COMPOSE: Using temporal patterns for interpreting wearable sensor data with computer interpretable guidelines

This paper describes a novel temporal logic-based framework for reasoning with continuous data collected from wearable sensors. The work is motivated by the Metabolic Syndrome, a cluster of conditions which are linked to obesity and unhealthy lifestyle. We assume that, by interpreting the physiological parameters of continuous monitoring, we can identify which patients have a higher risk of Metabolic Syndrome. We define temporal patterns for reasoning with continuous data and specify the coordination mechanisms for combining different sets of clinical guidelines that relate to this condition. The proposed solution is tested with data provided by twenty subjects, which used sensors for four days of continuous monitoring. The results are compared to the gold standard. The novelty of the framework stands in extending a temporal logic formalism, namely the Event Calculus, with temporal patterns. These patterns are helpful to specify the rules for reasoning with continuous data and in combining new knowledge into one consistent outcome that is tailored to the patient's profile. The overall approach opens new possibilities for delivering patient-tailored interventions and educational material before the patients present the symptoms of the disease

Processing Diabetes mellitus composite events in MAGPIE

The focus of this research is in the definition of programmable expert Personal Health Systems (PHS) to monitor patients affected by chronic diseases using agent oriented programming and mobile computing to represent the interactions happening amongst the components of the system. The paper also discusses issues of knowledge representation within the medical domain when dealing with temporal patterns concerning the physiological values of the patient. In the presented agent based PHS the doctors can personalize for each patient monitoring rules that can be defined in a graphical way. Furthermore, to achieve better scalability, the computations for monitoring the patients are distributed among their devices rather than being performed in a centralized server. The system is evaluated using data of 21 diabetic patients to detect temporal patterns according to a set of monitoring rules defined. The system’s scalability is evaluated by comparing it with a centralized approach. The evaluation concerning the detection of temporal patterns highlights the system’s ability to monitor chronic patients affected by diabetes. Regarding the scalability, the results show the fact that an approach exploiting the use of mobile computing is more scalable than a centralized approach. Therefore, more likely to satisfy the needs of next generation PHSs. PHSs are becoming an adopted technology to deal with the surge of patients affected by chronic illnesses. This paper discusses architectural choices to make an agent based PHS more scalable by using a distributed mobile computing approach. It also discusses how to model the medical knowledge in the PHS in such a way that it is modifiable at run time. The evaluation highlights the necessity of distributing the reasoning to the mobile part of the system and that modifiable rules are able to deal with the change in lifestyle of the patients affected by chronic illnesses.Peer ReviewedPostprint (author's final draft

MHC class I-related chain A and B ligands are differentially expressed in human cervical cancer cell lines

<p>Abstract</p> <p>Background</p> <p>Natural killer (NK) cells are an important resource of the innate immune system directly involved in the spontaneous recognition and lysis of virus-infected and tumor cells. An exquisite balance of inhibitory and activating receptors tightly controls the NK cell activity. At present, one of the best-characterized activating receptors is NKG2D, which promotes the NK-mediated lysis of target cells by binding to a family of cell surface ligands encoded by the MHC class I chain-related (MIC) genes, among others. The goal of this study was to describe the expression pattern of MICA and MICB at the molecular and cellular levels in human cervical cancer cell lines infected or not with human papillomavirus, as well as in a non-tumorigenic keratinocyte cell line.</p> <p>Results</p> <p>Here we show that MICA and MICB exhibit differential expression patterns among HPV-infected (SiHa and HeLa) and non-infected cell lines (C33-A, a tumor cell line, and HaCaT, an immortalized keratinocyte cell line). Cell surface expression of MICA was higher than cell surface expression of MICB in the HPV-positive cell lines; in contrast, HPV-negative cells expressed lower levels of MICA. Interestingly, the MICA levels observed in C33-A cells were overcome by significantly higher MICB expression. Also, all cell lines released higher amounts of soluble MICB than of soluble MICA into the cell culture supernatant, although this was most pronounced in C33-A cells. Additionally, Real-Time PCR analysis demonstrated that MICA was strongly upregulated after genotoxic stress.</p> <p>Conclusions</p> <p>This study provides evidence that even when MICA and MICB share a high degree of homology at both genomic and protein levels, differential regulation of their expression and cell surface appearance might be occurring in cervical cancer-derived cells.</p

Breast cancer survival analysis agents for clinical decision support.

peer reviewedPersonalized support and assistance are essential for cancer survivors, given the physical and psychological consequences they have to suffer after all the treatments and conditions associated with this illness. Digital assistive technologies have proved to be effective in enhancing the quality of life of cancer survivors, for instance, through physical exercise monitoring and recommendation or emotional support and prediction. To maximize the efficacy of these techniques, it is challenging to develop accurate models of patient trajectories, which are typically fed with information acquired from retrospective datasets. This paper presents a Machine Learning-based survival model embedded in a clinical decision system architecture for predicting cancer survivors' trajectories. The proposed architecture of the system, named PERSIST, integrates the enrichment and pre-processing of clinical datasets coming from different sources and the development of clinical decision support modules. Moreover, the model includes detecting high-risk markers, which have been evaluated in terms of performance using both a third-party dataset of breast cancer patients and a retrospective dataset collected in the context of the PERSIST clinical study

Cervical cancer cell lines expressing NKG2D-ligands are able to down-modulate the NKG2D receptor on NKL cells with functional implications

<p>Abstract</p> <p>Background</p> <p>Cervical cancer represents the third most commonly diagnosed cancer and the fourth leading cause of cancer-related deaths in women worldwide. Natural killer (NK) cells play an important role in the defense against viruses, intracellular bacteria and tumors. NKG2D, an activating receptor on NK cells, recognizes MHC class I chain-related molecules, such as MICA/B and members of the ULBP/RAET1 family. Tumor-derived soluble NKG2D-ligands have been shown to down-modulate the expression of NKG2D on NK cells. In addition to the down-modulation induced by soluble NKG2D-ligands, it has recently been described that persistent cell-cell contact can also down-modulate NKG2D expression. The goal of this study was to determine whether the NKG2D receptor is down-modulated by cell-cell contact with cervical cancer cells and whether this down-modulation might be associated with changes in NK cell activity.</p> <p>Results</p> <p>We demonstrate that NKG2D expressed on NKL cells is down-modulated by direct cell contact with cervical cancer cell lines HeLa, SiHa, and C33A, but not with non-tumorigenic keratinocytes (HaCaT). Moreover, this down-modulation had functional implications. We found expression of NKG2D-ligands in all cervical cancer cell lines, but the patterns of ligand distribution were different in each cell line. Cervical cancer cell lines co-cultured with NKL cells or fresh NK cells induced a marked diminution of NKG2D expression on NKL cells. Additionally, the cytotoxic activity of NKL cells against K562 targets was compromised after co-culture with HeLa and SiHa cells, while co-culture with C33A increased the cytotoxic activity of the NKL cells.</p> <p>Conclusions</p> <p>Our results suggest that differential expression of NKG2D-ligands in cervical cancer cell lines might be associated with the down-modulation of NKG2D, as well as with changes in the cytotoxic activity of NKL cells after cell-cell contact with the tumor cells.</p

A global experiment on motivating social distancing during the COVID-19 pandemic

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world

Quantitative analysis of medical images: finding relevant regions-of-interest for medical decision support

In the past decades the number of medical images inspected daily in health centers, as well as the complexity of imaging parameters have increased tremendously. An efficient quantitative analysis could improve health care by enabling a more objective interpretation of these imaging studies. The main goal of this thesis was to propose and evaluate novel methods that detect and quantify regions-of-interest (ROIs) in medical images. Challenges in medical image annotation and medical case-based retrieval were organized within a research group (VISCERAL) and are reviewed as a scientific contribution of this work. Moreover, multimodal (using both text and visual data) medical case-based retrieval systems are proposed both for radiology and digital pathology data, tackling the navigation of large-scale hospital repositories. By segmenting anatomical structures in full patient scans and measuring visual features in preselected regions, medical professionals can then prioritize their attention to the more significant structures in the images