Semantic multimedia remote display for mobile thin clients

Current remote display technologies for mobile thin clients convert practically all types of graphical content into sequences of images rendered by the client. Consequently, important information concerning the content semantics is lost. The present paper goes beyond this bottleneck by developing a semantic multimedia remote display. The principle consists of representing the graphical content as a real-time interactive multimedia scene graph. The underlying architecture features novel components for scene-graph creation and management, as well as for user interactivity handling. The experimental setup considers the Linux X windows system and BiFS/LASeR multimedia scene technologies on the server and client sides, respectively. The implemented solution was benchmarked against currently deployed solutions (VNC and Microsoft-RDP), by considering text editing and WWW browsing applications. The quantitative assessments demonstrate: (1) visual quality expressed by seven objective metrics, e.g., PSNR values between 30 and 42 dB or SSIM values larger than 0.9999; (2) downlink bandwidth gain factors ranging from 2 to 60; (3) real-time user event management expressed by network round-trip time reduction by factors of 4-6 and by uplink bandwidth gain factors from 3 to 10; (4) feasible CPU activity, larger than in the RDP case but reduced by a factor of 1.5 with respect to the VNC-HEXTILE

Hypoxia and hyperglycaemia determine why some endometrial tumours fail to respond to metformin

High expression of Ki67, a proliferation marker, is associated with reduced endometrial cancer-specific survival. Pre-surgical metformin reduces tumour Ki-67 expression in some women with endometrial cancer. Metformin's anti-cancer activity may relate to effects on cellular energy metabolism. Since tumour hypoxia and glucose availability are major cellular redox determinants, we evaluated their role in endometrial cancer response to metformin. Endometrial cancer biopsies from women treated with pre-surgical metformin were tested for the hypoxia markers, HIF-1α and CA-9. Endometrial cancer cell lines were treated with metformin in variable glucose concentrations in normoxia or hypoxia and cell viability, mitochondrial biogenesis, function and energy metabolism were assessed. In women treated with metformin (n = 28), Ki-67 response was lower in hypoxic tumours. Metformin showed minimal cytostatic effects towards Ishikawa and HEC1A cells in conventional medium (25 mM glucose). In low glucose (5.5 mM), a dose-dependent cytostatic effect was observed in normoxia but attenuated in hypoxia. Tumours treated with metformin showed increased mitochondrial mass (n = 25), while in cultured cells metformin decreased mitochondrial function. Metformin targets mitochondrial respiration, however, in hypoxic, high glucose conditions, there was a switch to glycolytic metabolism and decreased metformin response. Understanding the metabolic adaptations of endometrial tumours may identify patients likely to derive clinical benefit from metformin

Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding

We propose a model for tagging unstructured texts with an arbitrary number of terms drawn from a tree-structured vocabulary (i.e., an ontology). We treat this as a special case of sequence-to-sequence learning in which the decoder begins at the root node of an ontological tree and recursively elects to expand child nodes as a function of the input text, the current node, and the latent decoder state. We demonstrate that this method yields state-of-the-art results on the important task of assigning MeSH terms to biomedical abstracts

Machine learning reduced workload with minimal risk of missing studies: development and evaluation of an RCT classifier for Cochrane Reviews

BACKGROUND: To describe the development, calibration and evaluation of a machine learning classifier designed to reduce study identification workload in Cochrane for producing systematic reviews. METHODS: A machine learning classifier for retrieving RCTs was developed (the ‘Cochrane RCT Classifier’), with the algorithm trained using a dataset of title-abstract records from Embase, manually labelled by the Cochrane Crowd. The classifier was then calibrated using a further dataset of similar records manually labelled by the Clinical Hedges team, aiming for 99% recall. Finally, the recall of the calibrated classifier was evaluated using records of RCTs included in Cochrane Reviews that had abstracts of sufficient length to allow machine classification. RESULTS: The Cochrane RCT Classifier was trained using 280,620 records (20,454 of which reported RCTs). A classification threshold was set using 49,025 calibration records (1,587 of which reported RCTs) and our bootstrap validation found the classifier had recall of 0.99 (95% CI 0.98 to 0.99) and precision of 0.08 (95% CI 0.06 to 0.12) in this dataset. The final, calibrated RCT classifier correctly retrieved 43,783 (99.5%) of 44,007 RCTs included in Cochrane Reviews but missed 224 (0.5%). Older records were more likely to be missed than those more recently published. CONCLUSIONS: The Cochrane RCT Classifier can reduce manual study identification workload for Cochrane reviews, with a very low and acceptable risk of missing eligible RCTs. This classifier now forms part of the Evidence Pipeline, an integrated workflow deployed within Cochrane to help improve the efficiency of the study identification processes that support systematic review production

A Neural Candidate-Selector Architecture for Automatic Structured Clinical Text Annotation

We consider the task of automatically annotating free texts describing clinical trials with concepts from a controlled, structured medical vocabulary. Specifically, we aim to build a model to infer distinct sets of (ontological) concepts describing complementary clinically salient aspects of the underlying trials: the populations enrolled, the interventions administered and the outcomes measured, i.e., the PICO elements. This important practical problem poses a few key challenges. One issue is that the output space is vast, because the vocabulary comprises many unique concepts. Compounding this problem, annotated data in this domain is expensive to collect and hence sparse. Furthermore, the outputs (sets of concepts for each PICO element) are correlated: specific populations (e.g., diabetics) will render certain intervention concepts likely (insulin therapy) while effectively precluding others (radiation therapy). Such correlations should be exploited. We propose a novel neural model that addresses these challenges. We introduce a Candidate-Selector architecture in which the model considers setes of candidate concepts for PICO elements, and assesses their plausibility conditioned on the input text to be annotated. This relies on a 'candidate set' generator, which may be learned or relies on heuristics. A conditional discriminative neural model then jointly selects candidate concepts, given the input text. We compare the predictive performance of our approach to strong baselines, and show that it outperforms them. Finally, we perform a qualitative evaluation of the generated annotations by asking domain experts to assess their quality

Proton spectroscopic imaging of brain metabolites in basal ganglia of healthy older adults

Object: We sought to measure brain metabolite levels in healthy older people. Materials and methods: Spectroscopic imaging at the level of the basal ganglia was applied in 40 participants aged 73–74 years. Levels of the metabolites N-acetyl aspartate (NAA), choline, and creatine were determined in "institutional units" (IU) corrected for T1 and T2 relaxation effects. Structural imaging enabled determination of grey matter (GM), white matter (WM), and cerebrospinal fluid content. ANOVA analysis was carried out for voxels satisfying quality criteria. Results: Creatine levels were greater in GM than WM (57 vs. 44 IU, p < 0.001), whereas choline and NAA levels were greater in WM than GM [13 vs. 10 IU (p < 0.001) and 76 versus 70 IU (p = 0.03), respectively]. The ratio of NAA/cre was greater in WM than GM (2.1 vs. 1.4, p = 0.001) as was that of cho/cre (0.32 vs. 0.16, p < 0.001). A low voxel yield was due to brain atrophy and the difficulties of shimming over an extended region of brain. Conclusion: This study addresses the current lack of information on brain metabolite levels in older adults. The normal features of ageing result in a substantial loss of reliable voxels and should be taken into account when planning studies. Improvements in shimming are also required before the methods can be applied more widely

Foot pain and foot health in an educated population of adults: results from the Glasgow Caledonian University Alumni Foot Health Survey

Abstract Background Foot pain is common amongst the general population and impacts negatively on physical function and quality of life. Associations between personal health characteristics, lifestyle/behaviour factors and foot pain have been studied; however, the role of wider determinants of health on foot pain have received relatively little attention. Objectives of this study are i) to describe foot pain and foot health characteristics in an educated population of adults; ii) to explore associations between moderate-to-severe foot pain and a variety of factors including gender, age, medical conditions/co-morbidity/multi-morbidity, key indicators of general health, foot pathologies, and social determinants of health; and iii) to evaluate associations between moderate-to-severe foot pain and foot function, foot health and health-related quality-of-life. Methods Between February and March 2018, Glasgow Caledonian University Alumni with a working email address were invited to participate in the cross-sectional electronic survey (anonymously) by email via the Glasgow Caledonian University Alumni Office. The survey was constructed using the REDCap secure web online survey application and sought information on presence/absence of moderate-to-severe foot pain, patient characteristics (age, body mass index, socioeconomic status, occupation class, comorbidities, and foot pathologies). Prevalence data were expressed as absolute frequencies and percentages. Multivariate logistic and linear regressions were undertaken to identify associations 1) between independent variables and moderate-to-severe foot pain, and 2) between moderate-to-severe foot pain and foot function, foot health and health-related quality of life. Results Of 50,228 invitations distributed, there were 7707 unique views and 593 valid completions (median age [inter-quartile range] 42 [31–52], 67.3% female) of the survey (7.7% response rate). The sample was comprised predominantly of white Scottish/British (89.4%) working age adults (95%), the majority of whom were overweight or obese (57.9%), and in either full-time or part-time employment (82.5%) as professionals (72.5%). Over two-thirds (68.5%) of the sample were classified in the highest 6 deciles (most affluent) of social deprivation. Moderate-to-severe foot pain affected 236/593 respondents (39.8%). High body mass index, presence of bunions, back pain, rheumatoid arthritis, hip pain and lower occupation class were included in the final multivariate model and all were significantly and independently associated with moderate-to-severe foot pain (p < 0.05), except for rheumatoid arthritis (p = 0.057). Moderate-to-severe foot pain was significantly and independently associated lower foot function, foot health and health-related quality of life scores following adjustment for age, gender and body mass index (p < 0.05). Conclusions Moderate-to-severe foot pain was highly prevalent in a university-educated population and was independently associated with female gender, high body mass index, bunions, back pain, hip pain and lower occupational class. Presence of moderate-to-severe foot pain was associated with worse scores for foot function, foot health and health-related quality-of-life. Education attainment does not appear to be protective against moderate-to-severe foot pain

Early endostatin treatment inhibits metastatic seeding of murine colorectal cancer cells in the liver and their adhesion to endothelial cells

Endostatin, a carboxy-terminal fragment of collagen XVIII, potently inhibits angiogenesis and tumour growth, presumably through induction of apoptosis in endothelial cells and/or inhibition of their migration. Here we have tested how the timing of recombinant human endostatin (rh-E) administration affects its antitumour activity in a liver metastasis model of mouse C26 colorectal carcinoma cells. The effects of rh-E treatment on hepatic tumour load and on early tumour cell seeding were evaluated. Recombinant human endostatin was most effective in reducing intrahepatic tumour growth when administered prior to tumour cell inoculation. Analysis of early tumour cell seeding by using [125I]iododeoxyuridine-labelled C26 cells or by in vivo microscopy showed that rh-E reduced tumour cell seeding in the liver sinusoids. Recombinant human endostatin did not inhibit tumour growth when administered later than 4 days after tumour injection. Pretreatment of human umbilical vein endothelial cells with rh-E in vitro reduced C26 tumour cell adhesion under flow conditions two-fold as assessed by video microscopy and multiphoton laser scanning microscopy. Our results show that rh-E, in addition to antiangiogenic effects, reduces tumour cell adhesion in the liver sinusoids during the very early phases of metastasis formation. These data point towards a previously unknown mode of action of endostatin, that is, its ability to interfere with tumour cell seeding. Such insights may be helpful in the design of trials to improve (surgical) treatment of colorectal carcinoma and liver metastases