Unwanted side effects of drugs are a burden on patients and a severe impediment in the development of new drugs. At the same time, adverse drug reactions (ADRs) recorded during clinical trials are an important source of human phenotypic data. It is therefore essential to combine data on drugs, targets and side effects into a more complete picture of the therapeutic mechanism of actions of drugs and the ways in which they cause adverse reactions. To this end, we have created the SIDER (‘Side Effect Resource’, http://sideeffects.embl.de) database of drugs and ADRs. The current release, SIDER 4, contains data on 1430 drugs, 5880 ADRs and 140 064 drug–ADR pairs, which is an increase of 40% compared to the previous version. For more fine-grained analyses, we extracted the frequency with which side effects occur from the package inserts. This information is available for 39% of drug–ADR pairs, 19% of which can be compared to the frequency under placebo treatment. SIDER furthermore contains a data set of drug indications, extracted from the package inserts using Natural Language Processing. These drug indications are used to reduce the rate of false positives by identifying medical terms that do not correspond to ADRs.

Database containing information on marketed medicines and their recorded adverse drug reactions. The information is extracted from public documents and package inserts. The available information include side effect frequency, drug and side effect classifications as well as links to further information, for example drug-target relations. The SIDER Side Effect Resource represents an effort to aggregate dispersed public information on side effects. To our knowledge, no such resource exist in machine-readable form despite the importance of research on drugs and their effects. The creation of this resource was motivated by the many requests for data that we received related to our paper (Campillos, Kuhn et al., Science, 2008, 321(5886):263-6.) on the utilization of side effects for drug target prediction. Inclusion of side effects as readouts for drug treatment should have many applications and we hope to be able to enhance the respective research with this resource. You may browse the drugs by name, browse the side effects by name, download the current version of SIDER, or use the search interface.

Table A. Properties of drug-side effect matrix. Table B. Methods of matching SIDER drug names to Pharmaprojects. Table C. Methods of matching SIDER side effect names to MeSH terms. Table D. Reasons SIDER drugs drop out of analysis. Table E. Cross-tabulation of drug-SE properties. Table F. Effects of requiring genetic insight and removing similar indications. Table G. Sensitivity to similarity threshold for inclusion of similar genetic associations. Table H. Sensitivity to similarity threshold for removal of similar indications. Table I. Breakdown by source of genetic evidence. Table J. Breakdown by somatic vs. germline and oncology vs. non-oncology. Table K. Properties of oncology vs. non-oncology SEs Table L. Binned analysis of numerical SE frequency. Table M. Logit model coefficients for numerical SE frequency. Table N. Binned analysis of SE frequency terms. Table O. Logit model coefficients for SE frequency terms, ordinal model. Table P. Logit model coefficients for SE frequency terms, linear term only. Table Q. Binned analysis of placebo status. Table R. Logit model coefficients for placebo status. Table S. Binned analysis of SEs by drug specificity (number of drugs where the SE is observed). Table T. Binned analysis of SEs by severity quartile. Table U. Logit model coefficients for severity analysis. Table V. SE drug specificity versus severity bin. Table W. Linear model coefficients for SE severity vs. drug specificity. Table X. Breakdown by MeSH area. Table Y. Enrichment statistics by GWAS association MeSH term. Table Z. Enrichment statistics by side effect MeSH term. Table AA. Side effects lacking genetic insight, by number of drugs Table AB. Count of drug-indication pairs with and without genetic support. Table AC. Drug-indication pairs with genetic support. Table AD. Count and base rate of drug-SE pairs by genetic support status. Table AE. OR by genetic support status, with and without sim_indic filter. Table AF. Details of drugs whose targets are genetically associated to tachycardia. (XLSX)

These are various supplementary data files that comprise the underlying data and results of our analysis of associations between reports of adverse events of drugs and in vitro bioactivities, while taking into account drug plasma concentrations. This was part of the PhD research by Ines Smit supervised by Andreas Bender and funded by Lhasa Limited, Leeds.

Adverse event reports were extracted from the Food and Drug Administration Adverse Event Reporting System (FAERS) and from the Side Effect Resource (SIDER). In vitro bioactivities were obtained from the ChEMBL database or predicted using the target prediction tool PIDGIN. Drug plasma concentrations were compiled from literature and from the ChEMBL database.

Description of the files: Data File S 1. Drug-AE relationships based on FAERS. Data File S 2. Bioactivity data plus predictions used in the analysis. Data File S 3. All positive target-AE combinations assessed for FAERS using the unbound plasma concentrations. Data File S 4. All positive target-AE combinations assessed for SIDER using the unbound plasma concentrations. Data File S 5. All positive target-AE combinations assessed for FAERS using the constant pChEMBL cut-off. Data File S 6. All positive target-AE combinations assessed for SIDER using the constant pChEMBL cut-off. Data File S 7. Share of measured versus predicted bioactivities per target for SIDER. Data File S 8. Share of measured versus predicted bioactivities per target for FAERS. Data File S 9. Extracted total drug plasma concentrations with references. Data File S 10. Computed median unbound plasma concentrations used in the analysis. Data File S 11. Previously reported safety target associations extracted and mapped to MedDRA terms (PT). Data File S 12. Previously reported safety target associations extracted and mapped to MedDRA terms (HLT).

Prediction of polypharmacological profiles of drugs enables us to investigate drug side effects and further find their new indications, i.e. drug repositioning, which could reduce the costs while increase the productivity of drug discovery. Here we describe a new computational framework to predict polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. On the basis of our previous developed drug side effects database, named MetaADEDB, a drug side effect similarity inference (DSESI) method was developed for drug–target interaction (DTI) prediction on a known DTI network connecting 621 approved drugs and 893 target proteins. The area under the receiver operating characteristic curve was 0.882 ± 0.011 averaged from 100 simulated tests of 10-fold cross-validation for the DSESI method, which is comparative with drug structural similarity inference and drug therapeutic similarity inference methods. Seven new predicted candidate target proteins for seven approved drugs were confirmed by published experiments, with the successful hit rate more than 15.9%. Moreover, network visualization of drug–target interactions and off-target side effect associations provide new mechanism-of-action of three approved antipsychotic drugs in a case study. The results indicated that the proposed methods could be helpful for prediction of polypharmacological profiles of drugs.

Marine Trackline Geophysical data represented within the side-scan sonar data are from towed instruments closer to the seafloor that use sound to image features on the ocean floor. This technique can create shadows like shining a flashlight, which help determine size and features. This system is often used to map cultural heritage sites like shipwrecks, to characterize the makeup of the seafloor, and can even be used to help biologists identify habitats of marine animals.

IntroductionMost drugs fail during development and there is a clear and unmet need for approaches to better understand mechanistically how drugs exert both their intended and adverse effects. Gaining traction in this field is the use of disease data linking genes with pathological phenotypes and combining this with drugtarget interaction data.MethodsWe introduce methodology to associate drugs with effects, both intended and adverse, using a tripartite network approach that combines drug-target and target-phenotype data, in which targets can be represented as proteins and protein domains.ResultsWe were able to detect associations for over 140,000 ChEMBL drugs and 3,800 phenotypes, represented as Human Phenotype Ontology (HPO) terms. The overlap of these results with the SIDER databases of known drug side effects was up to 10 times higher than random, depending on the target type, disease database and score threshold used. In terms of overlap with drug-phenotype pairs extracted from the literature, the performance of our methodology was up to 17.47 times greater than random. The top results include phenotype-drug associations that represent intended effects, particularly for cancers such as chronic myelogenous leukemia, which was linked with nilotinib. They also include adverse side effects, such as blurred vision being linked with tetracaine.DiscussionThis work represents an important advance in our understanding of how drugs cause intended and adverse side effects through their action on disease causing genes and has potential applications for drug development and repositioning.

Stable isotope mixing models (SIMMs) are an important tool used to study species’ trophic ecology. These models are dependent on, and sensitive to, the choice of trophic discrimination factors (TDF) representing the offset in stable isotope delta values between a consumer and their food source when they are at equilibrium. Ideally, controlled feeding trials should be conducted to determine the appropriate TDF for each consumer, tissue type, food source, and isotope combination used in a study. In reality however, this is often not feasible nor practical. In the absence of species-specific information, many researchers either default to an average TDF value for the major taxonomic group of their consumer, or they choose the nearest phylogenetic neighbour for which a TDF is available. Here, we present the SIDER package for R, which uses a phylogenetic regression model based on a compiled dataset to impute (estimate) a TDF of a consumer. We apply information on the tissue type and feeding ...

The World Spider Catalog is the first fully searchable online database covering spider taxonomy, but it has a longer history of predecessors which started with Pierre Bonnet (University of Toulouse, France) and Carl Friedrich Roewer (Bremen, Germany). Bonnet's seven scholarly books of his Bibliographia araneorum, published in three volumes 1945-1961, were fully comprehensive and covered literature on all aspects of spider biology through 1939, on more than 6400 pages. Roewer's Katalog der Araneae von 1758 bis 1940 (three books, published in two volumes, more than 2700 pages) were published 1942-1955 and covered the taxonomically useful literature through 1940 or 1954 (depending on the taxon).

The next important step was performed by Paolo M. Brignoli (University of Aquila, Italy) with his Catalogue of the Araneae described between 1940 and 1981, published 1983. This 750 pages volume filled many of the post-Roewer gaps (through 1980, with scattered coverage of later papers as well). Brignoli intended to issue Catalogue supplements at periodic intervals but this stopped due to his untimely death in 1986. Fortunately, Brignoli’s idea could be continued because Norman I. Platnick (American Museum of Natural History, New York) accepted the challenge to take over the task of preparing supplements to Brignoli's volume. In the next decade, three supplement volumes (1989, 1993, 1997) of Advances in Spider Taxonomy with together 2500 pages were published, covering the literature from 1981 through 1995 and including all synonyms, transfers, and re-descriptions from 1940 to 1980.

By the end of the 20th century it became obvious that the increasing quantity of taxonomic information could no longer be managed in the conventional way. So far more than 10’000 catalog pages and (currently) an annual influx of more than 300 taxonomic publications with descriptions of ca. 900 new species need an internet based solution. Platnick started this task with a first online version of his World Spider Catalog in 2000 and continued through 2014, with two updated versions per year, a total of 30 updates. The catalog was hosted at the American Museum of Natural History and served as HTML files per family. You can find a complete archive.

With the retirement of Platnick in 2014, the Natural History Museum Bern (Switzerland) accepted to continue Platnick’s work and took over the World Spider Catalog. All data provided by the catalog version 14.5 has been processed in order to fit into a relational database. One of the major achievements of a true database is that it is fully searchable over the complete content of spider taxonomy since 1757 when the first now acknowledged 68 spider species were described by Carl Clerck. Another important novelty is the link to the World Spider Catalog Association (WSCA) which intends to provide access to more than 12’000 taxonomic publications which are behind this database information.

The World Spider Catalog considers all taxonomically useful published work. Unpublished statements – even if correct – will not be taken over here. Also contents of websites that are not published elsewhere are not considered. Roewer and Platnick set standards for the Catalog that persist largely until today. Basically, this includes all descriptions of new species, transfers, synonymies and all taxonomically useful (i.e., illustrated) references to previously described taxa. Electronic supplements can be considered in combination with the corresponding main article. Not included are subfamilial or subgeneric divisions and allocations, or mentions of taxa in purely faunistic works (unless accompanied by useful illustrations).

The catalog entries for literature prior to 1940 do not reflect a complete re-check of the classical literature. Roewer's listings based on the classical literature have largely been accepted, and only discrepancies detected between Roewer's and Bonnet's treatments have been re-checked and resolved. These listings are not intended to supplant either Roewer's or Bonnet's volumes, but rather to provide a quick, electronically searchable guide to the most important literature on spider systematics, worldwide. Investigators doing original research should still check the listings in Roewer and Bonnet; we hope that omissions are few, but no project of this magnitude could ever be error-free.

In certain cases, published nomenclatural or taxonomical changes will not be taken over by the catalog. This includes for example cases with violations of the provisions of the International Code of Zoological Nomenclature. In debatable cases (e.g. purely typological genus-splitting without phylogenetic reasons), an expert board will decide the case democratically. However, if some published alterations are not taken over by the catalog, the respective information and reference is given anyway.

The following abbreviations are used: Male or female signs (m or f) alone indicate that palpal or epigynal illustrations are included (hence figure references without such annotations include only somatic characters, generally through scanning electron micrographs; citations are not provided for cases where authors supplied only a general view of the body). The letter D indicates an original description, either of a taxon or of a previously unknown sex. The letter T indicates that one or both sexes have been transferred from a specified genus to the one under consideration; tentative statements indicating that a species "possibly belongs" or "may belong" elsewhere are not included as transfers (or synonymies). The letter S indicates that details of one or more new synonymies can be found immediately under the generic listing; an S followed by a male or female sign indicates that a previously unknown sex has been added through a synonymy. The type species of each genus is marked with an asterisk (*).

The organization of the entries is hierarchically determined; hence synonymies at the generic level are indicated under the family (and cross-referenced under the appropriate generic) listings, but affected species are listed separately only if there are significant references to them in particular. Similarly, synonymies at the species level are listed under generic, rather than familial, headings. The brief descriptions of geographic ranges are provided only as a general guide; no attempt has been made to ensure that they are comprehensive.

Users who detect errors, of any sort, are urged to bring them to our attention (email to wsc(at)nmbe.ch).

Aims: We propose a novel machine learning approach to expand the knowledge about drug-target interactions. Our method may help to develop effective, less harmful treatment strategies and to enable the detection of novel indications for existing drugs. Methods: We developed a novel machine learning strategy to predict drug-target interactions based on drug side effects and traits from genome-wide association studies. We integrated data from the databases SIDER and GWASdb and utilized them in a unique way by a neural network approach. Results: We validate our method using drug-target interactions from the STITCH database. In addition, we compare the chemical similarity of the predicted target to known targets of the drug under consideration and present literature-based evidence for predicted interactions. We find drug combination warnings for drugs we predict to target the same protein, hinting to synergistic effects aggravating harmful events. This substantiates the translational value of our approach, because we are able to detect drugs that should be taken together with care due to common mechanisms of action. Conclusion: Taken together, we conclude that our approach is able to generate a novel and clinically applicable insight into the molecular determinants of drug action.

Software and hardware side-channel analysis databases for attack on Ascon AEAD This repository contains datasets collected for side-channel analysis attack on Ascon AEAD. The repository is divided into two folders, one for software side-channel analysis and one for hardware side-channel analysis:

• cw.zip
  • ascon_collect.ipynb : Jupyter Notebook used to collect the traces for the unprotected Ascon implementation
  • ascon_protected_collect.ipynb : Jupyter Notebook used to collect the traces for the protected Ascon implementation
  • simpleserial-ascon : Ascon firmwares used to collect the traces
• hw.zip
  • ascon_g_protected
    • test_ascon.py : Script to test the protected Ascon implementation
    • collect_lecroy.py : Script to collect traces for unprotected Ascon implementation with Lecroy oscilloscope
    • rtl_src : RTL source files for the protected Ascon implementation
  • ascon_g_unprotected
    • test_ascon.py : Script to test the unprotected Ascon implementation
    • collect_lecroy.py : Script to collect traces for unprotected Ascon implementation with Lecroy oscilloscope
    • rtl_src : RTL source files for the unprotected Ascon implementation
• helpers.zip
  • ASCON.py : Python implementation of Ascon
  • SASEBO.py : Helper functions to communicate with the SAKURA-G board
  • lecroy3.py : Helper functions for Lecroy oscilloscope
  • ascon_helper.py : Helper functions for Ascon
  • convert_trs_to_h5.py : Script to convert Trsfile traceset to HDF5 database

ascon_cw_protected.h5 : Side-channel database for software protected Ascon implementation ascon_cw_unprotected.h5 : Side-channel database for software unprotected Ascon implementation ascon_hw_protected.h5 : Side-channel database for hardware protected Ascon implementation ascon_hw_unprotected.h5 : Side-channel database for hardware unprotected Ascon implementation

ascon_cw_protected.trs : Traces for software protected Ascon implementation ascon_cw_unprotected.trs : Traces for software unprotected Ascon implementation ascon_hw_protected.trs : Traces for hardware protected Ascon implementation ascon_hw_unprotected.trs : Traces for hardware unprotected Ascon implementation

Ascon authenticated encryption attack on a Chipwhisperer STM32F4 The dataset was used for side-channel attack on Ascon initialization phase attack of the authenticated encryption mode on a ChipWhisperer STM32F4 target board. The power traces are collected with the ChipWhisperer-Lite oscilloscope at a sampling rate of 4x the target clock frequency, and captures the first call of the round function of the Chi function of Ascon permutation. The code used to collect the traces is also available in this repository, and the trace collection can be replicated with a ChipWhisperer-Lite and a STM32F4 target board.

Ascon authenticated encryption attack on a SAKURA-G FPGA The hardware designs of the unprotected and protected Ascon implementations are available in the hw folder. Both implementations are written in VHDL/Verilog and can be synthesized for Spartan6 (XC6SLX75) with Xilinx ISE. Traces are collected with a Lecroy WaveRunner 610Zi oscilloscope at a sampling rate of 500 MS/s.

Databases description: Database | Ntraces | Traces (samples) | Label* (bytes) | Metadata (bytes) | Nf | Nr | | | Key | Nonce | Plaintext | Associated data | Ciphertext | Tag ascon_cw_unprotected.h5 | 100,000 | 100,000 | 772 | 64 | 16 | 16 | 4 | 4 | 4 | 16 ascon_cw_protected.h5 | 500,000 | 500,000 | 1408 | 64 | 32 | 32 | 16 | 16 | 16 | 32 ascon_hw_unprotected.h5 | 100,000 | 100,000 | 6,000 | 64 | 16 | 16 | 4 | 4 | 4 | 16 ascon_hw_protected.h5 | 500,000 | 500,000 | 10,000 | 64 | 32 | 32 | 8 | 8 | 8 | 32

*Label computed with the intermediate_value leakage model described in ascon_helper.py

2395 Global import shipment records of Side Effect with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Dataset Card for Sypder-Syn

Spyder-Syn is a human curated variant of the Spider Text-to-SQL database. The database was created to test the robustness of text-to-SQL models for robustness of synonym substitution. The source GIT repo for Sypder-Syn is located here: https://github.com/ygan/Spider-Syn Details regarding the data perterbation methods used and objectives are described in ACL 2021: arXiv

  Paper Abstract

Recently, there has been significant progress in… See the full description on the dataset page: https://huggingface.co/datasets/aherntech/spider-syn.

Drugs with similar side-effect profiles may share similar therapeutic properties through related mechanisms of action. In this study, a drug-drug network was constructed based on the similarities between their clinical side effects. The indications of a drug may be inferred by the enriched FDA-approved functions of its neighbouring drugs in the network. We systematically screened new indications for 1234 drugs with more than 2 network neighbours, 36.87% of the drugs achieved a performance score of Normalized Discounted Cumulative Gain in the top 5 positions (NDCG@5)≥0.7, which means most of the known FDA-approved indications were well predicted at the top 5 positions. In particular, drugs for diabetes, obesity, laxatives and antimycobacterials had extremely high performance with more than 80% of them achieving NDCG@5≥0.7. Additionally, by manually checking the predicted 1858 drug-indication pairs with Expression Analysis Systematic Explorer (EASE) score≤10−5 (EASE score is a rigorously modified Fisher exact test p value), we found that 80.73% of such pairs could be verified by preclinical/clinical studies or scientific literature. Furthermore, our method could be extended to predict drugs not covered in the network. We took 98 external drugs not covered in the network as the test sample set. Based on our similarity criteria using side effects, we identified 41 drugs with significant similarities to other drugs in the network. Among them, 36.59% of the drugs achieved NDCG@5≥0.7. In all of the 106 drug-indication pairs with an EASE score≤0.05, 50.94% of them are supported by FDA approval or preclinical/clinical studies. In summary, our method which is based on the indications enriched by network neighbors may provide new clues for drug repositioning using side effects.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

4 Active Global Side Effect buyers list and Global Side Effect importers directory compiled from actual Global import shipments of Side Effect.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The lowering of high blood pressure is supposed to protect target organs from hypertensive damage. The Antihypertensive and Lipid Lowering Treatment to Prevent Heart Attack Trial was designed to compare the cardioprotective properties of three antihypertensives from different classes (lisinopril, amlodipine and doxazosin) with chlorthalidone. Despite effective blood pressure lowering and a favorable metabolic profile, the doxazosin arm of the trial had a significantly higher relative risk of cardiovascular disease and heart failure compared with the chlorthalidone arm. This article speculates on possible causes for this unexpected result and suggests that the culprit may be accentuation of the vascular effects of vasopressin, which are maximized under α-adrenergic blockade. These findings may have implications for the large number of older men who receive monotherapy with α-blockers for treatment of prostatic symptoms.

It is estimated that **** billion U.S. dollars will be spent on alternative data globally by buy-side firms in 2020. Alternative data is data used by investors to evaluate a company or investment outside of their traditional data sources, which include press releases, SEC filings, and financial statements. Some forms of alternative data include app usage, financial transactions, and geolocation data.