Neurotoxicity of tetramethylammonium ion on larval and juvenile zebrafish: Effects on neurobehaviors and multiple biomarkers.

Tetramethylammonium hydroxide (TMAH) is an important compound that utilized and released by the rapidly expanding semiconductor industry, which could hardly be removed by the conventional wastewater treatment techniques. As a cholinergic agonist, the tetramethylammonium ion (TMA+) has been reported to induce toxicity to muscular and respiratory systems of mammals and human, however the toxicity on aquatic biota remains poorly known. We investigated the neurotoxic effects of TMA+ exposure on zebrafish, based on neurobehavior tests and a series of biomarkers. Significant inhibitions on the swimming distance of zebrafish larvae were observed when the exposure level exceeded 50 mg/L, and significant alterations on swimming path angles (straight and deflective movements) occurred even at 10 mg/L. The tested neurobehavioral endpoints of zebrafish larvae were significantly positively correlated with reactive oxygen species (ROS) and malondialdehyde (MDA), significantly negatively related with the activities of antioxidant enzymes, but not significantly correlated with the level of acetylcholinesterase (AChE). Such relationship indicates that the observed neurotoxic effects on swimming behavior of zebrafish larvae is mainly driven by oxidative stress, rather than the alterations of neurotransmitter. At the highest exposure concentration (200 mg/L), TMA+ evoked more severe toxicity on zebrafish juveniles, showing significantly stronger elevation on the MDA activity, and greater inhibitions on the activities of antioxidant enzymes and AChE, suggesting juveniles were more susceptible to TMA+ exposure than larval zebrafish.

Novel molecular classification and prognosis of papillary renal cell carcinoma based on a large-scale CRISPR-Cas9 screening and machine learning.

Papillary renal cell carcinoma (PRCC) is a highly heterogeneous cancer, and PRCC patients with advanced/metastatic subgroup showed obviously shorter survival compared to other kinds of renal cell carcinomas. However, the molecular mechanism and prognostic predictors of PRCC remain unclear and are worth deep studying. The aim of this study is to identify novel molecular classification and construct a reliable prognostic model for PRCC. The expression data were retrieved from TCGA, GEO, GTEx and TARGET databases. CRISPR data was obtained from Depmap database. The key genes were selected by the intersection of CRISPR-Cas9 screening genes, differentially expressed genes, and genes with prognostic capacity in PRCC. The molecular classification was identified based on the key genes. Drug sensitivity, tumor microenvironment, somatic mutation, and survival were compared among the novel classification. A prognostic model utilizing multiple machine learning algorithms based on the key genes was developed and tested by independent external validation set. Our study identified three clusters (C1, C2 and C3) in PRCC based on 41 key genes. C2 had obviously higher expression of the key genes and lower survival than C1 and C3. Significant differences in drug sensitivity, tumor microenvironment, and mutation landscape have been observed among the three clusters. By utilizing 21 combinations of 9 machine learning algorithms, 9 out of 41 genes were chosen to construct a robust prognostic signature, which exhibited good prognostic ability. SERPINH1 was identified as a critical gene for its strong prognostic ability in PRCC by univariate and multiple Cox regression analyses. Quantitative real-time PCR and Western blot demonstrated that SERPINH1 mRNA and protein were highly expressed in PRCC cells compared with normal human renal cells. This study exhibited a new molecular classification and prognostic signature for PRCC, which may provide a potential biomarker and therapy target for PRCC patients.

Insight into advanced oxidation processes for the degradation of fluoroquinolone antibiotics: Removal, mechanism, and influencing factors.

The enrichment and transport of antibiotics in the environments pose many potential hazards to aquatic animals and humans, which has become one of the public health challenges worldwide. As a widely used class of antibiotics, fluoroquinolones (FQs) generally accumulated in the environments as traditional sewage treatment plants cannot completely remove them. Advanced oxidation processes (AOPs) have been shown to be a promising method for the abatement of antibiotic contamination. In this review, influencing factors and relevant mechanisms of FQs removal by various AOPs were summarized. Compared with other AOPs, photocatalytic ozone may be considered as a cost-effective method for degrading FQs. Finally, the benefits and application restrictions of AOPs were discussed, along with proposed research directions to provide new insights into the control of FQs pollutant via AOPs in practical applications.

Fast Prediction of RF-induced Heating for Sacral Neuromodulation System Exposed to Multi-Channel 2 RF Field at 3T MRI.

This paper presents a fast method to predict the radiofrequency (RF) induced heating for Sacral Neuromodulation System (SNM) under multi-channel 2 (MC-2) RF field of 3 Tesla (T) magnetic resonance imaging (MRI) system by using the artificial neural network (ANN). The raw computational model for the SNM was based on the transfer function approach. The MC-2 parallel transmission RF field at 3T MRI exposure was considered for 2 independent channels, which have an exposure space of -15 dB to 15 dB magnitude difference and -180 degrees to 170 degrees phase difference. A total number of 535,680 study cases that cover all possible shimming conditions and the corresponding temperature rises are collected from raw calculation data. The ANN was used as the surrogate model to predict the temperature rises against the incident electromagnetic field distributions. 40320 cases were used for training while the rest data sets were used for testing. The ANN can estimate the temperature rises for each human model in a small exposure sampling space. The testing performance of the ANN has a correlation coefficient higher than 0.99 and the mean absolute error was less than 0.12°C. It is demonstrated that the ANN can be used as an efficient tool for quick temperature rise estimation under MRI 3T shimming.

A Roadmap Towards Standards for Neurally Controlled End Effectors.

The control and manipulation of various types of end effectors such as powered exoskeletons, prostheses, and 'neural' cursors by brain-machine interface (BMI) systems has been the target of many research projects. A seamless "plug and play" interface between any BMI and end effector is desired, wherein similar user's intent cause similar end effectors to behave identically. This report is based on the outcomes of an IEEE Standards Association Industry Connections working group on End Effectors for Brain-Machine Interfacing that convened to identify and address gaps in the existing standards for BMI-based solutions with a focus on the end-effector component. A roadmap towards standardization of end effectors for BMI systems is discussed by identifying current device standards that are applicable for end effectors. While current standards address basic electrical and mechanical safety, and to some extent, performance requirements, several gaps exist pertaining to unified terminologies, data communication protocols, patient safety and risk mitigation.