Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features

One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201

Exploiting Event Log Event Attributes in RNN Based Prediction

In predictive process analytics, current and historical process data in event logs are used to predict future. E.g., to predict the next activity or how long a process will still require to complete. Recurrent neural networks (RNN) and its subclasses have been demonstrated to be well suited for creating prediction models. Thus far, event attributes have not been fully utilized in these models. The biggest challenge in exploiting them in prediction models is the potentially large amount of event attributes and attribute values. We present a novel clustering technique which allows for trade-offs between prediction accuracy and the time needed for model training and prediction. As an additional finding, we also find that this clustering method combined with having raw event attribute values in some cases provides even better prediction accuracy at the cost of additional time required for training and prediction.Peer reviewe

Microstructural imaging of the human brain with a 'super-scanner': 10 key advantages of ultra-strong gradients for diffusion MRI

The key component of a microstructural diffusion MRI 'super-scanner' is a dedicated high-strength gradient system that enables stronger diffusion weightings per unit time compared to conventional gradient designs. This can, in turn, drastically shorten the time needed for diffusion encoding, increase the signal-to-noise ratio, and facilitate measurements at shorter diffusion times. This review, written from the perspective of the UK National Facility for In Vivo MR Imaging of Human Tissue Microstructure, an initiative to establish a shared 300 mT/m-gradient facility amongst the microstructural imaging community, describes ten advantages of ultra-strong gradients for microstructural imaging. Specifically, we will discuss how the increase of the accessible measurement space compared to a lower-gradient systems (in terms of Δ, b-value, and TE) can accelerate developments in the areas of 1) axon diameter distribution mapping; 2) microstructural parameter estimation; 3) mapping micro-vs macroscopic anisotropy features with gradient waveforms beyond a single pair of pulsed-gradients; 4) multi-contrast experiments, e.g. diffusion-relaxometry; 5) tractography and high-resolution imaging in vivo and 6) post mortem; 7) diffusion-weighted spectroscopy of metabolites other than water; 8) tumour characterisation; 9) functional diffusion MRI; and 10) quality enhancement of images acquired on lower-gradient systems. We finally discuss practical barriers in the use of ultra-strong gradients, and provide an outlook on the next generation of 'super-scanners'

Rescue of secretion of rare-disease associated misfolded mutant glycoproteins in UGGT1 knock-out mammalian cells

Endoplasmic reticulum (ER) retention of misfolded glycoproteins is mediated by the ER-localised eukaryotic glycoprotein secretion checkpoint, UDP-glucose glycoprotein glucosyl-transferase (UGGT). The enzyme recognises a misfolded glycoprotein and flags it for ER retention by re-glucosylating one of its N-linked glycans. In the background of a congenital mutation in a secreted glycoprotein gene, UGGT-mediated ER retention can cause rare disease, even if the mutant glycoprotein retains activity (“responsive mutant”). Using confocal laser scanning microscopy, we investigated here the subcellular localisation of the human Trop-2-Q118E, E227K and L186P mutants, which cause gelatinous drop-like corneal dystrophy (GDLD). Compared with the wild type Trop-2, which is correctly localised at the plasma membrane, these Trop-2 mutants are retained in the ER. We studied fluorescent chimeras of the Trop-2 Q118E, E227K and L186P mutants in mammalian cells harbouring CRISPR/Cas9-mediated inhibition of the UGGT1 and/or UGGT2 genes. The membrane localisation of the Trop-2 Q118E, E227K and L186P mutants was successfully rescued in UGGT1-/- cells. UGGT1 also efficiently reglucosylated Trop-2-Q118E-EYFP in cellula. The study supports the hypothesis that UGGT1 modulation would constitute a novel therapeutic strategy for the treatment of pathological conditions associated to misfolded membrane glycoproteins (whenever the mutation impairs but does not abrogate function), and it encourages the testing of modulators of ER glycoprotein folding quality control as broad-spectrum rescue-of-secretion drugs in rare diseases caused by responsive secreted glycoprotein mutants

Propionic Acid Produced by Propionibacterium acnes Strains Contributes to Their Pathogenicity

Propionibacterium acnes is an important member of the skin microbiome. The bacterium can initiate signalling events and changes in cellular properties in keratinocytes. The aim of this study was to analyse the effect of the bacterium on an immortalized human keratinocyte cell line. The results show that various P. acnes strains affect the cell-growth properties of these cells differentially, inducing cytotoxicity in a strain-specific and dosedependent manner. We propose that bacterially secreted propionic acid may contribute to the cytotoxic effect. This acid has a role in maintaining skin pH and exhibits antimicrobial properties, but may also have deleterious effects when the local concentration rises due to excessive bacterial growth and metabolism. These results, together with available data from the literature, may provide insight into the dual role of P. acnes in healthy skin and during pathogenic conditions, as well as the key molecules involved in these functions. Key words: immortalized keratinocyte cell line (HPV-KER); Propionibacterium acnes; acne vulgaris; short-chain fatty acid; propionic acid

Patient expectations of fair complaint handling in hospitals: empirical data

BACKGROUND: A common finding in several studies is patients' dissatisfaction with complaint handling in health care. The reasons why are for the greater part unknown. The key to an answer may be found in a better understanding of patients' expectations. We investigated patients' expectations of complaint handling in hospitals. METHODS: Subjects were patients who had lodged a complaint at the complaint committees of 74 hospitals in the Netherlands. A total of 424 patients (response 75%) completed a written questionnaire at the start of the complaint procedures. Derived from justice theory, we asked what they expected from fair procedures, fair communication and fair outcome of complaint handling. RESULTS: The predominant reason for complainants to lodge a complaint was to prevent the incident from happening again. Complainants expected fair procedures from the complaint committee, in particular an impartial position. This was most important to 87% of the complainants. They also expected to be treated respectfully. Furthermore, they expected the hospital and the professional involved to respond to their complaint. A change in hospital performances was the most wanted outcome of complaint handling, according to 79% of the complainants. They also expected disclosure from the professionals. Professionals should admit a mistake when it had occurred. More complainants (65%) considered it most important to get an explanation than an apology (41%). Only 32% of complainants expected the professional to make an effort to restore the doctor-patient relationship. A minority of complainants (7%) wanted financial compensation. CONCLUSION: Nearly all complainants want to prevent the incident from happening again, not out of pure altruism, but in order to restore their sense of justice. We conclude that complaint handling that does not allow for change is unlikely to meet patients' expectations. Secondly, complaint handling should not be left exclusively to complaint committees, the responses of hospital and professionals are indispensable

The use of {99m}Tc-Al[2]O[3] for detection of sentinel lymph nodes in cervical cancer patients

The purpose of the study was to evaluate the feasibility of using {99m}Tc-Al[2]O[3]- based radiopharmaceutical, a novel molecular imaging agent for sentinel lymph node detection in patients with invasive cervical cancer. The study included 23 cervical cancer patients (TlaNxMx- T[2]bNxMx) treated at the Tomsk Cancer Research Institute. At 18 hours before surgery, 80 MBq of the {99m}Tc-Al[2]O[3] were injected peritumorally, followed by single-photon emission computed tomography (SPECT) of the pelvis and intraoperative SLN identification. Twenty-seven SLNs were detected by SPECT, and 34 SLNs were identified by intraoperative gamma probe. The total number of identified SLNs per patient ranged from 1 to 3(the mean number of SLNs was 1.4 per patient). The most common site for SLN detection was the external iliac region (57.2%), followed by the internal iliac, obturator, presacral and retrosacral regions (they amounted to 14%, respectively),and the parametrial region (1%). Sensitivity in detecting SLNs was 100% for intraoperative SLN identification and 79% for SPECT image