Institution
University of Tübingen
Education•Tübingen, Germany•
About: University of Tübingen is a education organization based out in Tübingen, Germany. It is known for research contribution in the topics: Population & Immune system. The organization has 40555 authors who have published 84108 publications receiving 3015320 citations. The organization is also known as: Eberhard Karls University & Eberhard-Karls-Universität Tübingen.
Topics: Population, Immune system, Transplantation, Context (language use), Gene
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the authors present a simple but effective gradient-based approach that can be exploited to systematically assess the security of several, widely-used classification algorithms against evasion attacks.
Abstract: In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data. In one pertinent, well-motivated attack scenario, an adversary may attempt to evade a deployed system at test time by carefully manipulating attack samples. In this work, we present a simple but effective gradient-based approach that can be exploited to systematically assess the security of several, widely-used classification algorithms against evasion attacks. Following a recently proposed framework for security evaluation, we simulate attack scenarios that exhibit different risk levels for the classifier by increasing the attacker's knowledge of the system and her ability to manipulate attack samples. This gives the classifier designer a better picture of the classifier performance under evasion attacks, and allows him to perform a more informed model selection (or parameter setting). We evaluate our approach on the relevant security task of malware detection in PDF files, and show that such systems can be easily evaded. We also sketch some countermeasures suggested by our analysis.
937 citations
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TL;DR: The theoretical background defining its strength and directionality, a systematic analysis of its occurrence and interaction geometries in protein-ligand complexes, and recent examples where halogen bonding has been successfully harnessed for lead identification and optimization are provided.
Abstract: Halogen bonding has been known in material science for decades, but until recently, halogen bonds in protein–ligand interactions were largely the result of serendipitous discovery rather than rational design. In this Perspective, we provide insights into the phenomenon of halogen bonding, with special focus on its role in drug discovery. We summarize the theoretical background defining its strength and directionality, provide a systematic analysis of its occurrence and interaction geometries in protein–ligand complexes, and give recent examples where halogen bonding has been successfully harnessed for lead identification and optimization. In light of these data, we discuss the potential and limitations of exploiting halogen bonds for molecular recognition and rational drug design.
934 citations
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TL;DR: The nucleotide sequence of icaABC suggests that the three genes are organized in an operon and that they are co‐transcribed from the mapped ica A promoter, suggesting that Ica A has N‐acetylglucosaminyltransferase activity in the formation of PIA.
Abstract: The Staphylococcus epidermidis genes icaABC are involved in the synthesis of the polysaccharide intercellular adhesin (PIA), which is located mainly on the cell surface, as shown by immunofluorescence studies with PIA-specific antiserum. PIA was shown to be a linear beta-1,6-linked glucosaminoglycan composed of at least 130 2-deoxy-2-amino-D-glucopyranosyl residues of which 80-85% are N-acetylated, the rest being non-N-acetylated and positively charged. A transposon insertion in the icaABC gene cluster (ica, intercellular adhesion) led to the loss of several traits, such as the ability to form a biofilm on a polystyrene surface, cell aggregation, and PIA production. The mutant could be complemented by transformation with the icaABC-carrying plasmid pCN27. Transfer of pCN27 into the heterologous host Staphylococcus carnosus led to the formation of large cell aggregates, the formation of a biofilm on a glass surface, and PIA expression. The nucleotide sequence of icaABC suggests that the three genes are organized in an operon and that they are co-transcribed from the mapped icaA promoter. IcaA contains four potential transmembrane helices, indicative of a membrane location. The deduced IcaA sequence shows similarity to those of polysaccharide-polymerizing enzymes, the most pronounced being with a Rhizobium meliloti N-acetylglucosaminyltransferase involved in lipo-chitin biosynthesis (22.5% overall identity and 37.4% overall similarity). This similarity suggests that IcaA has N-acetylglucosaminyltransferase activity in the formation of the beta-1, 6-linked N-acetyl-D-glucosaminyl polymer. IcaB is secreted into the medium and contains a typical signal peptide. IcaC is hydrophobic and contains six predicted transmembrane helices distributed over its entire length, typical for an integral membrane protein. Neither IcaB nor IcaC shares similarity with known proteins, and their function is unknown. Inactivation of icaA, icaB, or icaC in pCN27 led to the complete loss of the intercellular adhesion phenotype in S. carnosus, suggesting that all three genes are involved in intercellular adhesion, PIA expression, and translocation.
931 citations
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TL;DR: In this article, the root mean square error (RMSE) and the Akaike Information Criterion (AIC) were used to compare different data mining algorithms for modelling soil visible-near infrared (vis-NIR) diffuse reflectance spectra and to assess the interpretability of the results.
928 citations
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TL;DR: This paper showed that for typical psychological and psycholinguistic data, higher power is achieved without inflating Type I error rate if a model selection criterion is used to select a random effect structure that is supported by the data.
928 citations
Authors
Showing all 41039 results
Name | H-index | Papers | Citations |
---|---|---|---|
John Q. Trojanowski | 226 | 1467 | 213948 |
Lily Yeh Jan | 162 | 467 | 73655 |
Monique M.B. Breteler | 159 | 546 | 93762 |
Wolfgang Wagner | 156 | 2342 | 123391 |
Thomas Meitinger | 155 | 716 | 108491 |
Hermann Brenner | 151 | 1765 | 145655 |
Amartya Sen | 149 | 689 | 141907 |
Bernhard Schölkopf | 148 | 1092 | 149492 |
Niels Birbaumer | 142 | 835 | 77853 |
Detlef Weigel | 142 | 516 | 84670 |
Peter Lang | 140 | 1136 | 98592 |
Marco Colonna | 139 | 512 | 71166 |
António Amorim | 136 | 1477 | 96519 |
Alexis Brice | 135 | 870 | 83466 |
Elias Campo | 135 | 761 | 85160 |