The EC Number is the numerical identifier for substances in the EC Inventory. The EC Inventory is a combination of three independent European lists of substances from the previous EU chemicals regulatory frameworks (EINECS, ELINCS and the NLP-list). More information about the EC Inventory can be found here. This section provides an overview of the calculated volume at which the substance is manufactured or imported to the European Economic Area (EU28 + Iceland, Liechtenstein and Norway). Additionally, if available, information on the use of the substance and how consumers and workers are likely to be exposed to it can also be displayed here. The quality and correctness of the information submitted to ECHA remains the responsibility of the data submitter. The type of uses and classifications may vary between different submissions to ECHA and for a full understanding it is recommended to consult the source data. Information on applicable regulatory frameworks is also automatically generated and may not be complete or up to date. It is the responsibility of the substance manufacturers and importers to consult official publications, e.g. the electronic edition of the Official Journal of the European Union. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s Note

The Cylinder Base Gasket 0,4 mm is a genuine spare part manufactured by Piaggio so you have peace of mind the Cylinder Base Gasket 0,4 mm part being sold by Motorcycle Spare Parts is both original and new. indoor use in long-life materials with low release rate (e.g. flooring, furniture, toys, construction materials, curtains, foot-wear, leather products, paper and cardboard products, electronic equipment). indoor use in close systems with minimal release (e.g. cooling liquids in refrigerators, oil-based electric heaters) and The purpose of the information provided under this section is to highlight the substance hazardousness in a readable format. It does not represent a new labelling, classification or hazard statement, neither reflect other factors that affect the susceptibility of the effects described, such as duration of exposure or substance concentration (e.g. in case of consumer and professional uses). Other relevant information includes the following: Conceptualization, YL; data curation, DC; formal analysis, DC; project administration, DC; writing—original draft, YL; and writing—review and editing, DC. Funding

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Impurities or additives: When a specific critical property is calculated from industry data and where the majority of data submitters have indicated that the property relates to cases containing impurities and/or additives, then the respective critical property icon is modified with an asterisk (*). This substance is registered under the REACH Regulation and is manufactured in and / or imported to the European Economic Area, at ≥ 1 000 000 tonnes per annum. Use descriptors are adapted from ECHA guidance to improve readability and may not correspond textually to descriptor codes described in Chapter R.12: Use Descriptor system of ECHA Guidance on information requirements and chemical safety assessment.

If no EU harmonised classification and labelling exists and the substance was not registered under REACH, information derived from classification and labelling (C&L) notifications to ECHA under CLP Regulation is displayed under this section. These notifications can be provided by manufacturers, importers and downstream users. ECHA maintains the C&L Inventory, but does not review or verify the accuracy of the information. Authorisation list (Annex XIV to REACH) - indicates if the substance is included in the Authorisation list. These substances cannot be placed on the market or used after a given date, unless an authorisation is granted for their specific use, or the use is exempted from authorisation. Recognised" - meaning that the concern is indicated in an official source. Recognised concerns are illustrated with a dark red icon. Sources for these are either a Harmonised C&L (CLP Regulation Annex VI) or in the Candidate list of substances of very high concern for authorisation (REACH). The materials used are as diverse as the clothing on offer in the women's fashion range. Although natural fibres are increasingly being replaced by synthetic materials, at PETER HAHN we still rely on the advantages of natural materials. Toxic to Reproduction (R) – Recognised as toxic to reproduction: comes from a harmonised C&L classifying the substance as Carc. 1A or 1B and/or an entry in the Candidate list. Potentially toxic to reproduction: comes from a harmonised C&L classifying the substance as suspected toxic to reproduction Repr. 2. Broad agreement: comes from industry data where a majority of data submitters agree the substance is toxic to reproduction. Minority position: comes from industry data where a minority of data submitters indicate the substance is toxic to reproduction. More information about reproductive toxicity here.

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Note that for readability purposes, only the pictograms, signal words and hazard statements referred in more than 5% of the notifications under CLP are displayed. Candidate List - indicates if the substance is included in the candidate list of substances of very high concern (SVHCs). The Candidate List includes substances that are subject to additional protocols and reporting obligations and which may eventually be included in the Authorisation List, further limiting their use. Broad agreement" - comes from data submitted by industry to ECHA, and indicates that the data submitted is aligned, with >= 50% of the data submitters providing the same concern. Broad agreement concerns are illustrated with a solid outlined circle icon. The described Product category (i.e. the products in which the substance may be used) may refer to uses as intermediate and under controlled conditions, for which there is no consumer exposure.

InfoCards are updated when new information is available. The date of the last update corresponds to the publication date of the InfoCard and not necessarily to the date in which the update occurred in the source data. The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author. Author Contributions Our impellers are manufactured to strict tolerances for optimal functionality unique MC97 rubber compound in many of our impellers

where N i denotes the number of the ith amino acid in the protein sequence and L denotes the length of a sequence. Obviously, ∑ V i = 1. PBT – Recognised Persistent, bioaccumulative and toxic (PBT) (or vPvB): comes from an entry in the Candidate list. Potential PBT: is shown for substances under assessment, and comes from an entry in the PBT assessment list. Broad agreement: comes from industry data where a majority of data submitters agree the substance is PBT. Minority position: comes from industry data where a minority of data submitters indicate the substance is PBT. More information about persistent, bioaccumulative and toxic substances here. Some substance identifiers may have been claimed confidential, or may not have been provided, and therefore not be displayed. EC (European Community) Number The ‘Hazard classification’ and labelling section uses the signal word, pictogram(s) and hazard statements of the substance under the harmonised classification and labelling (CLH) as its primary source of information.

In many experiments, we tried a variety of methods to extract highly recognizable features from protein sequences in the training set and used several algorithms to train the model to achieve optimal accuracy. The experimental comparison results of different features are explained in Performance of Different Features on Cross-Validation, and the experimental comparison results of different classifiers are explained in Performance of Different Classifiers on Cross-Validation. Performance of Different Features on Cross-Validation The support vector machine (SVM) ( Hearst et al., 1998) is a well-known machine learning algorithm that completes various classification tasks by constructing a separating hyperplane in the high-dimensional space. However, the training speed of support vector machines is heavily influenced by data size. To solve this problem, the sequential minimum optimization (SMO) ( Platt, 1999) algorithm was proposed, which decomposes large quadratic programming problems (OPs) of an original SVM into a series of the smallest possible QP problems. Moreover, the solution process of SMO needs no additional matrix storage, thus saving both time and space costs. Optimized to provide maximum life, especially for applications where the end user doesn’t run the engine on a regular basis: The InfoCard summarises the non-confidential data of a substance held in the databases of the European Chemicals Agency (ECHA). InfoCards are generated automatically based on the data available at the time of generation. Substances may have impurities and additives that lead to different classifications. If at least one company has indicated that the substance classification is affected by impurities or additives, this will be indicated by an informative sentence. However, substance notifications in the InfoCard are aggregated independently of the impurities and additives.

Guidance on the safe use of the substance provided by manufacturers and importers of this substance. At PETER HAHN you will find fashion for men and women, including plus size fashion and an extensive choice of fashion made from natural fibres. Above all, cashmere fashion is our passion as well as our specialty. At early stages, the research studies related to the MHC are developed based on mice experiments. With the availability of a large amount of data and development of machine learning, developing a machine learning–based model to research the MHC was feasible. Li et al. (2019) proposed an identification method of the MHC based on an extreme learning machine algorithm. Although high accuracy has been achieved, there are still many aspects worthy of further investigation ( Lv et al., 2019; Lv et al., 2021a; Lv et al., 2021b). In this study, we aim to propose a new MHC predictor, PredMHC, to further improve prediction performance. Materials and Methods Framework of PredMHC At least one company has indicated that the substance classification is affected by impurities or additives.