layers of JD were slightly polluted. The 20-40 cm soil layer and 0-20 cm soil layer of JLD and ZYY, respectively, were in the alerting state of heavy metal pollution. The Sobs index, Chao 1 index, and Ace index of most sampling points decreased with the increase in soil depth. The NMDS analysis showed that the composition of fungal communities between the 0-20 cm and 20-40 cm soil depths in both JD and ZYY were quite different. From the perspective of community composition, Ascomycota was the most abundant phylum in the soil, followed by unclassified _k _Fungi and Basidiomycota. At the genus level, unclassified_k_Fungi, unclassified_p_Ascomycota, unclassified_o_Sordariales, Scopuloides, Robillarda, and Dactylonectria had higher abundances. The redundancy analysis (RDA) showed that Pb and Zn had the greatest effect on the samples, and the effect on the fungal community structure was significantly different. This study has deepened the understanding of the relationship between the content of heavy metals in different urban functional areas and the structure of fungal communities and has provided a scientific basis for the rational use and planning of urban soil.

Open set task augmentation facilitates generalization of deep neural networks trained on small data sets

Many application scenarios for image recognition require learning of deep networks from small sample sizes in the order of a few hundred samples per class. Then, avoiding overfitting is critical. Common techniques to address overfitting are transfer learning, reduction of model complexity and artificial enrichment of the available data by, e.g., data augmentation. A key idea proposed in this paper is to incorporate additional samples into the training that do not belong to the classes of the target task. This can be accomplished by formulating the original classification task as an open set classification task. While the original closed set classification task is not altered at inference time, the recast as open set classification task enables the inclusion of additional data during training. Hence, the original closed set classification task is augmented with an open set task during training. We therefore call the proposed approach open set task augmentation. In order to integrate additional task-unrelated samples into the training, we employ the entropic open set loss originally proposed for open set classification tasks and also show that similar results can be obtained with a modified sum of squared errors loss function. Learning with the proposed approach benefits from the integration of additional “unknown” samples, which are often available, e.g., from open data sets, and can then be easily integrated into the learning process. We show that this open set task augmentation can improve model performance even when these additional samples are rather few or far from the domain of the target task. The proposed approach is demonstrated on two exemplary scenarios based on subsets of the ImageNet and Food-101 data sets as well as with several network architectures and two loss functions. We further shed light on the impact of the entropic open set loss on the internal representations formed by the networks. Open set task augmentation is particularly valuable when no additional data from the target classes are available—a scenario often faced in practice.

Zoo Visitor Attitudes Are More Influenced by Animal Behaviour than Environmental Enrichment Appearance.

Simple SummaryEnvironmental enrichment is a combination of techniques that aim to improve the quality of life of zoo animals. However, institutions might be reluctant to add certain enrichment items due to the belief that their presence could negatively affect the visitor experience in the zoo. To explore the veracity of this belief, we assessed visitor attitudes towards two types of enrichment items (naturalistic vs. artificial looking) in an outdoor walk-through enclosure for ring-tailed lemurs in Zoo Planckendael (Belgium). We developed a questionnaire that was answered by 371 visitors. We also took into consideration the behaviour of the animals and their visibility. We found that the visitor attitudes were more influenced by the behaviours displayed by the lemurs than the appearance of the enrichment items. We suggest that more emphasis should be placed on designing enrichment items that provide the animals with opportunities to display more active and appropriate behaviours, regardless of the appearance of the objects, in order to improve animal welfare while simultaneously improving the visitor experience.Decisions on environmental enrichment programmes are sometimes based on the assumption that non-natural or artificial looking items negatively affect visitor experiences. In this study, we developed a questionnaire to assess zoo visitor attitudes towards enrichment appearance in an outdoor walk-through enclosure for ring-tailed lemurs (Lemur catta). Naturalistic and artificial looking enrichment items were alternately provided in the enclosure. A total of 371 visitors filled out the questionnaire: 174 in the naturalistic and 197 in the artificial conditions. Both researchers and visitors conducted behavioural observations of the lemurs. Our results suggest that the appearance of the items did not have an effect on visitor attitudes and that visitors recognised both naturalistic and artificial items as enriching for the animals. Moreover, the behaviour and visibility of the lemurs had a greater effect on the visitors’ attitudes. We suggest that during the design of enrichment items, less concern should be placed on the appearance of the items and more on their effect on animal behaviour. Ultimately, this would improve both animal welfare in captivity and the visitor experience.

Selective 1H–1H recoupling via symmetry sequences in fully protonated samples at fast magic angle spinning

Proton-detected solid-state NMR at fast Magic Angle Spinning (MAS) is becoming the norm to characterize molecules. Routinely 1H–1H and 1H-X dipolar couplings are used to characterize the structure and dynamics of molecules. Selective proton recoupling techniques are emerging as a method for structural characterization via estimation of qualitative and quantitative distances. In the present study, we demonstrate through numerical simulations and experiments that the well-characterized CNvn sequences can also be tailored for selective recoupling of proton spins by employing C elements of the type (β)Φ(4β)Φ+π(3β)Φ. Herein, several CNvn sequences were examined through numerical simulations and experiments. C614 recoupling sequence with a modified POST-element ((β)Φ(4β)Φ+π(3β)Φ) shows selective polarization transfer efficiencies on the order of 40–50% between various proton spin pairs in fully protonated samples at rf amplitudes ranging from 0.3 to 0.8 times the MAS frequency. These selective recoupling sequences have been labeled as frequency-selective-CNvn sequences. The extent of selectivity, polarization transfer efficiency and the feasibility of experimentally measuring proton-proton distances in fully protonated samples are explored here. The development of efficient and robust selective 1H–1H recoupling experiments is required to structurally characterize molecules without artificial isotope enrichment or the need for diffracting crystals.

Preference for artificial range enrichment design features in free-range commercial laying hens

ABSTRACT 1. This study examined what elements of artificial enrichment structures attract hens in the outdoor range, and what behaviours hens perform around these structures. Three principles of cover design (height, orientation and visual density) were tested for laying hen preference in the outdoor range of a commercial egg farm using a 2 × 3 × 3 factorial design. The factors were height: 0.5 m or 1.5 m; orientation: vertical, horizontal or horizontal cover with one vertical side; and visual density: 0% (control), 50% or 90% UV blocking cloth. 2. A significant three-way interaction between all factors was found (P < 0.001). The most preferred visual density of these structures was the 90%, followed by 50% UV blocking cloth. Horizontal structures with one vertical side, of either height, were highly preferred. Short horizontal structures were preferred to tall ones, and tall vertical structures were slightly preferred over short ones. 3. The most common behaviours observed around the structures were interaction with the structure (pecks, scratches and vigilance directed at the structure; 32.7%), foraging (32.2%), locomotion (9.9%), and air foraging (pecks and snapping in the air; 9.0%). A three-way interaction between the design principles influenced the proportion of hens performing each of these behaviours (P < 0.001). Hens foraged more around structures with no shade cloth and interacted more with structures that had dense shade cloth. 4. These results highlighted the complexity of designing attractive outdoor environments for laying hens. By testing the combination of elements that hens prefer it is possible to understand how to design attractive outdoor ranges that attract hens and promote a diverse range of behaviours on commercial farms.

RELATIONSHIPS BETWEEN FRUGIVOROUS- BIRDS AND THEIR HABITATS IN FRAGMENTED FOREST AREAS NEAR TO VILLAVICENCIO CITY, VILLAVICENCIO, META, COLOMBIA.

The degradation of the Llanos foothill ecosystems is one of the biggest ecological problems in the areas surrounding the city of Villavicencio, Meta. The generation of fragments produces new conditions for the establishment of bird guilds. This study was carried out during 2017 - 2018 in areas of the campus Loma Linda at the Santo Tomas University. It was evaluated whether the forest interior, forest border and grassland areas constitute habitats that support the establishment of frugivorous birds. The key factors that affect the richness, diversity and abundance of bird species were identified, from which recommendations were made for the establishment of conservation measures. The richness of birds and their abundance was positively correlated with intermediate values of plant richness and diversity and with the presence and coverage of species of palms and lianas. The abundance and diversity of frugivorous birds were higher in border areas and lower in grasslands and forest interiors. Apparently the diversity of the habitat in border areas of fragmented lands increases the diversity of frugivorous birds through an increase in the abundance values. Artificial enrichment is recommended with different tree, shrubs and palms species planted in the edge and interior areas of the forest, and the maintenance of remaining trees and shrubs in the areas of shrub grasslands. Keywords : fragmentation, frugivorous-birds, diversity, natural regeneration.

Determination of the Chemical Compositions of Fine titanium Carbide and Niobium Carbide Precipitates in Isothermally Aged Ferritic Steel by Atom Probe Tomography Analysis.

The carbon (C) ratios, namely the atomic ratios of C/(C + M), in nano-sized coherent MC precipitates (M = Ti, Nb) with the NaCl-type (B1) structure in ferritic steels, which had been isothermally aged at 580 °C, were investigated using atom probe tomography (APT). Considering the influences of the trajectory aberration, detection loss, and peak overlap, we determined the C ratios to be ~0.40 and ~0.45 for an equivalent volume diameter of 1.5–5 nm and 1–5 nm for the TiC and NbC precipitates, respectively, suggesting that there is a considerable fraction of C vacancies in both nano-sized precipitates. The apparent C ratios show significant scatter with decreasing particle size, while the apparent mean C ratios of very fine TiC particles, smaller than 1.5 nm, decreased with decreasing particle size. With the use of one of the latest APT instruments with a high detection efficiency, the scattering in the apparent C ratios was reduced because the counting statistics were improved; however, the artificial enrichment of C atoms to particular crystallographic directions of ferrite hindered the determination of the C ratio for very fine TiC particles smaller than 1.5 nm.

CFD analysis for evaluating and optimizing spatial distribution of CO2 concentration in a strawberry greenhouse under different CO2 enrichment methods

A deeper understanding of the spatial distribution of CO2 concentration inside cultivation facilities (e.g., greenhouses and plant factories) is necessary for the quantitative evaluation and/or the improvement in the performance and efficiency of artificial CO2 enrichment methods. We developed 3D computational fluid dynamics (CFD) models for two CO2 enrichment methods in a strawberry greenhouse – one was for overall greenhouse enrichment, as the usual method, and the other was for localized crop enrichment. We confirmed the model validation by comparing the simulated and measured CO2 concentrations in the greenhouse. With regard to the entire enrichment condition, CO2 accumulated at the upper part of the greenhouse through convection due to the high temperature of CO2 generated while there was low CO2 concentration around the crops. This could be regarded as an ineffectual situation. However, such situation can be alleviated by lowering the temperature of CO2. On the other hand, we found that crop-local enrichment can increase CO2 concentration only around crops albeit with less effect on those at the upper part. Thus, changing the enrichment strategy as well as the equipment could greatly optimize the distribution of CO2 in a greenhouse, thereby improving the efficiency of CO2 enrichment.

Severe deactivation and artificial enrichment of thallium on commercial SCR catalysts installed in cement kiln

Thallium (Tl) poisoning is reported for the first time occurring on selective catalytic reduction (SCR) catalysts running in a certain cement kiln. NOx conversion of the poisoned catalyst is almost lost with 6.4 % Tl loading even at 400 °C. Besides the decrease of the BET surface area, Tl significantly decreases the oxidation ability of the active sites due to the coverage effect. Tl is present as both Tl2O3 and Tl2SO4 and covers both the Lewis and Brønsted acid sites of V2O5 and the Ti(OH)Ti bridging sites of TiO2, leading to a marked decrease in the quantity and strength of acid sites. Similar to the regeneration of alkali-poisoning catalysts, dilute H2SO4 is also effective to remove partial surface Tl2O3, by which yielded 89 % of Tl removal with a 0.4 M H2SO4. However, the SCR performance could not total recover because of the remnant Tl2O3 on the catalyst regenerated only once.

High and reversible oxygen uptake in carbon dot solutions generated from polyethylene facilitating reactant-enhanced solar light harvesting.

Solar-driven photocatalysis is emerging as a key chemical transformation strategy due to its favourable energy economy. However, in photocatalytic oxidation reactions where molecular oxygen (O2) is a reactant, achieving higher efficiency requires an O2-saturated environment in order to maintain a high oxygen level on the catalyst surface, necessitating an additional energy-consuming step of O2 separation from air. Here we show that in the presence of carbon quantum dots (CQDs), the oxygen content and the ability of O2 to diffuse in water increase significantly. We first demonstrate a novel strategy to convert several grams of polyethylene, a stubborn pollutant, into highly photoactive CQDs by stepwise dehydrogenation and graphitization. In a typical CQD concentration of ∼1 mg ml-1, the oxygen level in water reaches ∼640 μM, double that of pure water inferring an extremely high O2 content of ∼1 wt% associated with CQDs under ambient conditions. Therefore, when the CQDs were used to catalyze photo-oxidation of aromatic alcohols by sunlight, the efficiency was found higher than previous instances despite those employing high oxygen pressure, temperature and expensive materials. Besides waste polyethylene utilization, the uniqueness of oxygen enrichment in CQD solutions may offer immense prospects including those in photo-oxidation reactions.

Individual tree context and contrast dictate tree physiological features and arthropod biodiversity patterns across multiple trophic levels

1. The present study tested the hypothesis that tree context (natural, semi‐natural or planted) and contrast (height of surrounding vegetation) affect tree physiological characters (leaf size, nutrient content, and stress‐related factors), and also alter the arthropod biodiversity patterns either directly or indirectly.2. Arthropods were collected from tree canopies using chemical fogging from the ecologically important South African native tree Podocarpus elongatus.3. Low contrast trees had significantly larger leaves than those in high contrast environments and harboured an overall higher richness, abundance, and biomass of arthropods, although this was guild‐dependent.4. Trees in natural contexts had less foliar δ13C, suffered less from artificial nitrogen enrichment, and harboured significantly higher herbivore and predator arthropod richness and unique assemblages, compared with planted trees.5. Semi‐natural trees supported natural levels of arthropod richness, although these were mostly generalist species.6. Tree context and contrast can therefore dictate associated biota at multiple trophic levels and native trees may fail to maintain natural biodiversity in transformed landscapes.

Measurement precision and accuracy of high artificial enrichment 15 N and 13 C tracer samples.

Oversaturation of the Faraday cup amplifiers of isotope ratio mass spectrometers when using tracers that are highly enriched in heavier isotopes (up to 99.9%) remains a major bottleneck to obtaining high-precision measurements. The memory effect plays a key role in reducing tracer sample measurement precision and accuracy. Several sample preparation approaches are known to reduce memory effects and to improve tracer sample measurement precision. However, the potential benefits when using very high enrichment tracer samples (> +1000 mUr) have not been tested. In this study, we test how specific sample positioning for measurements and frequent use of natural isotope abundance reference materials within the sequence affects the precision and accuracy of isotopic ratio analyses when using a Flash elemental analyser coupled to a Deltaplus XP isotope ratio mass spectrometer for very high enrichment (> +22000 mUr) 15 N tracer sample measurements. Furthermore, we investigate if tracer sample dilution with natural isotope abundance materials reduces memory effects and increases measurement precision and accuracy when measurements of high-enrichment 15 N and 13 C biomass tracer samples are conducted. Frequent use of natural isotope abundance materials and specific positioning increased 15 N tracer sample precision, but it had a negative effect on the precision of quality control substances. 15 N and 13 C tracer sample dilution improved measurement precision by a maximum of ±0.9 mUr; however, a strong linear relationship between the original and the calculated φ values was found. Highly enriched 15 N tracer samples caused a maximum memory effect of 0.11%. High levels of 15 N abundance within the samples affected measurement accuracy by an average of 6.7%. We conclude that highly enriched tracer samples do not require dilution before analysis. Tracer sample precision can be improved by using a specific measurement order of expected isotope abundance and by the frequent use of natural abundance reference materials.