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Abstract Background Breast cancer (BC) is one of the malignant diseases threatening the life and health of women worldwide. The CYP4B1 gene was abnormally expressed in BC and was associated with the prognosis of BC patients. This study aimed to explore the relationship between CYP4B1 single nucleotide polymorphisms (SNPs) and BC risk in Chinese women. Methods A case-control study of 1,143 women (571 patients and 572 healthy individuals) was conducted. Rs2297813 G/T, rs12142787 G/A, and rs3766197 C/T in CYP4B1 were selected and genotyped by MassARRAY system. The relationships between these SNPs and the risk of BC were assessed by logistic regression analysis. In addition, multi-factor dimensionality reduction (MDR) was used to analyze SNP-SNP interactions. Results CYP4B1 rs2297813 had a risk-increasing effect on BC in women with body mass index (BMI) ≤ 24 kg/m2 (OR = 1.72, p = 0.026). CYP4B1 rs12142787 was associated with an increased BC risk in smokers (AA: OR = 1.32, p = 0.045). Among non-drinkers, rs2297813 (OR = 1.69, p = 0.009) and rs12142787 (OR = 1.51, p = 0.020) were related to an increased incidence of BC. CYP4B1 rs3766197 (OR = 1.61p = 0.031) was associated with a higher risk of advanced stages (III/IV stage) of BC. Besides, the contributions of CYP4B1 rs2297813 (OR = 1.55, p = 0.021) and rs12142787 (OR = 1.53, p = 0.033) to BC risk might be associated with more than one birth in patients with BC. The three-locus model consisting of rs2297813, rs12142787, and rs3766197 was regarded as the best predictive model for BC risk. Conclusion CYP4B1 SNPs were associated with BC risk in Chinese women, especially in patients with BMI ≤ 24 kg/m2, smokers, non-drinkers, patients in advanced stages (III/IV stage), and patients who reproduced once. These findings shed light on the relationship between CYP4B1 SNPs and BC risk in Chinese women.

Abstract Background The study aimed to evaluate the relationship of IL-1B/IL-1RN polymorphisms to the predisposition of head and neck cancer (HNC) in a Chinese Han population. Methods Nine single-nucleotide polymorphisms (SNPs) in IL-1B/IL-1RN were genotyped based on Agena MassARRAY platform. Logistic regression models were used to analyze the genetic association between these SNPs and HNC risk by calculating odds ratios (ORs) and 95% confidence intervals (CI). Haplotype analysis were performed using Haploview program and logistic regression model. Results The genetic association between rs1143643 in IL-1B and the higher risk of HNC was found (OR = 1.23, 95% CI 1.04–1.46) in the overall. IL-1RN rs17042888 was related to a reduced risk of HNC in the subjects aged > 46 years (OR = 0.70, 95% CI: 0.50–0.98) and in females (OR = 0.71, 95% CI 0.52–0.98), while rs1143643 increased the predisposition of HNC among females (OR = 1.76, 95% CI 1.13–2.74). Furthermore, rs1143643 had an increased susceptibility to thyroid carcinoma (OR = 1.61, 95% CI 1.10–2.34). Moreover, compared with stage I–II, the frequency of IL-1RN rs452204-AG genotype was lower in patients with stage III–IV. Conclusions IL-1B (rs1143643) and IL-1RN (rs17042888 and rs452204) polymorphisms might be related to the individual susceptibility of HNC in the Chinese Han population. These results might help to improve the understanding of IL-1B and IL-1RN genes in the occurrence of HNC.

Abstract Background Assessing the impact of the environment on plant performance requires growing plants under controlled environmental conditions. Plant phenotypes are a product of genotype × environment (G × E), and the Enviratron at Iowa State University is a facility for testing under controlled conditions the effects of the environment on plant growth and development. Crop plants (including maize) can be grown to maturity in the Enviratron, and the performance of plants under different environmental conditions can be monitored 24 h per day, 7 days per week throughout the growth cycle. Results The Enviratron is an array of custom-designed plant growth chambers that simulate different environmental conditions coupled with precise sensor-based phenotypic measurements carried out by a robotic rover. The rover has workflow instructions to periodically visit plants growing in the different chambers where it measures various growth and physiological parameters. The rover consists of an unmanned ground vehicle, an industrial robotic arm and an array of sensors including RGB, visible and near infrared (VNIR) hyperspectral, thermal, and time-of-flight (ToF) cameras, laser profilometer and pulse-amplitude modulated (PAM) fluorometer. The sensors are autonomously positioned for detecting leaves in the plant canopy, collecting various physiological measurements based on computer vision algorithms and planning motion via “eye-in-hand” movement control of the robotic arm. In particular, the automated leaf probing function that allows the precise placement of sensor probes on leaf surfaces presents a unique advantage of the Enviratron system over other types of plant phenotyping systems. Conclusions The Enviratron offers a new level of control over plant growth parameters and optimizes positioning and timing of sensor-based phenotypic measurements. Plant phenotypes in the Enviratron are measured in situ—in that the rover takes sensors to the plants rather than moving plants to the sensors.