Helping Made Easy: Ease of Argument Generation Enhances Intentions to Help

Previous work has shown that self-generating arguments is more persuasive than reading arguments provided by others, particularly if self-generation feels easy. The present study replicates and extends these findings by providing evidence for fluency effects on behavioral intention in the realm of helping. In two studies, participants were instructed to either self-generate or read two versus ten arguments about why it is good to help. Subsequently, a confederate asked them for help. Results show that self-generating few arguments is more effective than generating many arguments. While this pattern reverses for reading arguments, easy self-generation is the most effective strategy compared to all other conditions. These results have important implications for fostering behavioral change in all areas of life

A tomographic workflow to enable deep learning for X-ray based foreign object detection

Detection of unwanted (‘foreign’) objects within products is a common procedure in many branches of industry for maintaining production quality. X-ray imaging is a fast, non-invasive and widely applicable method for foreign object detection. Deep learning has recently emerged as a powerful approach for recognizing patterns in radiographs (i.e., X-ray images), enabling automated X-ray based foreign object detection. However, these methods require a large number of training examples and manual annotation of these examples is a subjective and laborious task. In this work, we propose a Computed Tomography (CT) based method for producing training data for supervised learning of foreign object detection, with minimal labor requirements. In our approach, a few representative objects are CT scanned and reconstructed in 3D. The radiographs that are acquired as part of the CT-scan data serve as input for the machine learning method. High-quality ground truth locations of the foreign objects are obtained through accurate 3D reconstructions and segmentations. Using these segmented volumes, corresponding 2D segmentations are obtained by creating virtual projections. We outline the benefits of objectively and reproducibly generating training data in this way. In addition, we show how the accuracy depends on the number of objects used for the CT reconstructions. The results show that in this workflow generally only a relatively small number of representative objects (i.e., fewer than 10) are needed to achieve adequate detection performance in an industrial setting

Tomosipo: fast, flexible, and convenient 3D tomography for complex scanning geometries in Python

Tomography is a powerful tool for reconstructing the interior of an object from a series of projection images. Typically, the source and detector traverse a standard path (e.g., circular, helical). Recently, various techniques have emerged that use more complex acquisition geometries. Current software packages require significant handwork, or lack the flexibility to handle such geometries. Therefore, software is needed that can concisely represent, visualize, and compute reconstructions of complex acquisition geometries. We present tomosipo, a Python package that provides these capabilities in a concise and intuitive way. Case studies demonstrate the power and flexibility of tomosipo

Deciphering long dsRNA fate in mammalian cells

In Arabidopsis thaliana, Caenorhabditis elegans and Drosophila, long dsRNA (ldsRNA) accumulation during virus infection, transposon activation or transgene expression, elicits a potent RNA interference (RNAi) response: ldsRNA is processed into small interfering RNAs by Dicer proteins, before loading into Argonaute (Ago) proteins which assemble into RNA-induced silencing complex (RISC) to slice complementary RNA or methylate DNA. The response can also be amplified and spread systemically throughout the organism. In mammals, specific RNAi can be observed in specific cell lines, but this effect is often mitigated by activation type I interferon (IFN) dsRNA binding protein (dsRBPs) which are responsible for cytotoxicity. In this PhD thesis, we aim to unravel these processes using state-of-the-art biochemical-, genetic- and molecular approaches. Here, we first show, that Dicer interactors protein activator of protein kinase R (PKR) (PACT) and TAR-RNA binding protein (TRBP) rapidly congregate in ldsRNA-rich granules (DRGs) upon ldsRNA stress. Mass spectrometry analysis reveals the recruitment of a compendium of additional RNA silencing- and type I IFN factors to these DRGs, thus providing evidence for an integrated cellular platform for recognition and processing of ldsRNA. Second, we confirm that modulating a key innate immunity receptor (PKR) in 293T cells dampens the ldsRNA-induced cytotoxic effects and uncovers a specific RNAi response on transgene- and endogenous targets in an Ago2- and a Dicer-dependent manner. Interestingly, our in-depth analysis reveals two modes of Dicer processing depending on the exogenous ldsRNA template used, suggesting ldsRNA sequence or structure specifically affects these processes. Furthermore, we also reveal that TRBP is a master regulator of Dicer-processing and Ago-loading during RNAi, most notably of ldsRNA templates which are fully processed by Dicer. Finally, we perform several genome-wide forward genetic screens to dissect the mechanisms leading to the uptake and sensing of exogenous ldsRNA. These experiments allowed us to identify a network of proteins involved in these processes, from cell surface binding to nuclear ldsRNA responses. By extensive validation of knock-outs from the type I IFN screen we managed to (i) molecularly dissect ldsRNA uptake by lipofection and (ii) describe a novel role of signal transducer and activator of transcription 1 (STAT1) in human RNAi. All together the work of this PhD thesis sheds new light on the cellular- and molecular impact of ldsRNA in mammalian cells, an important challenge in the fields of RNA therapeutics and virology

Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning

An important challenge in hyperspectral imaging tasks is to cope with the large number of spectral bins. Common spectral data reduction methods do not take prior knowledge about the task into account. Consequently, sparsely occurring features that may be essential for the imaging task may not be preserved in the data reduction step. Convolutional neural network (CNN) approaches are capable of learning the specific features relevant to the particular imaging task, but applying them directly to the spectral input data is constrained by the computational efficiency. We propose a novel supervised deep learning approach for combining data reduction and image analysis in an end-to-end architecture. In our approach, the neural network component that performs the reduction is trained such that image features most relevant for the task are preserved in the reduction step. Results for two convolutional neural network architectures and two types of generated datasets show that the proposed Data Reduction CNN (DRCNN) approach can produce more accurate results than existing popular data reduction methods, and can be used in a wide range of problem settings. The integration of knowledge about the task allows for more image compression and higher accuracies compared to standard data reduction methods

A tomographic workflow to enable deep learning for X-ray based foreign object detection

Detection of unwanted (‘foreign’) objects within products is a common procedure in many branches of industry for maintaining production quality. X-ray imaging is a fast, non-invasive and widely applicable method for foreign object detection. Deep learning has recently emerged as a powerful approach for recognizing patterns in radiographs (i.e., X-ray images), enabling automated X-ray based foreign object detection. However, these methods require a large number of training examples and manual annotation of these examples is a subjective and laborious task. In this work, we propose a Computed Tomography (CT) based method for producing training data for supervised learning of foreign object detection, with minimal labor requirements. In our approach, a few representative objects are CT scanned and reconstructed in 3D. The radiographs that are acquired as part of the CT-scan data serve as input for the machine learning method. High-quality ground truth locations of the foreign objects are obtained through accurate 3D reconstructions and segmentations. Using these segmented volumes, corresponding 2D segmentations are obtained by creating virtual projections. We outline the benefits of objectively and reproducibly generating training data in this way. In addition, we show how the accuracy depends on the number of objects used for the CT reconstructions. The results show that in this workflow generally only a relatively small number of representative objects (i.e., fewer than 10) are needed to achieve adequate detection performance in an industrial setting

Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning

An important challenge in hyperspectral imaging tasks is to cope with the large number of spectral bins. Common spectral data reduction methods do not take prior knowledge about the task into account. Consequently, sparsely occurring features that may be essential for the imaging task may not be preserved in the data reduction step. Convolutional neural network (CNN) approaches are capable of learning the specific features relevant to the particular imaging task, but applying them directly to the spectral input data is constrained by the computational efficiency. We propose a novel supervised deep learning approach for combining data reduction and image analysis in an end-to-end architecture. In our approach, the neural network component that performs the reduction is trained such that image features most relevant for the task are preserved in the reduction step. Results for two convolutional neural network architectures and two types of generated datasets show that the proposed Data Reduction CNN (DRCNN) approach can produce more accurate results than existing popular data reduction methods, and can be used in a wide range of problem settings. The integration of knowledge about the task allows for more image compression and higher accuracies compared to standard data reduction methods

A tomographic workflow to enable deep learning for X-ray based foreign object detection

Detection of unwanted (‘foreign’) objects within products is a common procedure in many branches of industry for maintaining production quality. X-ray imaging is a fast, non-invasive and widely applicable method for foreign object detection. Deep learning has recently emerged as a powerful approach for recognizing patterns in radiographs (i.e., X-ray images), enabling automated X-ray based foreign object detection. However, these methods require a large number of training examples and manual annotation of these examples is a subjective and laborious task. In this work, we propose a Computed Tomography (CT) based method for producing training data for supervised learning of foreign object detection, with minimal labor requirements. In our approach, a few representative objects are CT scanned and reconstructed in 3D. The radiographs that are acquired as part of the CT-scan data serve as input for the machine learning method. High-quality ground truth locations of the foreign objects are obtained through accurate 3D reconstructions and segmentations. Using these segmented volumes, corresponding 2D segmentations are obtained by creating virtual projections. We outline the benefits of objectively and reproducibly generating training data in this way. In addition, we show how the accuracy depends on the number of objects used for the CT reconstructions. The results show that in this workflow generally only a relatively small number of representative objects (i.e., fewer than 10) are needed to achieve adequate detection performance in an industrial setting