Methodologies for the analysis of pesticides and pharmaceuticals in sediments and plant tissue

Eco-technologies that utilize natural processes involving wetland vegetation, soil and their associated microbial assemblages are increasingly used for the removal of contaminants of emerging concern (CECs) from polluted water. However, information on removal processes in these systems is not always available, possibly due to the lack of simple and robust methodologies for analysis of CECs in complex matrices such as sediment and plant tissue. The aim of the present study was to use a simple and fast procedure based on ultrasonic extraction (USE) and reduced clean-up procedures to analyse 8 pesticides and 9 pharmaceuticals by high-performance liquid chromatography (HPLC) coupled with diode array detector. The established methods demonstrated suitable sensitivity and reliability, and proved fit-for-purpose in quantifying multiple classes of pesticides and pharmaceuticals. For sediments, extraction with methanol/acetone (95:5, v/v) followed by a simple evaporation to dryness and redissolution (water:methanol 50:50) provided acceptable recovery (50 - 101%) and RSD 64%) with RSD < 22% determined using different types of wetland plants. The methodology has been successfully applied in different studies on the fate of emerging contaminants in water treatment eco-technology systems

Upregulation of the microRNA cluster at the Dlk1-Dio3 locus in lung adenocarcinoma.

Mice in which lung epithelial cells can be induced to express an oncogenic Kras(G12D) develop lung adenocarcinomas in a manner analogous to humans. A myriad of genetic changes accompany lung adenocarcinomas, many of which are poorly understood. To get a comprehensive understanding of both the transcriptional and post-transcriptional changes that accompany lung adenocarcinomas, we took an omics approach in profiling both the coding genes and the non-coding small RNAs in an induced mouse model of lung adenocarcinoma. RNAseq transcriptome analysis of Kras(G12D) tumors from F1 hybrid mice revealed features specific to tumor samples. This includes the repression of a network of GTPase-related genes (Prkg1, Gnao1 and Rgs9) in tumor samples and an enrichment of Apobec1-mediated cytosine to uridine RNA editing. Furthermore, analysis of known single-nucleotide polymorphisms revealed not only a change in expression of Cd22 but also that its expression became allele specific in tumors. The most salient finding, however, came from small RNA sequencing of the tumor samples, which revealed that a cluster of ∼53 microRNAs and mRNAs at the Dlk1-Dio3 locus on mouse chromosome 12qF1 was markedly and consistently increased in tumors. Activation of this locus occurred specifically in sorted tumor-originating cancer cells. Interestingly, the 12qF1 RNAs were repressed in cultured Kras(G12D) tumor cells but reactivated when transplanted in vivo. These microRNAs have been implicated in stem cell pleuripotency and proteins targeted by these microRNAs are involved in key pathways in cancer as well as embryogenesis. Taken together, our results strongly imply that these microRNAs represent key targets in unraveling the mechanism of lung oncogenesis

3D Face Reconstruction from Light Field Images: A Model-free Approach

Reconstructing 3D facial geometry from a single RGB image has recently instigated wide research interest. However, it is still an ill-posed problem and most methods rely on prior models hence undermining the accuracy of the recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI) obtained from light field cameras and learn CNN models that recover horizontal and vertical 3D facial curves from the respective horizontal and vertical EPIs. Our 3D face reconstruction network (FaceLFnet) comprises a densely connected architecture to learn accurate 3D facial curves from low resolution EPIs. To train the proposed FaceLFnets from scratch, we synthesize photo-realistic light field images from 3D facial scans. The curve by curve 3D face estimation approach allows the networks to learn from only 14K images of 80 identities, which still comprises over 11 Million EPIs/curves. The estimated facial curves are merged into a single pointcloud to which a surface is fitted to get the final 3D face. Our method is model-free, requires only a few training samples to learn FaceLFnet and can reconstruct 3D faces with high accuracy from single light field images under varying poses, expressions and lighting conditions. Comparison on the BU-3DFE and BU-4DFE datasets show that our method reduces reconstruction errors by over 20% compared to recent state of the art

Antecedents and Consequences of Customer Satisfaction: Do They Differ Across Online and Offline Purchases?

Retailers seek to utilize both online and offline purchase channels strategically to satisfy customers and thrive in the marketplace. Unfortunately, current multichannel research is deficient in answering what drives customers’ satisfaction, and consequently their loyalty, differently when customers purchase online versus at a physical store. This gap in knowledge can be a significant concern for retailers due to the negative impact of having dissatisfied customers on their bottom lines. Using a version of the American Customer Satisfaction Index (ACSI) model, we demonstrate several important purchase-channel differences in the antecedents of customer satisfaction and its subsequent effect on customer loyalty. Specifically, we show that when retail customers buy electronic goods online they view purchase value as a significant attribute in rating satisfaction, and are more satisfaction-sensitive when making repurchase decisions than when they purchase offline. On the other hand, the overall quality of the purchase experience and customer expectations are stronger drivers of customer satisfaction in the offline purchases. We provide evidence that these differences between the channels generally persist across customer demographics (gender, age, and education) and broader product categories, and we also discuss the specific contexts where they do not. Our work offers actionable guidance to retailers seeking to enhance customer satisfaction and loyalty across both the online and offline channels

Cancer incidence in British vegetarians

Background: Few prospective studies have examined cancer incidence among vegetarians. Methods: We studied 61 566 British men and women, comprising 32 403 meat eaters, 8562 non-meat eaters who did eat fish ('fish eaters') and 20 601 vegetarians. After an average follow-up of 12.2 years, there were 3350 incident cancers of which 2204 were among meat eaters, 317 among fish eaters and 829 among vegetarians. Relative risks (RRs) were estimated by Cox regression, stratified by sex and recruitment protocol and adjusted for age, smoking, alcohol, body mass index, physical activity level and, for women only, parity and oral contraceptive use. Results: There was significant heterogeneity in cancer risk between groups for the following four cancer sites: stomach cancer, RRs (compared with meat eaters) of 0.29 (95% CI: 0.07–1.20) in fish eaters and 0.36 (0.16–0.78) in vegetarians, P for heterogeneity=0.007; ovarian cancer, RRs of 0.37 (0.18–0.77) in fish eaters and 0.69 (0.45–1.07) in vegetarians, P for heterogeneity=0.007; bladder cancer, RRs of 0.81 (0.36–1.81) in fish eaters and 0.47 (0.25–0.89) in vegetarians, P for heterogeneity=0.05; and cancers of the lymphatic and haematopoietic tissues, RRs of 0.85 (0.56–1.29) in fish eaters and 0.55 (0.39–0.78) in vegetarians, P for heterogeneity=0.002. The RRs for all malignant neoplasms were 0.82 (0.73–0.93) in fish eaters and 0.88 (0.81–0.96) in vegetarians (P for heterogeneity=0.001). Conclusion: The incidence of some cancers may be lower in fish eaters and vegetarians than in meat eaters

Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction

It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al. \cite{LARS}), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: 1) the local geometry of samples is well preserved for low dimensional data representation, 2) both the margin maximization and the classification error minimization are considered for sparse projection calculation, 3) the projection matrix of MEN improves the parsimony in computation, 4) the elastic net penalty reduces the over-fitting problem, and 5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.Comment: 33 pages, 12 figure

The effect of ultrasound pretreatment on some selected physicochemical properties of black cumin (Nigella Sativa)

Background In the present study, the effects of ultrasound pretreatment parameters including irradiation time and power on the quantity of the extracted phenolic compounds quantity as well as on some selected physicochemical properties of the extracted oils including oil extraction efficiency, acidity and peroxide values, color, and refractive index of the extracted oil of black cumin seeds with the use of cold press have been studied. Methods For each parameter, three different levels (30, 60, and 90 W) for the ultrasound power and (30, 45, and 60 min) and for the ultrasound irradiation time were studied. Each experiment was performed in three replications. Results The achieved results revealed that, with enhancements in the applied ultrasound power, the oil extraction efficiency, acidity value, total phenolic content, peroxide value, and color parameters increased significantly (P 0.05). Conclusions In summary, it could be mentioned that the application of ultrasound pretreatment in the oil extraction might improve the oil extraction efficiency, the extracted oil’s quality, and the extracted phenolic compounds content.info:eu-repo/semantics/publishedVersio