Low-temperature redox activity and alcohol ammoxidation performance on Cu- and Ru-incorporated ceria catalysts.

Transition-metal-incorporated cerium oxides with Cu and a small amount of Ru (Cu0.18Ru0.05CeOz) were prepared, and their low-temperature redox performance (<423 K) and catalytic alcohol ammoxidation performance were investigated. Temperature-programmed reduction/oxidation under H2/O2 and in situ X-ray absorption fine structure revealed the reversible redox behavior of the three metals, Cu, Ru, and Ce, in the low-temperature redox processes. The initially reduced Ru species decreased the reduction temperature of Cu oxides and promoted the activation of Ce species. Cu0.18Ru0.05CeOz selectively catalyzed the production of benzonitrile in the ammoxidation of benzyl alcohol. H2-treated Cu0.18Ru0.05CeOz showed a slightly larger initial conversion of benzyl alcohol than O2-treated Cu0.18Ru0.05CeOz, suggesting that the reduced structure of Cu0.18Ru0.05CeOz was active for the ammoxidation. The integration of both Cu and Ru resulted in the efficient promotion of ammoxidation, in which the Ru species were involved in the conversion of benzyl alcohol and Cu species were required for selective production of benzonitrile.

The adaptability, distribution, ecological function and restoration application of biological soil crusts on metal tailings: A critical review.

A large amount of metal tailings causes many environmental issues. Thus, the techniques for their ecological restoration have garnered extensive attention. However, they are still in the exploratory stage. Biological soil crusts (BSCs) are a coherent layer comprising photoautotrophic organisms, heterotrophic organisms and soil particles. They are crucial in global terrestrial ecosystems and play an equal importance in metal tailings. We summarized the existing knowledge on BSCs growing on metal tailings. The main photosynthetic organisms (cyanobacteria, eukaryotic algae, lichens, and mosses) of BSCs exhibit a high heavy metal(loid) (HM) tolerance. BSCs also have a strong adaptability to other adverse conditions in tailings, such as poor structure, acidification, and infertility. The literature about tailing BSCs has been rapidly increasing, particularly after 2022. The extensive literature confirms that the BSCs distributed on metal tailings, including all major types of metal tailings in different climatic regisions, are common. BSCs perform various ecological functions in tailings, including HM stress reduction, soil structure improvement, soil nutrient increase, biogeochemical cycle enhancement, and microbial community restoration. They interact and accelerate revegetation of tailings (at least in the temperate zone) and soil formation. Restoring tailings by accelerating/inducing BSC formation (e.g., resource augmentation and inoculation) has also attracted attention and achieved small-scale on-site application. However, some knowledge gaps still exist. The potential areas for further research include the relation between BSCs and HMs, large-scale quantification of tailing BSCs, application of emerging biological techniques, controlled laboratory experiments, and other restoration applications.

Vanadium Alumanyl Complex: Synthesis, Characterization, Reactivity, and Application as a Catalyst for C-H Alumanylation of Alkenes.

A complex containing a V-Al bond is described. This species can be prepared by either transmetalation of a previously disclosed alumanylpotassium with Cp2VCl or photolytic oxidative alumination of Cp2V using the corresponding dialumane. Reaction of the resulting V-Al complex with H2 gave a Cp2V-dihydridoaluminate complex. These complexes were studied with X-ray crystallography, vanadium K-edge XANES spectroscopy, and DFT calculations. Finally, the reactivity of these molecules was studied opening the way to a catalytic C-H alumanylation of alkenes.

Microalgae cultivation with recycled harvesting water achieved economic and sustainable production of biomass and lipid: Feasibility assessment and inhibitory factors analysis.

This study was conducted to achieve economic and sustainable production of biomass and lipids from Chlorella sorokiniana by recirculating cultivation with recycled harvesting water, to identify the major inhibitory factors in recirculating culture, and to analyze accordingly economic benefits. The results showed that recirculating microalgae cultivation (RMC) could obtain 0.20-0.32 g/L biomass and lipid content increased by 23.1 %-38.5 %. Correlation analysis showed that the extracellular polysaccharide (PSext), chemical oxygen demand (COD) and chromaticity of recirculating water inhibited photosynthesis and induced oxidative stress, thus inhibiting the growth of C. sorokiniana. In addition, the economic benefits analysis found that circulating the medium twice could save about 30 % of production cost, which is the most economical RMC solution. In conclusion, this study verified the feasibility and economy of RMC, and provided a better understanding of inhibitory factors identification in culture.

Mechanochemical Synthesis of Non-Solvated Dialkylalumanyl Anion and XPS Characterization of Al(I) and Al(II) Species.

A non-solvated alkyl-substituted Al(I) anion dimer was synthesized by a reduction of haloalumane precursor using a mechanochemical method. The crystallographic and theoretical analysis revealed its structure and electronic properties. Experimental XPS analysis of the Al(I) anions with reference compounds revealed the lower Al 2p binding energy corresponds to the lower oxidation state of Al species. It should be emphasized that the experimentally obtained XPS binding energies were reproduced by delta SCF calculations and were linearly correlated with NPA charges and 2p orbital energies.

Biochemical responses of freshwater microalgae Chlorella sorokiniana to combined exposure of Zn(â ¡) and estrone with simultaneous pollutants removal.

With the development of livestock industry, contaminants such as divalent zinc ions (Zn (Ⅱ)) and estrone are often simultaneously detected in livestock wastewater. Nevertheless, the combined toxicity of these two pollutants on microalgae is still unclear. Moreover, microalgae have the potential for biosorption and bioaccumulation of heavy metals and organic compounds. Thus, this study investigated the joint effects of Zn (Ⅱ) and estrone on microalgae Chlorella sorokiniana, in terms of growth, photosynthetic activity and biomolecules, as well as pollutants removal by algae. Interestingly, a low Zn (Ⅱ) concentration promoted C. sorokiniana growth and photosynthetic activity, while the high concentration experienced inhibition. As the increase of estrone concentration, chlorophyll a content increased continuously to resist the environmental stress. Concurrently, the secretion of extracellular polysaccharides and proteins by algae increased with exposure to Zn (Ⅱ) and estrone, reducing toxicity of pollutants to microalgae. Reactive oxygen species and superoxide dismutase activity increased as the increase of pollutant concentration after 96 h cultivation, but high pollutant concentrations resulted in damage of cells, as proved by increased MDA content. Additionally, C. sorokiniana displayed remarkable removal efficiency for Zn (Ⅱ) and estrone, reaching up to 86.14% and 84.96% respectively. The study provides insights into the biochemical responses of microalgae to pollutants and highlights the potential of microalgae in pollutants removal.

Propylene glycol-mixed steam enhanced extraction for an efficient and sustainable remediation of PAHs-contaminated soil.

An innovative thermal desorption method, propylene glycol (PG)-mixed steam enhanced extraction, is proposed for a highly efficient remediation of polycyclic aromatic hydrocarbons (PAHs)-contaminated soil. It is found that injecting PG-mixed steam into soil column could obtain > 99% removal efficiencies of PAHs either for the pyrene-spiked soil, or for the contaminated field soil with high-molecular-weight PAHs. PG is a safe and low-cost dihydric alcohol with a boiling point higher than water. When the PG-mixed steam penetrated the contaminated soil, the PG vapor preferentially condensed to form a hot liquid with concentrated PG (e.g., from 30 wt% PG in gas phase to 90 wt% PG in the liquid phase), which would significantly solubilize the PAHs and enhance their desorption from soils. The results also revealed that the effluents derived from the PG-mixed steam could be purified by removing the desorbed PAHs using a simple coagulation treatment, and the recovered PG solution could be reused. The plant assay using wheat seeds showed that the remediated soil had a good regreening potential. Our results demonstrate that PG-mixed steam injection is a promising thermal desorption method for an efficient and sustainable remediation of PAHs-contaminated soil.

Characterization and genome analysis of Acinetobacter oleivorans S4 as an efficient hydrocarbon-degrading and plant-growth-promoting rhizobacterium.

Plant-growth-promoting rhizobacteria (PGPR) have received increasing attention for assisting phytoremediation. However, the effect of PGPR on total petroleum hydrocarbon (TPH) degradation and plant growth promotion and its underlying mechanism is not well understood. In this study, phenotypic analysis and whole genome sequencing were conducted to comprehensively characterize a newly isolated rhizobacterium strain S4, which was identified as Acinetobacter oleivorans, from a TPH-contaminated soil. The strain degraded 62.5% of initially spiked diesel (1%) in minimal media within six days and utilized n-alkanes with a wide range of chain length (i.e., C12 to C40). In addition, the strain showed phenotypic traits beneficial to plant growth, including siderophore production, indole-3-acetic acid synthesis and phosphate solubilization. Potential metabolic pathways and genes encoding proteins responsible for the phenotypic traits were identified. In a real TPH-contaminated soil, inoculation of Acinetobacter oleivorans S4 significantly enhanced the growth of tall fescue relative to the soil without inoculation. In contrast, inoculation of Bacillus sp. Z7, a hydrocarbon-degrading strain, showed a negligible effect on the growth of tall fescue. The removal efficiency of TPH with inoculation of Acinetobacter oleivorans S4 was significantly higher than those without inoculation or inoculation of Bacillus sp. Z7. These results suggested that traits of PGPR beneficial to plant growth are critical to assist phytoremediation. Furthermore, heavy metal resistance genes and benzoate and phenol degradation genes were found in the genome of Acinetobacter oleivorans S4, suggesting its application potential in broad scenarios.

A Unified Visual Information Preservation Framework for Self-supervised Pre-Training in Medical Image Analysis.

Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital in medical image analysis (e.g., image-based diagnosis and tumor segmentation). To mitigate the locality problem of comparative SSL, we propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding but has not drawn much attention in SSL. The resulting framework can be formulated as a multi-task optimization problem on the feature pyramid. Specifically, we conduct multi-scale pixel restoration and siamese feature comparison in the pyramid. In addition, we propose non-skip U-Net to build the feature pyramid and develop sub-crop to replace multi-crop in 3D medical imaging. The proposed unified SSL framework (PCRLv2) surpasses its self-supervised counterparts on various tasks, including brain tumor segmentation (BraTS 2018), chest pathology identification (ChestX-ray, CheXpert), pulmonary nodule detection (LUNA), and abdominal organ segmentation (LiTS), sometimes outperforming them by large margins with limited annotations. Codes and models are available at https://github.com/RL4M/PCRLv2.

Highly Selective Reduction of Nitrate by Zero-Valent Aluminum (ZVAI) Ball-Milled Materials at Circumneutral pH: Important Role of Microgalvanic Cells for Depassivation of ZVAl and N2-Selectivity.

The passivation of zero-valent aluminum (ZVAl) limits its application in environmental remediation. Herein, a ternary composite material Al-Fe-AC is synthesized via a ball-milling treatment on a mixture of Al0, Fe0, and activated carbon (AC) powders. The results show that the as-prepared micronsized Al-Fe-AC powder could achieve highly efficient nitrate removal and a nitrogen (N2)-selectivity of >75%. The mechanism study reveals that, in the initial stage, numerous Al//AC and Fe//AC microgalvanic cells in the Al-Fe-AC material could lead to a local alkaline environment in the vicinity of the AC cathodes. The local alkalinity depassivated the Al0 component and enabled its continuous dissolution in the subsequent second stage of reaction. The functioning of the AC cathode of the Al//AC microgalvanic cell is revealed as the primary reason accounting for the highly selective reduction of nitrate. The investigation on the mass ratio of raw materials manifested that an Al/Fe/AC mass ratio of 1:1:5 or 1:3:5 was preferable. The test in simulated groundwater suggested that the as-prepared Al-Fe-AC powder could be injected into aquifers to achieve a highly selective reduction of nitrate to nitrogen. This study provides a feasible method to develop high-performance ZVAl-based remedial materials that could work in a wider pH range.

Relation Matters: Foreground-Aware Graph-Based Relational Reasoning for Domain Adaptive Object Detection.

Domain Adaptive Object Detection (DAOD) focuses on improving the generalization ability of object detectors via knowledge transfer. Recent advances in DAOD strive to change the emphasis of the adaptation process from global to local in virtue of fine-grained feature alignment methods. However, both the global and local alignment approaches fail to capture the topological relations among different foreground objects as the explicit dependencies and interactions between and within domains are neglected. In this case, only seeking one-vs-one alignment does not necessarily ensure the precise knowledge transfer. Moreover, conventional alignment-based approaches may be vulnerable to catastrophic overfitting regarding those less transferable regions (e.g., backgrounds) due to the accumulation of inaccurate localization results in the target domain. To remedy these issues, we first formulate DAOD as an open-set domain adaptation problem, in which the foregrounds and backgrounds are seen as the "known classes" and "unknown class" respectively. Accordingly, we propose a new and general framework for DAOD, named Foreground-aware Graph-based Relational Reasoning (FGRR), which incorporates graph structures into the detection pipeline to explicitly model the intra- and inter-domain foreground object relations on both pixel and semantic spaces, thereby endowing the DAOD model with the capability of relational reasoning beyond the popular alignment-based paradigm. FGRR first identifies the foreground pixels and regions by searching reliable correspondence and cross-domain similarity regularization respectively. The inter-domain visual and semantic correlations are hierarchically modeled via bipartite graph structures, and the intra-domain relations are encoded via graph attention mechanisms. Through message-passing, each node aggregates semantic and contextual information from the same and opposite domain to substantially enhance its expressive power. Empirical results demonstrate that the proposed FGRR exceeds the state-of-the-art performance on four DAOD benchmarks.

δ-MnO2 decorated layered double oxides in-situ grown on nickel foam towards electrothermal catalysis of n-heptane.

Energy-saving and efficient monolithic catalysts are hotspots of catalytic purification of industrial gaseous pollutants. Here, we have developed an electrothermal catalytic mode, in which the ignition temperature required for the reaction is provided by Joule heat generated when the current flows through the catalyst. In this paper, Mn/NiAl/NF, Mn/NiFe/NF and Mn/NF metal-based monolithic catalysts were prepared using nickel foam (NF) as the carrier for thermal and electrothermal catalysis of n-heptane. The results indicated that Mn-based monolithic catalysts exhibit high activity in thermal and electrothermal catalysis. Mn/NiFe/NF achieve conversion of n-heptane more than 99% in electrothermal catalysis under a direct-current (DC) power of 6 W, and energy-saving is 54% compared with thermal catalysis. In addition, the results indicated that the introduction of NiAl (or NiFe) greatly enhanced the catalytic activity of Mn/NF, which attributed to the higher specific surface area, Mn3+/Mn4+, Ni3+/Ni2+, adsorbed oxygen species (Oads)/lattice oxygen species (Olatt), redox performance of the catalyst. Electrothermal catalytic activity was significantly higher than thermal catalytic activity before complete conversion, which may be related to electronic effects. Besides, Mn/NiFe/NF has good cyclic and long-term stability in electrothermal catalysis. This paper provided a theoretical basis for applying electrothermal catalysis in the field of VOCs elimination.

GraphSKT: Graph-Guided Structured Knowledge Transfer for Domain Adaptive Lesion Detection.

Adversarial-based adaptation has dominated the area of domain adaptive detection over the past few years. Despite their general efficacy for various tasks, the learned representations may not capture the intrinsic topological structures of the whole images and thus are vulnerable to distributional shifts especially in real-world applications, such as geometric distortions across imaging devices in medical images. In this case, forcefully matching data distributions across domains cannot ensure precise knowledge transfer and are prone to result in the negative transfer. In this paper, we explore the problem of domain adaptive lesion detection from the perspective of relational reasoning, and propose a Graph-Structured Knowledge Transfer (GraphSKT) framework to perform hierarchical reasoning by modeling both the intra- and inter-domain topological structures. To be specific, we utilize cross-domain correspondence to mine meaningful foreground regions for representing graph nodes and explicitly endow each node with contextual information. Then, the intra- and inter-domain graphs are built on the top of instance-level features to achieve a high-level understanding of the lesion and whole medical image, and transfer the structured knowledge from source to target domains. The contextual and semantic information is propagated through graph nodes methodically, enhancing the expressive power of learned features for the lesion detection tasks. Extensive experiments on two types of challenging datasets demonstrate that the proposed GraphSKT significantly outperforms the state-of-the-art approaches for detection of polyps in colonoscopy images and of mass in mammographic images.

Rosin acid and SiO2 modified cotton fabric to prepare fluorine-free durable superhydrophobic coating for oil-water separation.

Currently, fluorides and long-chain aliphatic compounds are the most frequent low surface energy chemicals utilized in the preparation of superhydrophobic coatings, but associated environmental risks and instability restrict their potential application in oil-water separation. This research described a superhydrophobic coating based on rosin acid and SiO2 modified cotton fabric to overcome this challenge. By means of spray impregnation and UV-assisted click reaction, sulfhydryl modified rosin acid (RA), Octavinyl-POSS, and SiO2 were grafted onto the surface of cotton fabric to obtain RA-SiO2 superhydrophobic coating with rough surfaces such as lotus leaf and low surface energy. The RA-SiO2 superhydrophobic coating had favorable self-cleaning ability, and also adsorbed various light and heavy oils to achieve efficient separation of oil-water mixtures. The separation efficiency was 96.3% and the permeate flux was 6110.84 (L⋅m-2⋅h-1) after 10 repetitions. The RA-SiO2 superhydrophobic coating was found to be effective in separating oil-in-water and oil-in-water emulsions, and the separation mechanism was elaborated. In addition, it could effectively separate emulsions even after mechanical abrasion and chemical immersion, and had excellent stability. The fluorine-free and environmentally friendly low-cost superhydrophobic coating based on rosin acid is expected to play a significant potential in oil-water separation applications due to its excellent separation performance.

Subsurface Manure Injection Reduces Surface Transport of Antibiotic Resistance Genes but May Create Antibiotic Resistance Hotspots in Soils.

Compared to surface application, manure subsurface injection reduces surface runoff of nutrients, antibiotic resistant microorganisms, and emerging contaminants. Less is known regarding the impact of both manure application methods on surface transport of antibiotic resistance genes (ARGs) in manure-amended fields. We applied liquid dairy manure to field plots by surface application and subsurface injection and simulated rainfall on the first or seventh day following application. The ARG richness, relative abundance (normalized to 16s rRNA), and ARG profiles in soil and surface runoff were monitored using shotgun metagenomic sequencing. Within 1 day of manure application, compared to unamended soils, soils treated with manure had 32.5-70.5% greater ARG richness and higher relative abundances of sulfonamide (6.5-129%) and tetracycline (752-3766%) resistance genes (p ≤ 0.05). On day 7, soil ARG profiles in the surface-applied plots were similar to, whereas subsurface injection profiles were different from, that of the unamended soils. Forty-six days after manure application, the soil ARG profiles in manure injection slits were 37% more diverse than that of the unamended plots. The abundance of manure-associated ARGs were lower in surface runoff from manure subsurface injected plots and carried a lower resistome risk score in comparison to surface-applied plots. This study demonstrated, for the first time, that although manure subsurface injection reduces ARGs in the runoff, it can create potential long-term hotspots for elevated ARGs within injection slits.

Stainless steel catalyst for air pollution control: structure, properties, and activity.

With the awakening of environmental awareness, the importance of air quality to human health and the proper functioning of social mechanisms is becoming increasingly prominent. The low cost and high efficiency of catalytic technique makes it a natural choice for achieving deep air purification. Stainless steel alloys have demonstrated their full potential for application in a variety of catalytic fields. The diversity of 3D networks or fibrous structures increases the turbulence within the heterogeneous catalysis, balance the temperature distribution in the reaction bed and, in combination with a highly thermally conductive skeleton, avoid agglomeration and deactivation of the active components; corrosion resistance and thermal stability are adapted to highly endothermic/exothermic or corrosive reaction environments; oxide layers formed by bulk transition metals activated by thermal treatment or etching can significantly alter the physico-chemical properties between the substrate and active species, further improving the stability of stainless steel catalysts; suitable electronic conductivity can be applied to the electrothermal catalysis, which is expected to provide guidance for the reduction of intermittent emission exhausts and the storage of renewable energy. The current applications of stainless steel as catalyst or support in the air purification have covered soot particle capture and combustion, catalytic oxidation of VOCs, SCR, and air sterilization. This paper summarizes several preparation methods and presents the relationships between the preparation process and the activity, and reviews its application and the current status of research in atmospheric environmental management, proposing the advantages and challenges of the stainless steel-based catalysts.

Spatial and temporal distribution and affecting factors of iron and manganese in the groundwater in the middle area of the Yangtze River Basin, China.

Iron (Fe) and manganese (Mn) are heavy metals ubiquitous in groundwater. High levels of Fe and Mn in groundwater can compromise water quality and pose a risk to human health if the groundwater is used for drinking or irrigation. In the middle region of the Yangtze River Basin, groundwater has been extensively used for domestic and agricultural purposes. However, little is known about the distribution of Fe and Mn in the groundwater in this area. It was found that the 74.4% and 48.9% of the groundwater exceed the China national guideline for Fe (i.e., 0.3 mg/L) and Mn (i.e., 0.1 mg/L), respectively. And 6.38% and 2.13% of the wells had Fe and Mn health risks, respectively. Spatial heterogeneity of Fe and Mn was observed. Notably, the concentrations of Fe and Mn in a plain region located between two major rivers (i.e., the Yangtze River and the Han River) were significantly higher than those in other regions. Modeling using PHREEQC revealed that the Fe-bearing minerals in the plain region were more saturated compared to those in the other regions. Besides, temporal change of Fe and Mn was observed in the plain region, significantly affected by rainfalls and groundwater levels. In addition, the distribution of Fe and Mn was significantly affected by various physicochemical factors. Particularly, Fe was more sensitive to redox potential compared to Mn. Under a reducing condition, organic matter concentration and water residence time also affect the release of Fe from Fe-bearing minerals. Overall, a comprehensive understanding of distribution characteristics of Fe and Mn and affecting factors in the middle area of the Yangtze River Basin can provide guidance for the distribution of industrial water, agricultural water, and drinking water in different regions of the study area. Especially in the plain area between the Yangtze River and the Han River, direct drinking of groundwater shall be reduced since the higher health risk value of Fe and Mn.

Metagenomic tracking of antibiotic resistance genes through a pre-harvest vegetable production system: an integrated lab-, microcosm- and greenhouse-scale analysis.

Prior research demonstrated the potential for agricultural production systems to contribute to the environmental spread of antibiotic resistance genes (ARGs). However, there is a need for integrated assessment of critical management points for minimizing this potential. Shotgun metagenomic sequencing data were analysed to comprehensively compare total ARG profiles characteristic of amendments (manure or compost) derived from either beef or dairy cattle (with and without dosing antibiotics according to conventional practice), soil (loamy sand or silty clay loam) and vegetable (lettuce or radish) samples collected across studies carried out at laboratory-, microcosm- and greenhouse-scale. Vegetables carried the greatest diversity of ARGs (n = 838) as well as the most ARG-mobile genetic element co-occurrences (n = 945). Radishes grown in manure- or compost-amended soils harboured a higher relative abundance of total (0.91 and 0.91 ARGs/16S rRNA gene) and clinically relevant ARGs than vegetables from other experimental conditions (average: 0.36 ARGs/16S rRNA gene). Lettuce carried the highest relative abundance of pathogen gene markers among the metagenomes examined. Total ARG relative abundances were highest on vegetables grown in loamy sand receiving antibiotic-treated beef amendments. The findings emphasize that additional barriers, such as post-harvest processes, merit further study to minimize potential exposure to consumers.

Culturable antibiotic-resistant fecal coliform bacteria in soil and surface runoff after liquid dairy manure surface application and subsurface injection.

Land application of manure, while beneficial to soil health and plant growth, can lead to an overabundance of nutrients and introduction of emerging contaminants into agricultural fields. Compared with surface application of manure, subsurface injection has been shown to reduce nutrients and antibiotics in surface runoff. However, less is known about the influence of subsurface injection on the transport and persistence of antibiotic-resistant microorganisms. We simulated rainfall to field plots at two sites (one in Virginia and one in Pennsylvania) 1 or 7 d after liquid dairy manure surface and subsurface application (56 Mg ha-1 ) and monitored the abundance of culturable antibiotic-resistant fecal coliform bacteria (ARFCB) in surface runoff and soils for 45 d. We performed these tests at both sites in spring 2018 and repeated the test at the Virginia site in fall 2019. Manure subsurface injection, compared with surface application, resulted in less ARFCB in surface runoff, and this reduction was greater at Day 1 after application compared with Day 7. The reductions of ARFCB in surface runoff because of manure subsurface injection were 2.5-593 times at the Virginia site in spring 2018 and fall 2019 and 4-5 times at the Pennsylvania site in spring 2018. The ARFCB were only detectable in the 0-to-5-cm soil depth within 14 d of manure surface application but remained detectable in the injection slits of manure subsurface-injected plots even at Day 45. This study demonstrated that subsurface injection can significantly reduce surface runoff of ARFCB from manure-applied fields.

Act Like a Radiologist: Towards Reliable Multi-View Correspondence Reasoning for Mammogram Mass Detection.

Mammogram mass detection is crucial for diagnosing and preventing the breast cancers in clinical practice. The complementary effect of multi-view mammogram images provides valuable information about the breast anatomical prior structure and is of great significance in digital mammography interpretation. However, unlike radiologists who can utilize the natural reasoning ability to identify masses based on multiple mammographic views, how to endow the existing object detection models with the capability of multi-view reasoning is vital for decision-making in clinical diagnosis but remains the boundary to explore. In this paper, we propose an anatomy-aware graph convolutional network (AGN), which is tailored for mammogram mass detection and endows existing detection methods with multi-view reasoning ability. The proposed AGN consists of three steps. First, we introduce a bipartite graph convolutional network (BGN) to model the intrinsic geometric and semantic relations of ipsilateral views. Second, considering that the visual asymmetry of bilateral views is widely adopted in clinical practice to assist the diagnosis of breast lesions, we propose an inception graph convolutional network (IGN) to model the structural similarities of bilateral views. Finally, based on the constructed graphs, the multi-view information is propagated through nodes methodically, which equips the features learned from the examined view with multi-view reasoning ability. Experiments on two standard benchmarks reveal that AGN significantly exceeds the state-of-the-art performance. Visualization results show that AGN provides interpretable visual cues for clinical diagnosis.