Probing the origin of higher efficiency of terphenyl phosphine over the biaryl framework in Pd-catalyzed C-N coupling: A combined DFT and machine learning study

The Pd-catalyzed Buchwald–Hartwig coupling reaction is important in the construction of the C-N bond due to various applications in organic synthesis. Quantum chemical calculations are widely used in understanding reaction mechanisms whereas the machine learning method is extremely popular in recognizing the relationships of data. Here, we combine density functional theory calculations with the support vector regression method to probe the origin of the higher efficiency of terphenyl phosphine ligand over the biaryl counterpart in the Buchwald–Hartwig C-N coupling reaction. By quantum chemical calculations, the turnover frequency-determining transition states are located and ligand features are calculated with high accuracy. By machine learning, the relationship between the reaction barrier and ligand features has been examined. It is found that the interplay of the charge on the metal center, the cone angle of the ligands, and the Sterimol L parameters of the ligand determines the catalytic performance of the palladium catalysts with different phosphine ligands. Our findings could help experimental chemists to design the ligands for Pd-catalyzed C-N coupling reactions with high efficiency

Segmentation for Fabric Weave Pattern Using Empirical Mode Decomposition Based Histogram

This paper is focused on the segmentation of the fabric weave patterns for the urgent requirement of fabric imitative design and redesign.The weave patterns related to the fabric yarn are determined by a new technology,which is called bidimensional mode decomposition based method. The proposed method first iteratively decompose the underlying fabric image into a number of intrinsic mode functions (IMFs). The first order IMF is applied to to calculate histogram and fabric weave pattern segmentation results are obtained by integrating corresponding threshold decision strategies such as double maxima algorithm or Otsu algorithm . In comparison with the original image-only based histogram segmentation method ,the presented method have a high precision. Simulation results show that BEMD based method is a promising approach for the segmentation of fabric weave pattern. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.237

Biosolids inhibit bioavailability and plant uptake of triclosan and triclocarban.

Biosolids from wastewater treatment are primarily disposed of via land applications, where numerous pharmaceuticals and personal care products (PPCPs) may contaminate food crops and pose a human exposure risk. Biosolids are rich in organic carbon and addition of biosolids can increase the sorption of certain PPCPs in soil, decreasing their bioavailability. This study tested the hypothesis that the relative plant uptake of PPCPs decreases with increasing biosolids amendment. Accumulation of triclosan and triclocarban was measured in roots of radish and carrot grown in soils with or without biosolids. Addition of biosolids significantly prolonged the persistence of triclosan in soil. When expressed in bioaccumulation factor (BCF), accumulation of triclosan drastically decreased in biosolids-amended soils, while the effect was limited for triclocarban. Compared to the unamended soil, amending biosolids at 2% (w/w) decreased BCF of triclosan in the edible tissues of radish and carrot by 85.4 and 89.3%, respectively. Measurement using a thin-film passive sampler provided direct evidence showing that the availability of triclosan greatly decreased in biosolids-amended soils. Partial correlation analysis using data from this and published studies validated that biosolids decreased plant uptake primarily by increasing soil organic carbon content and subsequently sorption. Therefore, contamination of food crops by biosolids-borne contaminants does not linearly depend on biosolids use rates. This finding bears significant implications in the overall risk evaluation of biosolids-borne contaminants

Biosolids inhibit bioavailability and plant uptake of triclosan and triclocarban.

Biosolids from wastewater treatment are primarily disposed of via land applications, where numerous pharmaceuticals and personal care products (PPCPs) may contaminate food crops and pose a human exposure risk. Biosolids are rich in organic carbon and addition of biosolids can increase the sorption of certain PPCPs in soil, decreasing their bioavailability. This study tested the hypothesis that the relative plant uptake of PPCPs decreases with increasing biosolids amendment. Accumulation of triclosan and triclocarban was measured in roots of radish and carrot grown in soils with or without biosolids. Addition of biosolids significantly prolonged the persistence of triclosan in soil. When expressed in bioaccumulation factor (BCF), accumulation of triclosan drastically decreased in biosolids-amended soils, while the effect was limited for triclocarban. Compared to the unamended soil, amending biosolids at 2% (w/w) decreased BCF of triclosan in the edible tissues of radish and carrot by 85.4 and 89.3%, respectively. Measurement using a thin-film passive sampler provided direct evidence showing that the availability of triclosan greatly decreased in biosolids-amended soils. Partial correlation analysis using data from this and published studies validated that biosolids decreased plant uptake primarily by increasing soil organic carbon content and subsequently sorption. Therefore, contamination of food crops by biosolids-borne contaminants does not linearly depend on biosolids use rates. This finding bears significant implications in the overall risk evaluation of biosolids-borne contaminants

Hierarchical Attention Neural Network for Event Types to Improve Event Detection

Event detection is an important task in the field of natural language processing, which aims to detect trigger words in a sentence and classify them into specific event types. Event detection tasks suffer from data sparsity and event instances imbalance problems in small-scale datasets. For this reason, the correlation information of event types can be used to alleviate the above problems. In this paper, we design a Hierarchical Attention Neural Network for Event Types (HANN-ET). Specifically, we select Long Short-Term Memory (LSTM) as the semantic encoder and utilize dynamic multi-pooling and the Graph Attention Network (GAT) to enrich the sentence feature. Meanwhile, we build several upper-level event type modules and employ a weighted attention aggregation mechanism to integrate these modules to obtain the correlation event type information. Each upper-level module is completed by a Neural Module Network (NMNs), event types within the same upper-level module can share information, and an attention aggregation mechanism can provide effective bias scores for the trigger word classifier. We conduct extensive experiments on the ACE2005 and the MAVEN datasets, and the results show that our approach outperforms previous state-of-the-art methods and achieves the competitive F1 scores of 78.9% on the ACE2005 dataset and 68.8% on the MAVEN dataset

Multi-dimensional visualization of ingestion, biological effects and interactions of microplastics and a representative POP in edible jellyfish

Due to their ubiquity and potential risks, microplastics (MPs) and nanoplastics (NPs) are concerning environmental issues. Yet there are still significant knowledge gaps in understanding the tissue-specific accumulation and dynamic change of MPs and NPs in the aquatic organism and how these micro/nano-scale emerging contaminants interact with other environmental pollutants such as persistent organic pollutants (POPs). Here, in vivo imaging systems (IVIS), radioisotope tracing, and histological staining were innovatively used to reveal the fate and toxicity of fluorescently-labeled MPs/NPs and 14C-labeled 2,4,4′-trichlorobiphenyl (PCB28) in edible jellyfish Rhopilema esculentum. These contaminants' ingestion, biological effects, and interactions were visualized at cellular, tissue, and whole-body multidimensional levels. Both MPs and NPs were shown to be preferentially accumulated in the mouthlets of oral arms, and most ingested MPs/NPs were present in the extracellular environment instead of being internalized into the mesoglea. Moreover, the presence of MPs or NPs in the seawater significantly inhibited the bioaccumulation of PCB28 in the jellyfish tissue, thus alleviating physiological alteration, gastric damage, and apoptosis caused by PCB28. This study provides a multi-dimensional visualization strategy to display the distribution and biological effects of typical pollutants in marine organisms and offers new insights for understanding the impacts of MPs/NPs and POPs on marine ecosystems

Degradation Kinetics and Metabolites of Carbamazepine in Soil

The antiepileptic drug carbamazepine (CBZ) is one of the most frequently detected human pharmaceuticals in wastewater effluents and biosolids. Soil is a primary environmental compartment receiving CBZ through wastewater irrigation and biosolid application. In this study, we explored the transformation of CBZ to biologically active intermediates in soil. Both ¹⁴C labeling and liquid chromatography–tandem mass spectrometry (LC–MS/MS) were used to track transformation kinetics and identify major degradation intermediates. Through 120 days of incubation under aerobic conditions, mineralization of CBZ did not exceed 2% of the spiked rate in different soils. Amendment of biosolids further suppressed mineralization. The fraction of non-extractable (i.e., bound) residue also remained negligible (<5%). On the other hand, CBZ was transformed to a range of degradation intermediates, including 10,11-dihydro-10-hydroxycarbamazepine, carbamazepine-10,11-epoxide, acridone-<i>N</i>-carbaldehyde, 4-aldehyde-9-acridone, and acridine, of which acridone-<i>N</i>-carbaldehyde was formed in a large fraction and appeared to be recalcitrant to further degradation. Electrocyclization, ring cleavage, hydrogen shift, carbonylation, and decarbonylation contributed to CBZ transformative reactions in soil, producing biologically active products. The persistence of the parent compound and formation of incomplete intermediates suggest that CBZ has a high risk for off-site transport from soil, such as accumulation into plants and contamination of groundwater

Combination of High Specific Activity Carbon-14 Labeling and High Resolution Mass Spectrometry to Study Pesticide Metabolism in Crops: Metabolism of Cycloxaprid in Rice

The study of pesticide metabolism in crops is critical for assessing the mode of action and environmental risks of pesticides. However, the study of pesticide metabolism in crops is usually complicated and it is often a daunting challenge to accurately screen the metabolites of novel pesticides in complex matrices. This study demonstrated a combined use of high-specific activity carbon-14 labeling and high-resolution mass spectrometry (HSA-14C-HRMS) for metabolism profiling of a novel neonicotinoid cycloxaprid in rice. By generating the characteristic radioactive peaks on the liquid chromatogram, the use of 14C can eliminate the severe interference of complex matrices and quickly probe target compounds; by producing ion pairs with unique abundance ratios on HRMS, high-specific activity labeling can effectively exclude false matrix positives and promote the elucidation of metabolite structure. The structures of 15 metabolites were identified, three of which were further confirmed by authentic standards. Based on these metabolites, a metabolic profile of cycloxaprid was established, which includes denitrification, demethylation, imidazolidine hydroxylation and ring cleavage olefin formation, oxidation and carboxylation reactions. The strategy of combining high-specific activity 14C labeling and HRMS offers unique advantages and provides a powerful solution for profiling unknown metabolites of novel pesticides in complex matrices, especially when traditional non-labeling methods are not feasible