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Structure, function and regulation of the hsp90 machinery
Heat shock protein 90 (Hsp90) is an ATP-dependent molecular chaperone which is essential in eukaryotes. It is required for the activation and stabilization of a wide variety of client proteins and many of them are involved in important cellular pathways. Since Hsp90 affects numerous physiological processes such as signal transduction, intracellular transport, and protein degradation, it became an interesting target for cancer therapy. Structurally, Hsp90 is a flexible dimeric protein composed of three different domains which adopt structurally distinct conformations. ATP binding triggers directionality in these conformational changes and leads to a more compact state. To achieve its function, Hsp90 works together with a large group of cofactors, termed co-chaperones. Co-chaperones form defined binary or ternary complexes with Hsp90, which facilitate the maturation of client proteins. In addition, posttranslational modifications of Hsp90, such as phosphorylation and acetylation, provide another level of regulation. They influence the conformational cycle, co-chaperone interaction, and inter-domain communications. In this review, we discuss the recent progress made in understanding the Hsp90 machinery.
Journal Article
Enhancing students’ critical thinking skills
There is a need for effective methods to teach critical thinking (CT). One instructional method that seems promising is comparing correct and erroneous worked examples (i.e., contrasting examples). The aim of the present study, therefore, was to investigate the effect of contrasting examples on learning and transfer of CT-skills, focusing on avoiding biased reasoning. Students (N = 170) received instructions on CT and avoiding biases in reasoning tasks, followed by: (1) contrasting examples, (2) correct examples, (3) erroneous examples, or (4) practice problems. Performance was measured on a pretest, immediate posttest, 3-week delayed posttest, and 9-month delayed posttest. Our results revealed that participants’ reasoning task performance improved from pretest to immediate posttest, and even further after a delay (i.e., they learned to avoid biased reasoning). Surprisingly, there were no differences in learning gains or transfer performance between the four conditions. Our findings raise questions about the preconditions of contrasting examples effects. Moreover, how transfer of CT-skills can be fostered remains an important issue for future research.
Journal Article
The History of for example and for instance as Markers of Exemplification, Selection and Argumentation (1600-1999)
This article analyses the use of the example markers for example and for instance in exemplifying, selective and argumentative constructions. Of these three uses, exemplification—twofold sequences with a first general unit or hyperonym and a second more specific item or hyponym—has received recurrent attention in the literature, whereas selection—constructions where the first element is omitted—and argumentation—the use of example markers to connect whole chunks of discourse—have long been ignored. The present study, using data from ARCHER 3.2, shows that the three uses have coexisted since at least the second half of the seventeenth century and that argumentation prevails in both British and American English. Moreover, example markers are very productive in certain genres, such as science, sermons and advertising. Additionally, even though the primary function of example markers is to introduce their scope domain, they have developed different pragmatic values that bring them closer to the category of discourse markers. Thus, for example, their use as mitigators makes them an optimal tool for smoothing interaction and hence reducing the risk of offending our interlocutor.
Este artículo analiza el uso de los marcadores for example y for instance en construcciones ejemplificativas, selectivas y argumentativas. De estos tres usos, la ejemplificación—a saber, estructuras con una primera unidad más genérica o hiperónimo y un segundo elemento más específico o hipónimo—ha recibido una atención recurrente en la literatura, mientras que la selección—construcciones donde se omite el primer elemento genérico—y la argumentación—el uso de marcadores para conectar fragmentos completos de discurso—han sido ignoradas durante mucho tiempo. Este estudio, basado en datos de ARCHER 3.2, muestra que los tres usos coexisten desde al menos la segunda mitad del siglo diecisiete y que la argumentación prevalece tanto en inglés británico como americano. Además, estos marcadores son muy productivos en ciertos géneros, como la ciencia, los sermones y la publicidad. Por otro lado, aunque la función principal de for example y for instance es introducir ejemplos, ambos han desarrollado diferentes valores pragmáticos que los acercan a la categoría de marcadores del discurso. Así, por ejemplo, su uso como mitigadores los convierte en una herramienta óptima para suavizar la interacción y, por tanto, reducir el riesgo de ofender a nuestro interlocutor.
Journal Article
Data exploration using example-based methods
Data usually comes in a plethora of formats and dimensions, rendering the exploration and information extraction processes challenging. Thus, being able to perform exploratory analyses in the data with the intent of having an immediate glimpse on some of the data properties is becoming crucial. Exploratory analyses should be simple enough to avoid complicate declarative languages (such as SQL) and mechanisms, and at the same time retain the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user, or the analyst, circumvents query languages by using examples as input. An example is a representative of the intended results, or in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind, but may not able to (easily) express. They can be useful in cases where a user is looking for information in an unfamiliar dataset, when the task is particularly challenging like finding duplicate items, or simply when they are exploring the data. In this book, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how that different data types require different techniques, and present algorithms that are specifically designed for relational, textual, and graph data. The book presents also the challenges and the new frontiers of machine learning in online settings which recently attracted the attention of the database community. The lecture concludes with a vision for further research and applications in this area.
Book
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples raises our concerns in adopting deep learning to safety-critical applications. As a result, we have witnessed increasing interests in studying attack and defense mechanisms for DNN models on different data types, such as images, graphs and text. Thus, it is necessary to provide a systematic and comprehensive overview of the main threats of attacks and the success of corresponding countermeasures. In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for three most popular data types, including images, graphs and text.
Journal Article
Exemplary epic : Silius Italicus' Punica
The force of example was a distinctive determiner of Roman identity. In this study of the representation of certain central characters in Silius Italicus' 'Punica', Ben Tipping considers the virtues & vices they embody, their status as exemplars, & the process by which Silius as epic poet heroizes, demonizes, & establishes models.
Book
AT‐AER: Adversarial Training With Adaptive Example Reuse
Adversarial training (AT) is widely regarded as a crucial defense method for deep neural networks against adversarial attacks. Most of the existing AT methods suffer from the problems of insufficient coverage of perturbation space and robust overfitting. In view of this, we propose an AT framework with adaptive example reuse (AT‐AER) to help improve the adversarial robustness of deep models. In AT‐AER, a new concept named 2nd‐order adversarial example (AE) is proposed by adaptively filtering AEs generated during the historical training phase, which achieves sufficient coverage of diverse attack directions. Meanwhile, by analysing the fundamental causes of robust overfitting, we propose the strategies of wave descending learning rate (WDLR), cosine increasing weight decay (CIWD) and cosine increasing attack strength (CIAS) in collaboration with AT‐AER to optimise models. In addition, the Stochastic Weight Averaging (SWA) technique is introduced to further improve the stability of training. Finally, experiments on three benchmark datasets show that AT‐AER exhibits significant advantages in the face of strong adversarial attacks. Its adaptive mechanism effectively alleviates the phenomenon of robust overfitting where the performance difference between the best model and the last model is less than 1%. The study further reveals that using traditional weak attacks (e.g., FGSM) to evaluate the robustness of models may lead to a false sense of reliability, indicating the necessity of using strong attacks for robustness evaluation. This study provides a solution for AT that balances efficiency and performance.
Journal Article