Molecular mechanisms underlying human spatial cognitive ability revealed with neurotransmitter and transcriptomic mapping.

Mental rotation, one of the cores of spatial cognitive abilities, is closely associated with spatial processing and general intelligence. Although the brain underpinnings of mental rotation have been reported, the cellular and molecular mechanisms remain unexplored. Here, we used magnetic resonance imaging, a whole-brain spatial distribution atlas of 19 neurotransmitter receptors, transcriptomic data from Allen Human Brain Atlas, and mental rotation performances of 356 healthy individuals to identify the genetic/molecular foundation of mental rotation. We found significant associations of mental rotation performance with gray matter volume and fractional amplitude of low-frequency fluctuations in primary visual cortex, fusiform gyrus, primary sensory-motor cortex, and default mode network. Gray matter volume and fractional amplitude of low-frequency fluctuations in these brain areas also exhibited significant sex differences. Importantly, spatial correlation analyses were conducted between the spatial patterns of gray matter volume or fractional amplitude of low-frequency fluctuations with mental rotation and the spatial distribution patterns of neurotransmitter receptors and transcriptomic data, and identified the related genes and neurotransmitter receptors associated with mental rotation. These identified genes are localized on the X chromosome and are mainly involved in trans-synaptic signaling, transmembrane transport, and hormone response. Our findings provide initial evidence for the neural and molecular mechanisms underlying spatial cognitive ability.

In silico prediction of chemical genotoxicity using machine learning methods and structural alerts.

Genotoxicity tests can detect compounds that have an adverse effect on the process of heredity. The in vivo micronucleus assay, a genotoxicity test method, has been widely used to evaluate the presence and extent of chromosomal damage in human beings. Due to the high cost and laboriousness of experimental tests, computational approaches for predicting genotoxicity based on chemical structures and properties are recognized as an alternative. In this study, a dataset containing 641 diverse chemicals was collected and the molecules were represented by both fingerprints and molecular descriptors. Then classification models were constructed by six machine learning methods, including the support vector machine (SVM), naïve Bayes (NB), k-nearest neighbor (kNN), C4.5 decision tree (DT), random forest (RF) and artificial neural network (ANN). The performance of the models was estimated by five-fold cross-validation and an external validation set. The top ten models showed excellent performance for the external validation with accuracies ranging from 0.846 to 0.938, among which models Pubchem_SVM and MACCS_RF showed a more reliable predictive ability. The applicability domain was also defined to distinguish favorable predictions from unfavorable ones. Finally, ten structural fragments which can be used to assess the genotoxicity potential of a chemical were identified by using information gain and structural fragment frequency analysis. Our models might be helpful for the initial screening of potential genotoxic compounds.

In silico prediction of pesticide aquatic toxicity with chemical category approaches.

Aquatic toxicity is an important issue in pesticide development. In this study, using nine molecular fingerprints to describe pesticides, binary and ternary classification models were constructed to predict aquatic toxicity of pesticides via six machine learning methods: Naïve Bayes (NB), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), Classification Tree (CT), Random Forest (RF) and Support Vector Machine (SVM). For the binary models, local models were obtained with 829 pesticides on rainbow trout (RT) and 151 pesticides on lepomis (LP), and global models were constructed on the basis of 1258 diverse pesticides on RT and LP and 278 on other fish species. After analyzing the local binary models, we found that fish species caused influence in terms of accuracy. Considering the data size and predictive range, the 1258 pesticides were also used to build global ternary models. The best local binary models were Maccs_ANN for RT and Maccs_SVM for LP, which exhibited accuracies of 0.90 and 0.90, respectively. For global binary models, the best model was Graph_SVM with an accuracy of 0.89. Accuracy of the best global ternary model Graph_SVM was 0.81, which was a little lower than that of the best global binary model. In addition, several substructural alerts were identified including nitrobenzene, chloroalkene and nitrile, which could significantly correlate with pesticide aquatic toxicity. This study provides a useful tool for an early evaluation of pesticide aquatic toxicity in environmental risk assessment.

Dynamics of novel insecticide HNPC-A9908 residue in vegetable-field ecosystem.

HNPC-A9908 (o-(3-phenoxybenzyl)-2-methylthio-l-(4-chlorophenyl) propyl ketone oxime), a novel oxime insecticide, is a highly effective and broad-spectrum insecticide which can be widely used to control many species of foliar insects on various crops. A study was conducted to evaluate the fate of HNPC-A9908 and study the degradation dynamics of HNPC-A9908 residue in vegetable field ecosystem. The results showed that degradation of HNPC-A9908 was much faster in vegetable pakchoi than in soil, and its half-life in pakchoi and soil was 1.32 and 3.75 d, respectively. The final residue of HNPC-A9908 in pakchoi was at the undetectable level to 0.122 mg/kg. As a conclusion, a dosage of 90 g/hm(2) was suggested and considered to be safe to human beings and animals.

Biodegradation of imazapyr in typical soils in Zhejiang Province, China.

The degradation of imazapyr in non-sterile and sterile soils from four sampling sites in Zhejiang, China was studied. The results showed that the half-lives of imazapyr in non-sterile soils were in the range of 30 to 45 d, while 81 to 133 d in sterile (by autoclaving) soils. It means the rate constants of imazapyr under non-sterile conditions were 2.3-4.4 times faster than that under sterile (by autoclaving) conditions, evidently indicating that the indigenous microorganisms in soil play an important role in the degradation of imazapyr. The different sterilization methods could result in different degradation rates of imazapyr. The heat of sterilization of soil largely decreased the degradation. However, the sterile treatment of soil by sodium azide had a different effect from that by autoclaving. Further more, the mechanism was also discussed. Biodegradation in four non-sterile soils accounted for 62% to 78% of imazapyr degradation. In contrast, less than 39% of imazapyr degradation was associated with chemical mechanisms. Therefore, the degradation mechanism was predominantly involved in biology including organisms and microorganisms in soil. Two imazapyr-degrading bacterial strains were isolated in enrichment culture technique and they were identified as Pseudomonas fluorescenes biotype II (ZJX-5) and Bacillus cereus (ZJX-9), respectively. When added at a concentration of 50 microg/g in mineral salts medium (MSM), ZJX-5 and ZJX-9 could degrade 81% and 87% imazapyr after 48 h of incubation. For the treatment of incorporation of ZJX-5 or ZJX-9 into soil, the degradation rate enhanced 3-4 fold faster than that for control samples, which showed an important value in quick decontamination of imazapyr in soil.

Dissipation of chlorpyrifos on pakchoi inside and outside greenhouse.

The dissipation of chlorpyrifos on pakchoi inside and outside greenhouse was studied. The decline curve of chlorpyrifos on pakchoi could be described as first-order kinetic. The experimental data showed that both the hermetic environment of greenhouse and season affected dissipation rates of chlorpyrifos on pakchoi. Chlorpyrifos declined faster outside greenhouse than inside greenhouse. Chlorpyrifos residues at pre-harvest time were below the maximum residue limits (MRLs) fixed in China, whereas the values inside greenhouse were higher than those outside greenhouse by almost 50%. The recommended pre-harvest time established under conditions of open field might not always fit to greenhouse production.

[Microbial degradation of Abamectin in soil].

Incubation test on the degradation dynamics of Abamectin in soil showed that the half-life of its non-biodegradation plus microbial biodegradation, non-biodegradation, and microbial biodegradation was 34.8, 277.3 and 49.9 d, respectively, and its degradation in soil was mostly by microbes. A dominant bacterium which could effectively degrade Abamectin was isolated from test soil, and identified as Stenotrophomonas maltrophilia by 16S rDNA. The crude enzyme extracted from the dominant bacteria had a Michaelis-Menten's constant 6.78 nmol x ml(-1) and a maximum rate 81.5 nmol x min(-1) x mg(-1).

[Degradation of fenpropathrin, phoxim and their mixture by soil microbes].

The degradation rates of fenpropathrin, phoxim and their mixture in un-sterilized soil were much quicker than those in sterilized soil, which indicated that soil microorganisms played a significant role in the degradation process in soil. The half-life (T0.5) in un-sterilized soil was 56.2 d for fenpropathrin, 57.8 d for mixed fenpropathrin, 48.2 d for phoxim, and 41.7 d for mixed phoxim. The corresponding half-life (T0.5) in sterilized soil was 135.1 d, 147.3 d, 123.6 d, and 126.2 d, respectively. There were no significant differences for degradation rates between single use and mixed use of fenpropathrin and phoxim.

Correlation between biochemical parameters and susceptibility of freshwater fish to malathion.

Acute toxicity (96-h LC50) of malathion was tested using five species of freshwater fish, namely, topmouth gudgeon (Pseudorasbora parva), goldfish (Carassius auratus), nile tilapia (Tilapia nilotica), mosquitofish (Gambusia affinis), and rainbow trout (Salmo gairdneri). Correlation was found between susceptibility and biochemical parameters such as activity of brain acetylcholinesterase (AChE) and in vitro resistance of the enzyme to inhibition (IC50) of malaoxon (a major metabolite of malathion). The in vitro study also showed that malaoxon instead of malathion was the main inhibitor of AChE. Susceptibility to malathion was considerably changed as the fish was pretreated with piperonyl butoxide (PB, a P-450 inhibitor) and triphenyl phosphate (TPP, an inhibitor of carboxylesterase), respectively. Toxicity of malathion was significantly increased by TPP, but the responses of fish to PB were quite different among species. This suggested that both carboxylesterase and monooxygenase played an important role in susceptibility determination, and great variations existed among species in activity of monooxygenase.