Machine Learning Classification of Females Susceptibility to Visceral Fat Associated Diseases

The problem of classifying subjects into risk categories is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of these algorithms is to predict dichotomous responses (e.g. healthy/at risk) based on several features. Similarly to statistical inference models, also ML models are subject to the common problem of class imbalance. Therefore, they are affected by the majority class increasing the false-negative rate. In this paper, we built and evaluated eighteen ML models classifying approximately 4300 female participants from the UK Biobank into three categorical risk statuses based on responses for the discretised visceral adipose tissue values from magnetic resonance imaging. We also examined the effect of sampling techniques on classification modelling when dealing with class imbalance. Results showed that the use of sampling techniques had a significant impact. They not only drove an improvement in predicting patients risk status but also facilitated an increase in the information contained within each variable. Based on domain experts criteria, the three best models for classification were finally identified. These encouraging results will guide further developments of classification models for predicting visceral adipose tissue without the need for a costly scan

FAK acts as a suppressor of RTK-MAP kinase signalling in Drosophila melanogaster epithelia and human cancer cells

Receptor Tyrosine Kinases (RTKs) and Focal Adhesion Kinase (FAK) regulate multiple signalling pathways, including mitogen-activated protein (MAP) kinase pathway. FAK interacts with several RTKs but little is known about how FAK regulates their downstream signalling. Here we investigated how FAK regulates signalling resulting from the overexpression of the RTKs RET and EGFR. FAK suppressed RTKs signalling in Drosophila melanogaster epithelia by impairing MAPK pathway. This regulation was also observed in MDA-MB-231 human breast cancer cells, suggesting it is a conserved phenomenon in humans. Mechanistically, FAK reduced receptor recycling into the plasma membrane, which resulted in lower MAPK activation. Conversely, increasing the membrane pool of the receptor increased MAPK pathway signalling. FAK is widely considered as a therapeutic target in cancer biology; however, it also has tumour suppressor properties in some contexts. Therefore, the FAK-mediated negative regulation of RTK/MAPK signalling described here may have potential implications in the designing of therapy strategies for RTK-driven tumours

Lead concentrations in blood from incubating common eiders (Somateria mollissima) in the Baltic Sea

Here we investigate if lead may be a contributing factor to the observed population decline in a Baltic colony of incubating eiders (Somateria mollissima). Body mass and blood samples were obtained from 50 incubating female eiders at the Baltic breeding colony on Christianso during spring 2017 (n = 27) and 2018 (n = 23). All the females were sampled twice during early (day 4) and late (day 24) incubation. The full blood was analysed for lead to investigate if the concentrations exceeded toxic thresholds or changed over the incubation period due to remobilisation from bones and liver tissue. Body mass, hatch date and number of chicks were also analysed with respect to lead concentrations. The body mass (mean +/- SD g) increased significantly in the order: day 24 in 2018 (1561 +/- 154 g) < day 24 in 2017 (1618 +/- 156 g) < day 4 in 2018 (2183 +/- 140 g) < day 4 in 2017 (2359 +/- 167 g) (all p < 0.001). The lead concentrations increased significantly in the opposite order i.e. day 4 in 2017 (41.7 +/- 67.1 mu g/L) < day 24 in 2017 (55.4 +/- 66.8 mu g/L) < day 4 in 2018 (177 +/- 196 mu g/L) < day 24 in 2018 (258 +/- 243) (all p < 0.001). From day 4 to 24, the eider females had a 1.33-fold increase in blood lead concentrations in 2017 and a 1.46-fold increase in 2018. Three of the birds (13%) sampled in 2018 had lead concentrations that exceeded concentrations of clinical poisoning (500 mu g/L) and eleven (48%) had concentrations that exceeded the threshold for subclinical poisoning (200 mu g/L). In 2017, none of the birds exceeded the high toxic threshold of clinical poisoning while only one (4%) exceeded the lower threshold for subclinical poisoning. Three of the birds (6%) sampled in 2018 had lead concentrations that exceeded those of clinical poisoning while 12 birds (24%) resampled in both years exceeded the threshold for subclinical poisoning. In addition, lead concentrations and body mass on day 4 affected hatch date positively in 2018 (both p < 0.03) but not in 2017. These results show that bioavailable lead in bone and liver tissue pose a threat to the health of about 25% of the incubating eiders sampled. This is particularly critical because eiders are largely capital breeding which means that incubating eiders are in an energetically stressed state. The origin of lead in incubating eiders in the Christianso colony is unknown and it remains an urgent priority to establish the source, prevalence and mechanism for uptake. The increase in lead from day 4 to day 24 is due to bone and liver remobilization; however, the additional lead source(s) on the breeding grounds needs to be identified. Continued investigations should determine the origin, uptake mechanisms and degree of exposure to lead for individual birds. Such research should include necropsies, x-ray, lead isotope and stable C and N isotope analyses to find the lead sources(s) in the course of the annual cycle and how it may affect the population dynamics of the Christianso colony which reflects the ecology of the Baltic eiders being suitable for biomonitoring the overall flyway

Quality of life and salivary output in patients with head-and-neck cancer five years after radiotherapy

BACKGROUND: To describe long-term changes in time of quality of life (QOL) and the relation with parotid salivary output in patients with head-and-neck cancer treated with radiotherapy. METHODS: Forty-four patients completed the EORTC-QLQ-C30(+3) and the EORTC-QLQ-H&N35 questionnaires before treatment, 6 weeks, 6 months, 12 months, and at least 3.5 years after treatment. At the same time points, stimulated bilateral parotid flow rates were measured. RESULTS: There was a deterioration of most QOL items after radiotherapy compared with baseline, with gradual improvement during 5 years follow-up. The specific xerostomia-related items showed improvement in time, but did not return to baseline. Global QOL did not alter significantly in time, although 41% of patients complained of moderate or severe xerostomia at 5 years follow-up. Five years after radiotherapy the mean cumulated parotid flow ratio returned to baseline but 20% of patients had a flow ratio <25%. The change in time of xerostomia was significantly related with the change in flow ratio (p = 0.01). CONCLUSION: Most of the xerostomia-related QOL scores improved in time after radiotherapy without altering the global QOL, which remained high. The recovery of the dry mouth feeling was significantly correlated with the recovery in parotid flow ratio

Angular and Current-Target Correlations in Deep Inelastic Scattering at HERA

Correlations between charged particles in deep inelastic ep scattering have been studied in the Breit frame with the ZEUS detector at HERA using an integrated luminosity of 6.4 pb-1. Short-range correlations are analysed in terms of the angular separation between current-region particles within a cone centred around the virtual photon axis. Long-range correlations between the current and target regions have also been measured. The data support predictions for the scaling behaviour of the angular correlations at high Q2 and for anti-correlations between the current and target regions over a large range in Q2 and in the Bjorken scaling variable x. Analytic QCD calculations and Monte Carlo models correctly describe the trends of the data at high Q2, but show quantitative discrepancies. The data show differences between the correlations in deep inelastic scattering and e+e- annihilation.Comment: 26 pages including 10 figures (submitted to Eur. J. Phys. C

Regulatory capacity building and the governance of clinical stem cell research in China

While other works have explained difficulties in applying ‘international’ guidelines in the field of regenerative medicine in so-called low- and middle-income countries (LMICs) in terms of ‘international hegemony’, ‘political and ethical governance’ and ‘cosmopolitisation’, this article on stem cell regulation in China emphasises the particular complexities faced by large LMICs: the emergence of alternative regulatory arrangements made by stakeholders at a provincial level at home. On the basis of ethnographic and archival research of clinical stem cell research hubs, we have characterized six types of entrepreneurial ‘bionetworks’, each of which embodies a regulatory orientation that developed in interaction with China’s regulatory dilemmas. Rather than adopting guidelines from other countries, we argue that regulatory capacity building is more appropriately viewed as a relational concept, referring to the ability to develop regulatory requirements that can cater for different regulatory research needs on an international level and at home

The CogBIAS longitudinal study protocol: cognitive and genetic factors influencing psychological functioning in adolescence.

BACKGROUND: Optimal psychological development is dependent upon a complex interplay between individual and situational factors. Investigating the development of these factors in adolescence will help to improve understanding of emotional vulnerability and resilience. The CogBIAS longitudinal study (CogBIAS-L-S) aims to combine cognitive and genetic approaches to investigate risk and protective factors associated with the development of mood and impulsivity-related outcomes in an adolescent sample. METHODS: CogBIAS-L-S is a three-wave longitudinal study of typically developing adolescents conducted over 4 years, with data collection at age 12, 14 and 16. At each wave participants will undergo multiple assessments including a range of selective cognitive processing tasks (e.g. attention bias, interpretation bias, memory bias) and psychological self-report measures (e.g. anxiety, depression, resilience). Saliva samples will also be collected at the baseline assessment for genetic analyses. Multilevel statistical analyses will be performed to investigate the developmental trajectory of cognitive biases on psychological functioning, as well as the influence of genetic moderation on these relationships. DISCUSSION: CogBIAS-L-S represents the first longitudinal study to assess multiple cognitive biases across adolescent development and the largest study of its kind to collect genetic data. It therefore provides a unique opportunity to understand how genes and the environment influence the development and maintenance of cognitive biases and provide insight into risk and protective factors that may be key targets for intervention.This work was supported by the European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement no: [324176]

Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning

Many protein engineering problems involve finding mutations that produce proteins with a particular function. Computational active learning is an attractive approach to discover desired biological activities. Traditional active learning techniques have been optimized to iteratively improve classifier accuracy, not to quickly discover biologically significant results. We report here a novel active learning technique, Most Informative Positive (MIP), which is tailored to biological problems because it seeks novel and informative positive results. MIP active learning differs from traditional active learning methods in two ways: (1) it preferentially seeks Positive (functionally active) examples; and (2) it may be effectively extended to select gene regions suitable for high throughput combinatorial mutagenesis. We applied MIP to discover mutations in the tumor suppressor protein p53 that reactivate mutated p53 found in human cancers. This is an important biomedical goal because p53 mutants have been implicated in half of all human cancers, and restoring active p53 in tumors leads to tumor regression. MIP found Positive (cancer rescue) p53 mutants in silico using 33% fewer experiments than traditional non-MIP active learning, with only a minor decrease in classifier accuracy. Applying MIP to in vivo experimentation yielded immediate Positive results. Ten different p53 mutations found in human cancers were paired in silico with all possible single amino acid rescue mutations, from which MIP was used to select a Positive Region predicted to be enriched for p53 cancer rescue mutants. In vivo assays showed that the predicted Positive Region: (1) had significantly more (p<0.01) new strong cancer rescue mutants than control regions (Negative, and non-MIP active learning); (2) had slightly more new strong cancer rescue mutants than an Expert region selected for purely biological considerations; and (3) rescued for the first time the previously unrescuable p53 cancer mutant P152L