Search

We argue that recent technology developments hold great promises for health and wellbeing. In our view, recent advances of (1) smart tools and wearable sensors of diverse kinds, (2) data collection and data mining methods, (3) 3D visual recording and visual processing methods, (4) 3D models of the environment with robust physics engine, and last but not least, (5) new applications of human computing and crowdsourcing started the revolution. We are neither claiming nor excluding that human intelligence will be reached in some years from now, but make the above claim, which is both weaker and stronger. We believe that fast developments for health and wellbeing are the question of active collaboration between health and wellbeing experts and motivated engineers.

We treat the problem of searching for hidden multi-dimensional independent auto-regressive processes (auto-regressive independent process analysis, AR-IPA). Independent subspace analysis (ISA) can be used to solve the AR-IPA task. The so-called separation theorem simplifies the ISA task considerably: the theorem enables one to reduce the task to one-dimensional blind source separation task followed by the grouping of the coordinates. However, the grouping of the coordinates still involves two types of combinatorial problems: (a) the number of the independent subspaces and their dimensions, and then (b) the permutation of the estimated coordinates are to be determined. Here, we generalize the separation theorem. We also show a non-combinatorial procedure, which—under certain conditions—can treat these two combinatorial problems. Numerical simulations have been conducted. We investigate problems that fulfill sufficient conditions of the theory and also others that do not. The success of the numerical simulations indicates that further generalizations of the separation theorem may be feasible.

Background The ATP-Binding Cassette (ABC)-transporter MultiDrug Resistance Protein 1 (MDR1) and Multidrug Resistance Related Protein 1 (MRP1) are expressed on the surface of enterocytes, which has led to the belief that these high capacity transporters are responsible for modulating chemosensitvity of colorectal cancer. Several immunohistochemistry and reverse transcription polymerase chain reaction (RT-PCR) studies have provided controversial results in regards to the expression levels of these two ABC-transporters in colorectal cancer. Our study was designed to determine the yet uninvestigated functional activity of MDR1 and MRP1 transporters in normal human enterocytes compared to colorectal cancer cells from surgical biopsies. Methods 100 colorectal cancer and 28 adjacent healthy mucosa samples were obtained by intraoperative surgical sampling. Activity of MDR1 and MRP1 of viable epithelial and cancer cells were determined separately with the modified calcein-assay for multidrug resistance activity and sufficient data of 73 cancer and 11 healthy mucosa was analyzed statistically. Results Significantly decreased mean MDR1 activity was found in primary colorectal cancer samples compared to normal mucosa, while mean MRP1 activity showed no significant change. Functional activity was not affected by gender, age, stage or grade and localization of the tumor. Conclusion We found lower MDR activity in cancer cells versus adjacent, apparently, healthy control tissue, thus, contrary to general belief, MDR activity seems not to play a major role in primary drug resistance, but might rather explain preferential/selective activity of Irinotecan and/or Oxaliplatin. Still, this picture might be more complex since chemotherapy by itself might alter MDR activity, and furthermore, today limited data is available about MDR activity of cancer stem cells in colorectal cancers. Virtual slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1675739129145824

In this paper, we present a probabilistic method that can improve the efficiency of document classification when applied to structured documents. The analysis of the structure of a document is the starting point of document classification. Our method is designed to augment other classification schemes and complement pre-filtering information extraction procedures to reduce uncertainties. To this end, a probabilistic distribution on the structure of XML documents is introduced. We show how to parameterise existing learning methods to describe the structure distribution efficiently. The learned distribution is then used to predict the classes of unseen documents. Novelty detection making use of the structure-based distribution function is also discussed. Demonstration on model documents and on Internet XML documents are presented.