Hubertroelsen1239

In addition, the first-class priority-controlled species had been determined, urgently needing more attention in the future VOCs management during asphalt pavement construction.Rivers are a significant reservoir of antibiotic resistance genes (ARGs), yet the biogeographic pattern of riverine ARGs and its underlying driving forces remain poorly understood. Here, we used metagenomic approach to investigate the spatio-temporal variation of ARGs in two adjacent sub-watersheds viz. North River (NR) and West River (WR), China. The results demonstrated that Bacitracin (22.8 % of the total ARGs), multidrug (20.7 %), sulfonamide (15.2 %) and tetracycline (10.9 %) were the dominant ARG types. SourceTracker analysis indicated that sewage treatment plants as the main source of ARGs, while animal feces mainly contributed in spreading the ARGs in the upstream of NR. Random forest and network analyses confirmed that NR was under the influence of fecal pollution. PCoA analysis demonstrated that the composition of ARGs changed along with the anthropogenic gradients, while the Raup-Crick null model showed that homogenizing selection mediated by class 1 integron intI1 resulted in stable ARG communities at whole watershed scale. Structural equation models revealed that microbial community, grassland and several non-antibiotic micropollutants may also play certain roles in influencing the distribution of ARGs. Overall, the observed deterministic formation of ARGs in riverine systems calls effective management strategies to mitigate the risks of antibiotic resistance on public health.Engineering drawings are commonly used in different industries such as Oil and Gas, construction, and other types of engineering. Digitising these drawings is becoming increasingly important. This is mainly due to the need to improve business practices such as inventory, assets management, risk analysis, and other types of applications. However, processing and analysing these drawings is a challenging task. A typical diagram often contains a large number of different types of symbols belonging to various classes and with very little variation among them. Another key challenge is the class-imbalance problem, where some types of symbols largely dominate the data while others are hardly represented in the dataset. In this paper, we propose methods to handle these two challenges. First, we propose an advanced bounding-box detection method for localising and recognising symbols in engineering diagrams. Our method is end-to-end with no user interaction. Thorough experiments on a large collection of diagrams from an industrial partner proved that our methods accurately recognise more than 94% of the symbols. Secondly, we present a method based on Deep Generative Adversarial Neural Network for handling class-imbalance. The proposed GAN model proved to be capable of learning from a small number of training examples. Experiment results showed that the proposed method greatly improved the classification of symbols in engineering drawings.Research explaining the behavior of convolutional neural networks (CNNs) has gained a lot of attention over the past few years. Although many visualization methods have been proposed to explain network predictions, most fail to provide clear correlations between the target output and the features extracted by convolutional layers. In this work, we define a concept, i.e., class-discriminative feature groups, to specify features that are extracted by groups of convolutional kernels correlated with a particular image class. We propose a detection method to detect class-discriminative feature groups and a visualization method to highlight image regions correlated with particular output and to interpret class-discriminative feature groups intuitively. The experiments showed that the proposed method can disentangle features based on image classes and shed light on what feature groups are extracted from which regions of the image. We also applied this method to visualize "lost" features in adversarial samples and features in an image containing a non-class object to demonstrate its ability to debug why the network failed or succeeded.Convolutional neural networks (CNNs) are emerging as powerful tools for EEG decoding these techniques, by automatically learning relevant features for class discrimination, improve EEG decoding performances without relying on handcrafted features. Nevertheless, the learned features are difficult to interpret and most of the existing CNNs introduce many trainable parameters. Here, we propose a lightweight and interpretable shallow CNN (Sinc-ShallowNet), by stacking a temporal sinc-convolutional layer (designed to learn band-pass filters, each having only the two cut-off frequencies as trainable parameters), a spatial depthwise convolutional layer (reducing channel connectivity and learning spatial filters tied to each band-pass filter), and a fully-connected layer finalizing the classification. This convolutional module limits the number of trainable parameters and allows direct interpretation of the learned spectral-spatial​ features via simple kernel visualizations. Furthermore, we designed a post-hoc gradient-based technique to enhance interpretation by identifying the more relevant and more class-specific features. Sinc-ShallowNet was evaluated on benchmark motor-execution and motor-imagery datasets and against different design choices and training strategies. Results show that (i) Sinc-ShallowNet outperformed a traditional machine learning algorithm and other CNNs for EEG decoding; (ii) The learned spectral-spatial features matched well-known EEG motor-related activity; (iii) The proposed architecture performed better with a larger number of temporal kernels still maintaining a good compromise between accuracy and parsimony, and with a trialwise rather than a cropped training strategy. In perspective, the proposed approach, with its interpretative capacity, can be exploited to investigate cognitive/motor aspects whose EEG correlates are yet scarcely known, potentially characterizing their relevant features.A guidance document for the identification of endocrine disruptors (EDs) in the regulatory assessment of plant protection products (PPP) and biocidal products (BP) has been published by the European Chemical Agency (ECHA) and the European Food Safety Authority (EFSA). The ECHA/EFSA guidance, mainly addressing EATS (estrogen, androgen, thyroid, steroidogenesis) modalities, is intended to guide applicants and assessors of the competent regulatory authorities on the implementation of the scientific criteria for the determination of ED properties pursuant to the recently implemented PPP (EU 2018/605) and BP (EU 2017/2100) EU Regulations. In this study, a search filter for targeted literature search in context of assessing if a substance can be identified as an ED relevant for human health was developed and validated. Development of the search filter was based on the search strategy presented in the ECHA/EFSA guidance and using the estrogenic chemical Bisphenol AF (BPAF) as a model substance. Information specialists from two independent institutions developed refined search filters based on the suggested original search strategy published (ECHA/EFSA guidance - Appendix F). Articles identified by a systematic literature search for BPAF were screened for relevance with inclusion and exclusion criteria by two independent reviewers obtaining positive (relevant) and negative (irrelevant) controls. The developed search filter was quantitatively evaluated in terms of sensitivity, specificity and precision based on the positive and negative controls. The developed filter was then validated for T modality by its application to the known thyroid-disruptor perchlorate. The result is a sensitive search filter with sufficient specificity, which can be applied for all chemicals where a targeted literature search is needed to assess and identify ED properties of chemicals with relevance for humans. Future application of the filter to a broader range of chemicals may identify further points of improvement.Background Corona Virus Disease 19 (COVID-19) had a worldwide negative impact on healthcare systems, which were not used to coping with such pandemic. Adaptation strategies prioritizing COVID-19 patients included triage of patients and reduction or re-allocation of other services. The aim of our survey was to provide a real time international snapshot of modifications of breast cancer management during the COVID-19 pandemic. Methods A survey was developed by a multidisciplinary group on behalf of European Breast Cancer Research Association of Surgical Trialists and distributed via breast cancer societies. One reply per breast unit was requested. Results In ten days, 377 breast centres from 41 countries completed the questionnaire. RT-PCR testing for SARS-CoV-2 prior to treatment was reported by 44.8% of the institutions. The estimated time interval between diagnosis and treatment initiation increased for about 20% of institutions. Indications for primary systemic therapy were modified in 56% (211/377), with upfront surgery increasing from 39.8% to 50.7% (p less then 0.002) and from 33.7% to 42.2% (p less then 0.016) in T1cN0 triple-negative and ER-negative/HER2-positive cases, respectively. Sixty-seven percent considered that chemotherapy increases risks for developing COVID-19 complications. Fifty-one percent of the responders reported modifications in chemotherapy protocols. Gene-expression profile used to evaluate the need for adjuvant chemotherapy increased in 18.8%. In luminal-A tumours, a large majority (68%) recommended endocrine treatment to postpone surgery. Postoperative radiation therapy was postponed in 20% of the cases. Conclusions Breast cancer management was considerably modified during the COVID-19 pandemic. Our data provide a base to investigate whether these changes impact oncologic outcomes.Background Walking training is an essential intervention to improve the function in stroke patients. However, only a limited number of gait training strategies are available for stroke patients with relatively severe disabilities. Research question Is underwater gait training or overground gait training more effective in severe stroke patients? Methods A total of 21 patients with severe hemiplegic stroke were randomly assigned to the experimental and control groups. All participants (n = 21) received 60-minute sessions of general physical therapy, 5 times a week for a period of 12 weeks. Additionally, the experimental and control groups underwent underwater and overground walking training, respectively, for 30 min twice times a week for 12 weeks. Postural assessment for stroke score, center of pressure path length and velocity, step time and step length difference, and walking velocity were measured before and after the 12-week training. Results Both groups showed a significant decrease in the center of pressure path length and velocity after the intervention compared to the values before the intervention (p .05). In the walking variables, the step length difference changes after training between the two groups showed a significant difference (p less then .05). Remdesivir purchase In the experimental group, the step length difference increased after the intervention compared to that before the intervention (+4.55 cm), whereas that of the control group decreased (-1.25 cm). Significance In severe stroke patients, underwater gait training can be effective for improving balancing ability, but it may be less effective on the improvement of gait function than overground walking. Clinical trial registration number KCT0002587 (https//cris.nih.go.kr).