FePO4/WB as an efficient heterogeneous Fenton-like catalyst for rapid removal of neonicotinoid insecticides: ROS quantification, mechanistic insights and degradation pathways.

Iron-based catalysts for peroxymonosulfate (PMS) activation hold considerable potential in water treatment. However, the slow conversion of Fe(III) to Fe(II) restricts its large-scale application. Herein, an iron phosphate tungsten boride composite (FePO4/WB) was synthesized by a simple hydrothermal method to facilitate the Fe(III)/Fe(II) redox cycle and realize the efficient degradation of neonicotinoid insecticides (NEOs). Based on electron paramagnetic resonance (EPR) characterization, scavenging experiments, chemical probe approaches, and quantitative tests, both radicals (HO• and SO4⋅-) and non-radicals (1O2 and Fe(IV)) were produced in the FePO4/WB-PMS system, with relative contributions of 3.02 %, 3.58 %, 6.24 %, and 87.16 % to the degradation of imidacloprid (IMI), respectively. Mechanistic studies revealed that tungsten boride (WB) promoted the reduction of FePO4, and the generated Fe(II) dominantly activated PMS through a two-electron transfer to form Fe(IV), while a minority of Fe(II) engaged in a one-electron transfer with PMS to produce SO4⋅-, HO•, and 1O2. In addition, four degradation pathways of NEOs were proposed by analyzing the byproducts using UPLC-Q-TOF-MS/MS. Besides, seed germination experiments revealed the biotoxicity of NEOs was significantly reduced after degradation via the FePO4/WB-PMS system. Meanwhile, the recycling experiments and continuous flow reactor experiments showed that FePO4/WB exhibited high stability. Overall, this study provided a new perspective on water remediation by Fenton-like reaction. ENVIRONMENTAL IMPLICATION: Neonicotinoids (NEOs) are a type of insecticide used widely around the world. They've been found in many aquatic environments, raising concerns about their possible negative effects on the environment and health. Iron-based catalysts for peroxymonosulfate (PMS) activation hold great promise for water purification. However, the slow conversion of Fe(III) to Fe(II) restricts its large-scale application. Herein, iron phosphate tungsten boride composite (FePO4/WB) was synthesized by a simple hydrothermal method to facilitate the Fe(III)/Fe(II) redox cycle and realize the efficient degradation of NEOs. The excellent stability and reusability provided a great prospect for water remediation.

Design and use of an ex vivo peripheral simulating bioreactor system for pharmacokinetic analysis of a drug coated stent.

Currently, there are no ex vivo systems that can model the motion of peripheral arteries and allow for the evaluation of pharmacokinetics (PK) of endovascular devices. The objective of this study was to develop a novel peripheral simulating bioreactor system to evaluate drug pharmacokinetics of stents. We utilized 3D-printed and off-the-shelf components to construct a peripheral-simulating bioreactor system capable of mimicking the motion of peripheral arteries. Servo motors were primarily used to shorten/elongate, twist, and bend explanted porcine carotid arteries. To evaluate the pharmacokinetics in the bioreactor, drug-eluting stents were deployed within explanted arteries and subjected to vascular motion along with pulsatile flow conditions. Following 30 min and 24 h, the arteries were removed, and paclitaxel levels were measured. Scanning electron microscopy was also performed to evaluate the stent surface. Arterial paclitaxel levels of the stent-treated arteries were found to be higher at 30 min than at 24 h following pulsatile and no vascular motion and even higher at 24 h following pulsatile flow and vascular motion. The residual drug on the stent significantly decreased from 30 min to 24 h. Scanning electron microscopy confirmed the loss of paclitaxel coating at 24 h and greater disturbance in stents under peripheral motion versus pulsatile only. This system represents the first ex vivo system to determine the PK of drug-eluting stents under physiological flow and vascular motion conditions. This work provides a novel system for a quick and inexpensive preclinical tool to study acute drug tissue concentration kinetics of drug-releasing interventional vascular devices designed for peripheral applications.

Predicting anticancer synergistic drug combinations based on multi-task learning.

BACKGROUND: The discovery of anticancer drug combinations is a crucial work of anticancer treatment. In recent years, pre-screening drug combinations with synergistic effects in a large-scale search space adopting computational methods, especially deep learning methods, is increasingly popular with researchers. Although achievements have been made to predict anticancer synergistic drug combinations based on deep learning, the application of multi-task learning in this field is relatively rare. The successful practice of multi-task learning in various fields shows that it can effectively learn multiple tasks jointly and improve the performance of all the tasks. METHODS: In this paper, we propose MTLSynergy which is based on multi-task learning and deep neural networks to predict synergistic anticancer drug combinations. It simultaneously learns two crucial prediction tasks in anticancer treatment, which are synergy prediction of drug combinations and sensitivity prediction of monotherapy. And MTLSynergy integrates the classification and regression of prediction tasks into the same model. Moreover, autoencoders are employed to reduce the dimensions of input features. RESULTS: Compared with the previous methods listed in this paper, MTLSynergy achieves the lowest mean square error of 216.47 and the highest Pearson correlation coefficient of 0.76 on the drug synergy prediction task. On the corresponding classification task, the area under the receiver operator characteristics curve and the area under the precision-recall curve are 0.90 and 0.62, respectively, which are equivalent to the comparison methods. Through the ablation study, we verify that multi-task learning and autoencoder both have a positive effect on prediction performance. In addition, the prediction results of MTLSynergy in many cases are also consistent with previous studies. CONCLUSION: Our study suggests that multi-task learning is significantly beneficial for both drug synergy prediction and monotherapy sensitivity prediction when combining these two tasks into one model. The ability of MTLSynergy to discover new anticancer synergistic drug combinations noteworthily outperforms other state-of-the-art methods. MTLSynergy promises to be a powerful tool to pre-screen anticancer synergistic drug combinations.

A complete graph-based approach with multi-task learning for predicting synergistic drug combinations.

MOTIVATION: Drug combination therapy shows significant advantages over monotherapy in cancer treatment. Since the combinational space is difficult to be traversed experimentally, identifying novel synergistic drug combinations based on computational methods has become a powerful tool for pre-screening. Among them, methods based on deep learning have far outperformed other methods. However, most deep learning-based methods are unstable and will give inconsistent predictions even by simply changing the input order of drugs. In addition, the insufficient experimental data of drug combination screening limits the generalization ability of existing models. These problems prevent the deep learning-based models from being in service. RESULTS: In this article, we propose CGMS to address the above problems. CGMS models a drug combination and a cell line as a heterogeneous complete graph, and generates the whole-graph embedding to characterize their interaction by leveraging the heterogeneous graph attention network. Based on the whole-graph embedding, CGMS can make a stable, order-independent prediction. To enhance the generalization ability of CGMS, we apply the multi-task learning technique to train the model on drug synergy prediction task and drug sensitivity prediction task simultaneously. We compare CGMS's generalization ability with six state-of-the-art methods on a public dataset, and CGMS significantly outperforms other methods in the leave-drug combination-out scenario, as well as in the leave-cell line-out and leave-drug-out scenarios. We further present the benefit of eliminating the order dependency and the discrimination power of whole-graph embeddings, interpret the rationality of the attention mechanism, and verify the contribution of multi-task learning. AVAILABILITY AND IMPLEMENTATION: The code of CGMS is available via https://github.com/TOJSSE-iData/CGMS.

Metabolomic analysis reveals the molecular responses to copper toxicity in rice (Oryza sativa).

Copper (Cu) is one of the essential microelements and widely participates in various pathways in plants, but excess Cu in plant cells could induce oxidative stress and harm plant growth. Rice (Oryza sativa) is a main crop food worldwide. The molecular mechanisms of rice in response to copper toxicity are still not well understood. In this study, two-week-old seedlings of the rice cultivar Nipponbare were treated with 100 µM Cu2+ (CuSO4) in the external solution for 10 days. Physiological analysis showed that excess Cu significantly inhibited the growth and biomass of rice seedlings. After Cu treatment, the contents of Mn and Zn were significantly reduced in the roots and shoots, while the Fe content was significantly increased in the roots. Meanwhile, the activities of antioxidant enzymes including SOD and POD were dramatically enhanced after Cu treatment. Based on metabolomic analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods, 695 metabolites were identified in rice roots. Among these metabolites, 123 metabolites were up-regulated and 297 were down-regulated, respectively. The differential metabolites (DMs) include carboxylic acids and derivatives, benzene and substituted derivatives, carbonyl compounds, cinnamic acids and derivatives, fatty acyls and organ nitrogen compounds. KEGG analysis showed that these DMs were mainly enriched in TCA cycle, purine metabolism and starch and sucrose metabolism pathways. Many intermediates in the TCA cycle and purine metabolism were down-regulated, indicating a perturbed carbohydrate and nucleic acid metabolism. Taken together, the present study provides new insights into the mechanism of rice roots to Cu toxicity.

Stomatal morphological variation contributes to global ecological adaptation and diversification of Brassica napus.

MAIN CONCLUSION: Stomatal density and guard cell length of 274 global core germplasms of rapeseed reveal that the stomatal morphological variation contributes to global ecological adaptation and diversification of Brassica napus. Stomata are microscopic structures of plants for the regulation of CO2 assimilation and transpiration. Stomatal morphology has changed substantially in the adaptation to the external environment during land plant evolution. Brassica napus is a major crop to produce oil, livestock feed and biofuel in the world. However, there are few studies on the regulatory genes controlling stomatal development and their interaction with environmental factors as well as the genetic mechanism of adaptive variation in B. napus. Here, we characterized stomatal density (SD) and guard cell length (GL) of 274 global core germplasms at seedling stage. It was found that among the significant phenotypic variation, European germplasms are mostly winter rapeseed with high stomatal density and small guard cell length. However, the germplasms from Asia (especially China) are semi-winter rapeseed, which is characterized by low stomatal density and large guard cell length. Through selective sweep analysis and homology comparison, we identified several candidate genes related to stomatal density and guard cell length, including Epidermal Patterning Factor2 (EPF2; BnaA09g23140D), Epidermal Patterning Factor Like4 (EPFL4; BnaC01g22890D) and Suppressor of LLP1 (SOL1 BnaC01g22810D). Haplotype and phylogenetic analysis showed that natural variation in EPF2, EPFL4 and SOL1 is closely associated with the winter, spring, and semi-winter rapeseed ecotypes. In summary, this study demonstrated for the first time the relation between stomatal phenotypic variation and ecological adaptation in rapeseed, which is useful for future molecular breeding of rapeseed in the context of evolution and domestication of key stomatal traits and global climate change.

Genome-Wide Identification, Expression Pattern and Sequence Variation Analysis of SnRK Family Genes in Barley.

Sucrose non-fermenting 1 (SNF1)-related protein kinase (SnRK) is a large family of protein kinases that play a significant role in plant stress responses. Although intensive studies have been conducted on SnRK members in some crops, little is known about the SnRK in barley. Using phylogenetic and conserved motif analyses, we discovered 46 SnRK members scattered across barley's 7 chromosomes and classified them into 3 sub-families. The gene structures of HvSnRKs showed the divergence among three subfamilies. Gene duplication and synteny analyses on the genomes of barley and rice revealed the evolutionary features of HvSnRKs. The promoter regions of HvSnRK family genes contained many ABRE, MBS and LTR elements responding to abiotic stresses, and their expression patterns varied with different plant tissues and abiotic stresses. HvSnRKs could interact with the components of ABA signaling pathway to respond to abiotic stress. Moreover, the haplotypes of HvSnRK2.5 closely associated with drought tolerance were detected in a barley core collection. The current results could be helpful for further exploration of the HvSnRK genes responding to abiotic stress tolerance in barley.

Transcriptome-wide m6A methylation profile reveals regulatory networks in roots of barley under cadmium stress.

Cadmium (Cd) pollutants restrict crop yield and food security in long-term agricultural activities. Crops have evolved adaptive strategies under Cd condition, however, the transcriptional regulatory mechanism of Cd-tolerant genes remains to be largely illustrated. In this study, barley roots were exposed to 5 µM CdCl2 for physiological response and transcriptome-wide m6A methylation profile. Cd stress inhibited root growth after 7 d Cd treatment, which is mainly associated with inhibited absorption of Mn. After Cd treatment, 8151 significantly modified m6A sites and 3920 differentially expressed genes were identified. Transcriptome-wide m6A hypermethylation was widely induced by Cd stress and enriched near the stop codon and 3' UTR regions. Among 435 m6A modified DEGs, 319 hypermethylated genes were up-regulated and 84 hypomethylated genes were down-regulated, respectively, indicating a positive correlation of m6A methylation and expression. But well-known Cd transporter genes (HvNramp5, HvIRT1, HvHMA3, etc.) were not modified by m6A methylation, except for ABC transporters. We further found key Cd-responding regulatory genes were positively modulated with m6A methylation, including MAPK, WRKY and MYB members. This study proposed a transcriptional regulatory network of Cd stress response in barley roots, which may provide new insight into gene manipulation of controlling low Cd accumulation for crops.

Study on the Effect of Capsaicin on the Intestinal Flora through High-Throughput Sequencing.

As a common kind of food, pepper is well known for its special effects on the physiological state of human individuals. Capsaicin, the main component of pepper, is speculated to be linked with intestinal microorganisms on account of their direct contact. Herein, we first utilized mouse models and 16S rRNA high-throughput sequencing to compare the differences in intestinal flora between mouse groups with and without capsaicin treatment by gavage. The mice in the two groups showed significantly distinct performance in terms of body weight, leukocyte count, fecal humidity, and constituent ratios of intestinal bacteria, such as Faecalibacterium, Akkermansia, Roseburia, Helicobacter, and Bacteroides species. In particular, the Faecalibacterium abundance was the most highly variable among the 5 bacterial genera. Based on statistical analysis and comparison, the variation tendency of body weight, leukocyte count, and fecal humidity was closely related to the bacteria. In conclusion, capsaicin could affect the physiological state of mice by changing the constitution of the intestinal flora.

Construction of High-Density Genetic Map and Identification of QTLs Associated with Seed Vigor after Exposure to Artificial Aging Conditions in Sweet Corn Using SLAF-seq.

Seed vigor is a key factor that determines the quality of seeds, which is of great significance for agricultural production, with the potential to promote growth and productivity. However, the underlying molecular mechanisms and genetic basis for seed vigor remain unknown. High-density genetic linkage mapping is an effective method for genomic study and quantitative trait loci (QTL) mapping. In this study, a high-density genetic map was constructed from a 148 BC4F3 population cross between 'M03' and 'M08' strains based on specific-locus amplified fragment (SLAF) sequencing. The constructed high-density genetic linkage map (HDGM) included 3876 SNP markers on ten chromosomes covering 2413.25 cM in length, with a mean distance between markers of 0.62 cM. QTL analysis was performed on four sweet corn germination traits that are related to seed vigor under artificial aging conditions. A total of 18 QTLs were identified in two seasons. Interestingly, a stable QTL was detected in two seasons on chromosome 10-termed qGR10-within an interval of 1.37 Mb. Within this interval, combined with gene annotation, we found four candidate genes (GRMZM2G074309, GRMZM2G117319, GRMZM2G465812, and GRMZM2G343519) which may be related to seed vigor after artificial aging.