An explainable integrated optimization methodology for source apportionment of ambient particulate matter components.

Source apportionment of fine particulate matter (PM2.5) components is crucial for air pollution control. Prediction accuracies by the chemical transport model (CTM) significantly affect source apportionment results. Many efforts have been made to improve source apportionment results based on the CTM using mathematical algorithms, but the reasons for uncertainties in source apportionment results are less concerned. Here, an integrated optimization methodology is developed to quantify deviations from emission inventory and chemical mechanism in the model for improving prediction and source apportionment accuracies. Emission deviations of primary aerosols and gaseous pollutants are firstly calculated by an optimization algorithm with observation and receptor model constraints. Emission inventory is then adjusted for a new CTM simulation. Deviations from chemical mechanism for secondary conversions are evaluated by biases between observations and new predictions. Source apportionment results are adjusted according to both emission and chemical mechanism deviations. A winter month in 2016 at the Qingpu supersite in eastern China is selected as a case study. Results show that our integrated optimization methodology can successfully adjust emissions to pull original predictions towards observations. Total deviations of emissions for elemental carbon, organic carbon, primary sulfate, primary nitrate, primary ammonium, sulfur dioxide (SO2), nitrogen oxides (NOx) and ammonia (NH3) are estimated +59.6%, +95.9%, +72.9%, +82.2%, +75.9%, -6.4%, +67.6% and -17.6%, respectively. Also, major directions of deviations from chemical mechanisms can be captured. Deviations from SO2 to secondary sulfate, nitrogen dioxide (NO2) to secondary nitrate and NH3 to secondary ammonium conversions are estimated -77.3%, +27.1% and -38.8%, respectively. Consequently, source apportionment results are significantly improved. This developed methodology provides an efficient way to quantify deviations from emissions and chemical mechanisms to improve source apportionment for air pollution management.

Graphitic carbon nitride alleviates cadmium toxicity to microbial communities in soybean rhizosphere.

Cadmium (Cd) contamination has led to various harmful impacts on soil microbial ecosystem, agricultural crops, and thus human health. Nanomaterials are promising candidates for reducing the accumulation of heavy metals in plants. In this study, graphitic carbon nitride (g-C3N4), a two-dimensional polymeric nanomaterial, was applied for ameliorating Cd phytotoxicity to soybean (Glycine max (L.) Merr.). Its impacts on rhizosphere variables, microorganisms, and metabolism were examined. It was found that g-C3N4 increased carbon/nitrogen/phosphorus (C/N/P) content, especially when N contents were averagely 4.2 times higher in the g-C3N4-treated groups. g-C3N4 significantly induced alterations in microbial community structures (P < 0.05). The abundance of the probiotics class Nitrososphaeria was enriched (on average 70% higher in the g-C3N4-treated groups) as was Actinobacteria (226% higher in the g-C3N4 group than in the CK group). At the genus level, g-C3N4 recruited more Bradyrhizobium (122% higher) in the Cd + g-C3N4 group than in the Cd group and more Sphingomonas (on average 24% higher) in the g-C3N4-treated groups. The changes of microbial clusters demonstrated the potential of g-C3N4 to shape microbial functions, promote plant growth, and enhance Cd resistance, despite observing less pronounced modifications in microbial communities in Cd-contaminated soil compared to Cd-free soil. Moreover, abundance of functional genes related to C/N/P transformation was more significantly promoted by g-C3N4 in Cd-contaminated soil (increased by 146%) than in Cd-free one (increased by 32.8%). Therefore, g-C3N4 facilitated enhanced microbial survival and adaptation through the amplification of functional genes. These results validated the alleviation of g-C3N4 on the microbial communities in the soybean rhizosphere and shed a new light on the application of environmental-friendly nanomaterials for secure production of the crop under soil Cd exposure.

RNA-Seq analysis implicates dysregulation of the immune system in schizophrenia.

BACKGROUND: While genome-wide association studies identified some promising candidates for schizophrenia, the majority of risk genes remained unknown. We were interested in testing whether integration gene expression and other functional information could facilitate the identification of susceptibility genes and related biological pathways. RESULTS: We conducted high throughput sequencing analyses to evaluate mRNA expression in blood samples isolated from 3 schizophrenia patients and 3 healthy controls. We also conducted pooled sequencing of 10 schizophrenic patients and matched controls. Differentially expressed genes were identified by t-test. In the individually sequenced dataset, we identified 198 genes differentially expressed between cases and controls, of them 19 had been verified by the pooled sequencing dataset and 21 reached nominal significance in gene-based association analyses of a genome wide association dataset. Pathway analysis of these differentially expressed genes revealed that they were highly enriched in the immune related pathways. Two genes, S100A8 and TYROBP, had consistent changes in expression in both individual and pooled sequencing datasets and were nominally significant in gene-based association analysis. CONCLUSIONS: Integration of gene expression and pathway analyses with genome-wide association may be an efficient approach to identify risk genes for schizophrenia.

Considering CYP1A2 phenotype and genotype for optimizing the dose of olanzapine in the management of schizophrenia.

INTRODUCTION: Schizophrenia, a mental disorder, is a debilitating condition which typically strikes young people in their early 20's. Antipsychotic medications are widely prescribed for the treatment of schizophrenia however a balancing act is necessary to provide the correct dose to each patient. It is suggested that a large number of patients discontinue antipsychotic pharmacotherapy because the treatments provided do not always reduce the positive symptoms of the disease, while many have adverse effects on the patients. This implies that neither the incorrect drug nor the optimal dosage for that patient is achieved. AREAS COVERED: The current review investigates variability in response to olanzapine with a specific focus on the common intrinsic and extrinsic factors that influence both olanzapine and CYP1A2 activity. Furthermore, the authors discuss the utilization of phenotyping and genotyping of CYP1A2 and their potential utility in clinical practice for olanzapine dosing regimens. The authors also consider the potential of pharmacometrics compared to pharmacogenomics as a tool to personalize medicine. EXPERT OPINION: Careful consideration must be given to the impact of a genetic variant on the disposition of a drug prior to implementing genetic 'tests' to determine response. CYP1A2 phenotypic assessment can yield important information regarding the disposition of olanzapine; however, it relies on the accuracy of the metric and the minimal impact of other metabolic pathways. The application of pharmacometrics provides an effective method to establish covariates that significantly influence olanzapine disposition which can incorporate phenotype and/or genotype.