Chronic fatigue syndrome (CFS) is characterized by long-lasting, disabling and unexplained fatigue that is often accompanied by unrefreshing sleep. The aim of this cross-sectional study was to investigate sleep-wake rhythm and perceived sleep in adolescent CFS patients compared to healthy individuals. We analysed baseline data on 120 adolescent CFS patients and 39 healthy individuals included in the NorCAPITAL project. Activity measures from a uniaxial accelerometer (activPAL) were used to estimate mid-sleep time (mid-point of a period with sleep) and time in bed. Scores from the Karolinska Sleep Questionnaire (KSQ) were also assessed. The activity measures showed that the CFS patients stayed significantly longer in bed, had a significantly delayed mid-sleep time and a more varied sleep-wake rhythm during weekdays compared with healthy individuals. On the KSQ, the CFS patients reported significantly more insomnia symptoms, sleepiness, awakening problems and a longer sleep onset latency than healthy individuals. These results might indicate that disrupted sleep-wake phase could contribute to adolescent CFS; however, further investigations are warranted.

Abstract.A large up-to-date compendium of integrated genomic data is often required for biological data analysis. The compendium can be tens of terabytes in size, and must often be frequently updated with new experimental or metadata. Manual compendium update is cumbersome, requires a lot of unnecessary computation, and it may result in errors or inconsistencies in the compendium. We propose a transparent file based approach for adding incremental update capabilities to unmodified genomics data analysis tools and pipeline workflow managers. This approach is implemented in the GeStore system. We evaluate GeStore using a real world genomics compendium. Our results show that it is easy to add incremental updates to genomics data processing pipelines, and that incremental updates can reduce the computation time such that it becomes practical to maintain large-scale up-to-date genomics compendia on small clusters.

Biological data analysis is typically implemented using a pipeline that combines many data analysis tools and meta-databases. These pipelines must scale to very large datasets, and therefore often require parallel and distributed computing. There are many infrastructure systems for data-intensive computing. However, most biological data analysis pipelines do not leverage these systems. An important challenge is therefore to integrate biological data analysis frameworks with data-intensive computing infrastructure systems. In this paper, we describe how we have extended data-intensive computing systems to support unmodified biological data analysis tools. We also describe four approaches for integrating the extended systems with biological data analysis frameworks, and discuss challenges for such integration on production platforms. Our results demonstrate how biological data analysis pipelines can benefit from infrastructure systems for data-intensive computing.

Abstract. Biological data analysis is typically implemented using a deep pipeline that combines a wide array of tools and databases. These pipelines must scale to very large datasets, and consequently require parallel and distributed computing. It is therefore important to choose a hardware platform and underlying data management and processing systems well suited for processing large datasets. There are many infrastructure systems for such data-intensive computing. However, in our experience, most biological data analysis pipelines do not leverage these systems.We give an overview of data-intensive computing infrastructure systems, and describe how we have leveraged these for: (i) scalable fault-tolerant computing for large-scale biological data; (ii) incremental updates to reduce the resource usage required to update large-scale compendium; and (iii) interactive data analysis and exploration. We provide lessons learned and describe problems we have encountered during development and deployment. We also provide a literature survey on the use of data-intensive computing systems for biological data processing. Our results show how unmodified biological data analysis tools can benefit from infrastructure systems for data-intensive computing.

Increased levels of tumor-infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. The manual quantification of immune cells is inaccurate and time-consuming for pathologists. Our aim is to leverage a computational solution to automatically quantify TILs in standard diagnostic hematoxylin and eosin-stained sections (H&E slides) from lung cancer patients. Our approach is to transfer an open-source machine learning method for the segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of the data. Our results show that the resulting TIL quantification correlates to the patient prognosis and compares favorably to the current state-of-the-art method for immune cell detection in non-small cell lung cancer (current standard CD8 cells in DAB-stained TMAs HR 0.34, 95% CI 0.17–0.68 vs. TILs in HE WSIs: HoVer-Net PanNuke Aug Model HR 0.30, 95% CI 0.15–0.60 and HoVer-Net MoNuSAC Aug model HR 0.27, 95% CI 0.14–0.53). Our approach bridges the gap between machine learning research, translational clinical research and clinical implementation. However, further validation is warranted before implementation in a clinical setting.