KubeNow: A Cloud Agnostic Platform for Microservice-Oriented Applications

KubeNow is a platform for rapid and continuous deployment of microservice-based applications over cloud infrastructure. Within the field of software engineering, the microservice-based architecture is a methodology in which complex applications are divided into smaller, more narrow services. These services are independently deployable and compatible with each other like building blocks. These blocks can be combined in multiple ways, according to specific use cases. Microservices are designed around a few concepts: they offer a minimal and complete set of features, they are portable and platform independent, they are accessible through language agnostic APIs and they are encouraged to use standard data formats. These characteristics promote separation of concerns, isolation and interoperability, while coupling nicely with test-driven development. Among many others, some well-known companies that build their software around microservices are: Google, Amazon, PayPal Holdings Inc. and Netflix [11]

Improving the Integration between Palliative Radiotherapy and Supportive Care: A Narrative Review

Palliative radiotherapy (PRT) is known to be effective in relieving cancer related symptoms. However, many studies and clinical practice show several barriers hindering its use and worsening the quality of patient support during PRT. Various solutions were proposed to overcome these barriers: training on PRT for supportive and palliative care specialists and training on palliative care for radiation oncologists, and introduction of pathways and organizational models specifically dedicated to PRT. Evidence on innovative organizational models and mutual training experiences is few and sparse. Therefore, the aim of this literature review is to present a quick summary of the information available on improving the PRT quality through training, new pathways, and innovative organizational models. The majority of studies on the integration of PRT with other palliative and supportive therapies present low levels of evidence being mostly retrospective analyses. However, it should be emphasized that all reports uniformly showed advantages coming from the integration of PRT with supportive therapies. To actively participate in the integration of PRT and palliative care, providing comprehensive support to the needs of patients with advanced cancer, radiation oncologists should not only plan PRT but also: (i) assess and manage symptoms and stress, (ii) rapidly refer patients to specialists in management of more complex symptoms, and (iii) participate in multidisciplinary palliative care teams. To this end, improved education in palliative care both in residency schools and during professional life through continuous medical education is clearly needed. In particular, effective training is needed for radiotherapy residents to enable them to provide patients with comprehensive palliative care. Therefore, formal teaching of adequate duration, interactive teaching methods, attendance in palliative care services, and education in advanced palliative care should be planned in post-graduated schools of radiotherapy

Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction

Unreliable predictions can occur when an artificial intelligence (AI) system is presented with data it has not been exposed to during training. We demonstrate the use of conformal prediction to detect unreliable predictions, using histopathological diagnosis and grading of prostate biopsies as example. We digitized 7788 prostate biopsies from 1192 men in the STHLM3 diagnostic study, used for training, and 3059 biopsies from 676 men used for testing. With conformal prediction, 1 in 794 (0.1%) predictions is incorrect for cancer diagnosis (compared to 14 errors [2%] without conformal prediction) while 175 (22%) of the predictions are flagged as unreliable when the AI-system is presented with new data from the same lab and scanner that it was trained on. Conformal prediction could with small samples (N = 49 for external scanner, N = 10 for external lab and scanner, and N = 12 for external lab, scanner and pathology assessment) detect systematic differences in external data leading to worse predictive performance. The AI-system with conformal prediction commits 3 (2%) errors for cancer detection in cases of atypical prostate tissue compared to 44 (25%) without conformal prediction, while the system flags 143 (80%) unreliable predictions. We conclude that conformal prediction can increase patient safety of AI-systems.publishedVersionPeer reviewe

Enabling Scalable Data Analysis on Cloud Resources with Applications in Life Science

Over the past 20 years, the rise of high-throughput methods in life science has enabled research laboratories to produce massive datasets of biological interest. When dealing with this "data deluge" of modern biology researchers encounter two major challenges: first, there is a need for substantial technical skills for dealing with Big Data and; second, infrastructure procurement becomes difficult. In connection to this second challenge, the computing model and business trend that was originally popularized by Amazon under the name of cloud computing represents an interesting opportunity. Instead of buying computing infrastructure upfront, cloud providers enable the allocation and release of virtual resources on-demand. These resources are then billed with a pay-per-use pricing model and physical infrastructure management is delegated to the provider. In this thesis, we introduce a number of methods for running Big Data analyses of biological interest using cloud computing. Considerable efforts were made in enabling the application of trusted, bioinformatics software to Big Data scenarios as opposed to reimplementing the existing codebase. Further, we improve the accessibility of the technology with the aim of reducing the entry barrier for biologists. The thesis includes 5 papers. In Papers I and II, we explore the applicability of Apache Spark, one of the leading Big Data analytics platforms in cloud environments, to two drug-discovery use cases. In Paper III, we present a general method for running bioinformatics analyses on the cloud using the microservices-oriented architecture. In Paper IV, we introduce a method that combines microservices and Apache Spark with the aim of providing the best of both technologies. In Paper V, we discuss how to reduce the entry barrier for the allocation of cloud research environments. We show that all of the developed methods scale well and we provide high-level programming interfaces for improving accessibility. We have also made the developed software publicly available