Crewsmann6470

The total toxic equivalent concentrations of 16 PAHs in topsoil ranged from 3.73 to 105.66 ng/g (mean, 30.93 ng/g), suggesting that exposure to the basin's topsoil does not pose a risk to the environment or public health according to the Canadian soil quality guidelines. A risk assessment revealed that the total PAH concentrations in surface water posed a low ecological risk.PURPOSE To recommend a new simple and explicit index termed the anteroposterior diameter of the lateral ventricle index (ALVI) for assessing brain ventricular size in neuroimaging and to compare Evans index (EI) between idiopathic normal pressure hydrocephalus (iNPH) patients and age-matched healthy elderly subjects. METHODS Retrospective measurements of ventricular volume (VV), relative VV (RVV), the EI, and the ALVI were taken from thin-section CT scans for 23 pre-shunt-insertion iNPH patients and 62 age-matched healthy elderly volunteers. The area under the receiver operating characteristic (ROC) curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to assess the effectiveness of ALVI scores for predicting VV. RESULTS The correlations between VV or RVV and ALVI scores (VV, r = 0.957; RVV, r = 0.983) were significantly stronger than the corresponding correlations with EI scores (VV, r = 0.843; RVV, r = 0.840). The AUC for ALVI scores was significantly greater than the AUC for EI scores. Furthermore, with the inclusion of the ALVI, the NRI value was 0.14 and the IDI value was 0.14; these improvements were also statistically significant. CONCLUSION The ALVI is a more accurate and more explicitly defined marker of VV than the EI and assesses ventricular enlargement effectively. We suggest that ventricular enlargement of the healthy elderly be defined by ALVI > 0.50.PURPOSE Recent randomized-controlled clinical trials have provided preliminary evidence for expanding the time window of intravenous thrombolysis (IVT) in acute ischemic stroke (AIS) patients by applying certain neuroimaging criteria. We prospectively assessed the potential eligibility for IVT in the extended time window (4.5-9 h) among consecutive AIS patients treated in a comprehensive stroke center during a nine-month period. METHODS Potential eligibility for IVT in the extended time window was evaluated by using inclusion criteria from the EXTEND trial. All patients were underwent baseline emergent neurovascular imaging using either computed tomography angiography/computed tomography perfusion (CTA/CTP) or magnetic resonance angiography/magnetic resonance perfusion (MRA/MRP). Images were post processed by the automated software RAPID. RESULTS Our study population consisted of 317 AIS patients, and, among them, 31 (9.8 %) patients were presented in the time window of 4.5-9 h. Seven patients (2.2 %) fulfilled the EXTEND neuroimaging criteria. Four patients (1.3 %) were treated with IVT because they fulfilled both clinical and neuroimaging EXTEND criteria. Patients eligible for EXTEND neuroimaging criteria had no ischemic core lesion, whereas the mean volume of critical hypoperfusion was relatively small (17.0 ± 11.8 ml). There was no hemorrhagic complication in any of the patients treated with IVT. The median mRS score at three months was 0 (range 0-3) among patients who were eligible for EXTEND neuroimaging criteria. CONCLUSION Our everyday clinical practice experience suggests 9.8 % of consecutive AIS patients present in the 4.5-9 h window and 2.2 % adhere to EXTEND neuroimaging eligibility criteria for IVT. Only 1.3% of AIS is eligible for IVT according to EXTEND neuroimaging and clinical eligibility criteria.Magnetically controlled capsule endoscopy (MCE) system has been used to screen gastric lesions. However, the visualization performance of MCE in the esophagus has not been investigated systematically. String method improved the ability of capsule endoscopy (CE) for esophageal observing; however, the string attachment is complicated and cannot be detached with the CE inside the esophagus. We used a modified string, called detachable string attached to MCE. The aim of the study was to compare the observation performance of MCE with and without the detachable string. A total of 238 participants with upper gastrointestinal symptoms and a healthy check who refused esophagogastroduodenoscopy examination were retrospectively divided into the detachable string MCE group and the MCE alone group from June 2016 to May 2018. A suction cap with a thin, hollow string was attached to the MCE system in the detachable string group. Circumferential visualization of the esophagus including the upper, middle, and lower esophagusional Society for Diseases of the Esophagus. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.Clustering is central to many data-driven bioinformatics research and serves a powerful computational method. In particular, clustering helps at analyzing unstructured and high-dimensional data in the form of sequences, expressions, texts and images. Further, clustering is used to gain insights into biological processes in the genomics level, e.g. clustering of gene expressions provides insights on the natural structure inherent in the data, understanding gene functions, cellular processes, subtypes of cells and understanding gene regulations. Subsequently, clustering approaches, including hierarchical, centroid-based, distribution-based, density-based and self-organizing maps, have long been studied and used in classical machine learning settings. In contrast, deep learning (DL)-based representation and feature learning for clustering have not been reviewed and employed extensively. Since the quality of clustering is not only dependent on the distribution of data points but also on the learned representation, deep neural networks can be effective means to transform mappings from a high-dimensional data space into a lower-dimensional feature space, leading to improved clustering results. In this paper, we review state-of-the-art DL-based approaches for cluster analysis that are based on representation learning, which we hope to be useful, particularly for bioinformatics research. Further, we explore in detail the training procedures of DL-based clustering algorithms, point out different clustering quality metrics and evaluate several DL-based approaches on three bioinformatics use cases, including bioimaging, cancer genomics and biomedical text mining. We believe this review and the evaluation results will provide valuable insights and serve a starting point for researchers wanting to apply DL-based unsupervised methods to solve emerging bioinformatics research problems. © The authors 2020. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.The use of machine learning (ML) has become prevalent in the genome engineering space, with applications ranging from predicting target site efficiency to forecasting the outcome of repair events. However, jargon and ML-specific accuracy measures have made it hard to assess the validity of individual approaches, potentially leading to misinterpretation of ML results. This review aims to close the gap by discussing ML approaches and pitfalls in the context of CRISPR gene-editing applications. Specifically, we address common considerations, such as algorithm choice, as well as problems, such as overestimating accuracy and data interoperability, by providing tangible examples from the genome-engineering domain. Equipping researchers with the knowledge to effectively use ML to better design gene-editing experiments and predict experimental outcomes will help advance the field more rapidly. © The Author(s) 2020. Published by Oxford University Press.Mitragyna speciosa (Kratom) has emerged as a recreational drug and a substance of medicinal intrigue. Although the drug was initially used recreationally for its sedating and euphoric effects, more recently its use has been associated with the non-medically supervised treatment of opioid abstinence syndrome. Volasertib concentration Mitragynine is the principal pharmacologically active alkaloid in kratom. Although metabolites of mitragynine have been identified, the cytochrome P450 (CYP450) enzymes responsible for its biotransformation are still under investigation. The goal of this study was to contribute further knowledge regarding CYP450 activity as it relates to mitragynine. Recombinant cytochrome P450 enzymes (rCYPs) were used to investigate the isoforms involved in its metabolism. Biotransformational products were identified using liquid chromatography-quadrupole/time of flight-mass spectrometry. Four rCYP enzymes (2C18, 2C19, 2D6 and 3A4) were found to contribute to the metabolism of mitragynine. 7-Hydroxymitragynine (which has an affinity for the mu-opioid receptor >10-folds that of morphine) was produced exclusively by 3A4. 9-O-demethylmitragynine, the most abundant metabolite in vitro (and the most prevalent metabolite in urine among kratom users) was produced by 2C19, 3A4 and 2D6. 16-Carboxymitragynine was produced by rCYPs 2D6, 2C19 and 2C18. 2C19 was solely responsible for the formation of 9-O-demethyl-16-carboxymitragynine. In vitro rCYP studies were compared with phase I metabolites in urine from cases involving mitragynine. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.De novo microdeletion of chromosome 2p15-16.1 presents clinically recognizable phenotypes that include mental retardation, autism, and microcephaly. Chromosomal maintenance 1 (CRM1) is a gene commonly missing in patients with 2p15-16.1 microdeletion and one of two genes found in the smallest deletion case. In this study, we investigate the role and mechanism of Crm1 in the developing mouse brain by inhibiting the protein or knocking down the gene in vivo. Inhibition of Crm1 reduces the proliferation and increases p53-dependent apoptosis of the cortical neural progenitors, thereby impeding the growth of embryonic cerebral cortex. Live imaging of mitosis in ex vivo embryonic brain slices reveals that inhibition of CRM1 arrests the cortical progenitors at metaphase. The arrested cells eventually slip into a pseudo-G1 phase without chromosome segregation. The mitotic slippage cells are marked by persistent expression of the spindle assembly checkpoint (SAC), repressing of which rescues the cells from apoptosis. Our study reveals that activating the SAC and inducing the mitotic slippage may lead to apoptosis of the cortical neural progenitors. The resulting cell death may well contribute to microcephaly associated with microdeletion of chromosome 2p15-16.1 involving CRM1. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.As an important post-translational modification (PTM), protein phosphorylation is involved in the regulation of almost all of biological processes in eukaryotes. Due to the rapid progress in mass spectrometry-based phosphoproteomics, a large number of phosphorylation sites (p-sites) have been characterized but remain to be curated. Here, we briefly summarized the current progresses in the development of data resources for the collection, curation, integration and annotation of p-sites in eukaryotic proteins. Also, we designed the eukaryotic phosphorylation site database (EPSD), which contained 1 616 804 experimentally identified p-sites in 209 326 phosphoproteins from 68 eukaryotic species. In EPSD, we not only collected 1 451 629 newly identified p-sites from high-throughput (HTP) phosphoproteomic studies, but also integrated known p-sites from 13 additional databases. Moreover, we carefully annotated the phosphoproteins and p-sites of eight model organisms by integrating the knowledge from 100 additional resources that covered 15 aspects, including phosphorylation regulator, genetic variation and mutation, functional annotation, structural annotation, physicochemical property, functional domain, disease-associated information, protein-protein interaction, drug-target relation, orthologous information, biological pathway, transcriptional regulator, mRNA expression, protein expression/proteomics and subcellular localization.