| 1. |
- Klionsky, Daniel J., et al.
(författare)
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Guidelines for the use and interpretation of assays for monitoring autophagy
- 2012
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Ingår i: Autophagy. - : Informa UK Limited. - 1554-8635 .- 1554-8627. ; 8:4, s. 445-544
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Forskningsöversikt (refereegranskat)abstract
- In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field.
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| 2. |
- Haenssle, H A, et al.
(författare)
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Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.
- 2018
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Ingår i: Annals of Oncology. - : Elsevier BV. - 1569-8041 .- 0923-7534. ; 29:8, s. 1836-1842
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to larger groups of dermatologists are lacking.Google's Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists' diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN's performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge.In level-I dermatologists achieved a mean (±standard deviation) sensitivity and specificity for lesion classification of 86.6% (±9.3%) and 71.3% (±11.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (±9.6%, P = 0.19) and specificity to 75.7% (±11.7%, P < 0.05). The CNN ROC curve revealed a higher specificity of 82.5% when compared with dermatologists in level-I (71.3%, P < 0.01) and level-II (75.7%, P < 0.01) at their sensitivities of 86.6% and 88.9%, respectively. The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79, P < 0.01). The CNN scored results close to the top three algorithms of the ISBI 2016 challenge.For the first time we compared a CNN's diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians' experience, they may benefit from assistance by a CNN's image classification.This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; https://www.drks.de/drks_web/).
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| 3. |
- Hirano, Ginji, et al.
(författare)
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Automatic diagnosis of melanoma using hyperspectral data and GoogLeNet
- 2020
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Ingår i: Skin Research and Technology. - : Wiley. - 0909-752X .- 1600-0846. ; 26:6, s. 891-897
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Tidskriftsartikel (refereegranskat)abstract
- Background: Melanoma is a type of superficial tumor. As advanced melanoma has a poor prognosis, early detection and therapy are essential to reduce melanoma-related deaths. To that end, there is a need to develop a quantitative method for diagnosing melanoma. This paper reports the development of such a diagnostic system using hyperspectral data (HSD) and a convolutional neural network, which is a type of machine learning. Materials and Methods: HSD were acquired using a hyperspectral imager, which is a type of spectrometer that can simultaneously capture information about wavelength and position. GoogLeNet pre-trained with Imagenet was used to model the convolutional neural network. As many CNNs (including GoogLeNet) have three input channels, the HSD (involving 84 channels) could not be input directly. For that reason, a “Mini Network” layer was added to reduce the number of channels from 84 to 3 just before the GoogLeNet input layer. In total, 619 lesions (including 278 melanoma lesions and 341 non-melanoma lesions) were used for training and evaluation of the network. Results and Conclusion: The system was evaluated by 5-fold cross-validation, and the results indicate sensitivity, specificity, and accuracy of 69.1%, 75.7%, and 72.7% without data augmentation, 72.3%, 81.2%, and 77.2% with data augmentation, respectively. In future work, it is intended to improve the Mini Network and to increase the number of lesions.
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| 4. |
- Kato, Kana, et al.
(författare)
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Performance improvement of automated melanoma diagnosis system by data augmentation
- 2020
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Ingår i: Advanced Biomedical Engineering. - : Japanese Society for Medical and Biological Engineering. - 2187-5219. ; 9, s. 62-70
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Tidskriftsartikel (refereegranskat)abstract
- Color information is an important tool for diagnosing melanoma. In this study, we used a hyper-spectral imager (HSI), which can measure color information in detail, to develop an automated melanoma diagnosis system. In recent years, the effectiveness of deep learning has become more widely accepted in the field of image recognition. We therefore integrated the deep convolutional neural network with transfer learning into our system. We tried data augmentation to demonstrate how our system improves diagnostic performance. 283 melanoma lesions and 336 non-melanoma lesions were used for the analysis. The data measured by HSI, called the hyperspectral data (HSD), were converted to a single-wavelength image averaged over plus or minus 3 nm. We used GoogLeNet which was pre-trained by ImageNet and then was transferred to analyze the HSD. In the transfer learning, we used not only the original HSD but also artificial augmentation dataset to improve the melanoma classification performance of GoogLeNet. Since GoogLeNet requires three-channel images as input, three wavelengths were selected from those single-wavelength images and assigned to three channels in wavelength order from short to long. The sensitivity and specificity of our system were estimated by 5-fold cross-val-idation. The results of a combination of 530, 560, and 590 nm (combination A) and 500, 620, and 740 nm (com-bination B) were compared. We also compared the diagnostic performance with and without the data augmentation. All images were augmented by inverting the image vertically and/or horizontally. Without data augmentation, the respective sensitivity and specificity of our system were 77.4% and 75.6% for combination A and 73.1% and 80.6% for combination B. With data augmentation, these numbers improved to 79.9% and 82.4% for combination A and 76.7% and 82.2% for combination B. From these results, we conclude that the diagnostic performance of our system has been improved by data augmentation. Furthermore, our system suc-ceeds to differentiate melanoma with a sensitivity of almost 80%.
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| 5. |
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| 6. |
- Lennerfors, Thomas Taro, 1979-, et al.
(författare)
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Editorial Preface: Why Cross-National Studies Between Japan and Sweden?
- 2021
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Ingår i: Review of Socionetwork Strategies. - : Springer Nature.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- For the last decades, people’s lives, public and private organizations, and societies have been increasingly intertwined with developments in information and communication technologies (ICT). From the early beginnings of ICT, as is shown in the 2011 BBC documentary All Watched Over by Machines of Loving Grace, there were positive visions embedded in ICT that human beings would be able to relate to each other in an ethical way and lead environmentally sustainable lives. ICT could assist democracy, equal treatment, access to resources, and not the least it could help us plan for a resource-efficient society. Today, the view of ICT is more nuanced. ICT is seen as a perfectly malleable, in other words multi-purpose technology [3], which can be used to promote positive values (democracy, equal treatment etc.), as well as support less socially accepted values (crime, digital divide, environmental pollution). Thus, since the 1980s there is a lively debate about ICT development and usage, where ICT is assessed from an ethical perspective and where it is discussed how we can make more ethical ICT. A current debate concerns whether ICT should be as transparent as possible, in other words, promote the autonomy and decision-making capacities of the user, or whether it should nudge users into socially accepted behaviours. Furthermore, since the 2000s, peripheral questions about the environmental sustainability of ICT have become more central, since the energy consumption of ICT is significant and that the waste flow of ICT equipment is hazardous and growing. Most of the research within sustainable ICT is technical, while there are exceptions. The recent advancement of ICT centred on big data, Internet of Things, robotics and Artificial Intelligence has compelled us to reconsider the meaning of human existence, autonomy, freedom and dignity. [8, 9] has raised an alarm over the advent of surveillance capitalism, an emergent, but invisible, logic of accumulation, of which big data is both a condition and an expression, and cautioned about its posing serious threat to freedom and democracy.
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| 7. |
- Persson, Anders, et al.
(författare)
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We Mostly Think Alike : Individual Differences in Attitude Towards AI in Sweden and Japan
- 2021
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Ingår i: REVIEW OF SOCIONETWORK STRATEGIES. - : Springer Nature. - 2523-3173 .- 1867-3236. ; 15:1, s. 123-142
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Tidskriftsartikel (refereegranskat)abstract
- Attitudes towards artificial intelligence (AI) and social robots are often depicted as different in Japan, compared to other western countries, such as Sweden. Several different reasons for why there are general differences in attitudes have been suggested. In this study, five hypotheses based on previous literature were investigated. Rather than attempting to establish general differences between groups, subjects were sampled from the respective populations, and correlations between the hypothesized confounding factors and attitudes were investigated within the groups between individuals. The hypotheses in this exploratory study concerned: (H1) animistic beliefs in inanimate objects and phenomena, (H2) worry about unemployment due to AI deployment, (H3) perceived positive or negative portrayal of AI in popular culture, (H4) familiarity with AI, and (H5) relational closeness and privacy with AI. No clear correlations between attitudes and animistic belief (H1), or portrayal of AI in popular culture (H3) could be observed. When it comes to the other attributes, worry about unemployment (H2), familiarity with AI (H4), and relational closeness and privacy (H5), the correlations were similar for the individuals in both groups and in line with the hypotheses. Thus, the general picture following this exploratory study is that individuals in the two populations are more alike than different.
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| 8. |
- Zeichner, Sarah S., et al.
(författare)
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Polycyclic aromatic hydrocarbons in samples of Ryugu formed in the interstellar medium
- 2023
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Ingår i: Science. - : AMER ASSOC ADVANCEMENT SCIENCE. - 0036-8075 .- 1095-9203. ; 382:6677, s. 1411-1415
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Tidskriftsartikel (refereegranskat)abstract
- Polycyclic aromatic hydrocarbons (PAHs) contain less than or similar to 20% of the carbon in the interstellar medium. They are potentially produced in circumstellar environments (at temperatures greater than or similar to 1000 kelvin), by (similar to 10 kelvin) interstellar clouds, or by processing of carbon-rich dust grains. We report isotopic properties of PAHs extracted from samples of the asteroid Ryugu and the meteorite Murchison. The doubly-C-13 substituted compositions (Delta 2x(13)C values) of the PAHs naphthalene, fluoranthene, and pyrene are 9 to 51 parts per thousand higher than values expected for a stochastic distribution of isotopes. The Delta 2x(13)C values are higher than expected if the PAHs formed in a circumstellar environment, but consistent with formation in the interstellar medium. By contrast, the PAHs phenanthrene and anthracene in Ryugu samples have Delta 2x(13)C values consistent with formation by higher-temperature reactions.
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