Handcrafted and learning-based tie point features-comparison using the EuroSDR RPAS benchmark datasets

The identification of accurate and reliable image correspondences is fundamental for Structure-from-Motion (SfM) photogrammetry. Alongside handcrafted detectors and descriptors, recent machine learning-based approaches have shown promising results for tie point extraction, demonstrating matching success under strong perspective and illumination changes, and a general increase of tie point multiplicity. Recently, several methods based on convolutional neural networks (CNN) have been proposed, but few tests have yet been performed under real photogrammetric applications and, in particular, on full resolution aerial and RPAS image blocks that require rotationally invariant features. The research reported here compares two handcrafted (Metashape local features and RootSIFT) and two learning-based methods (LFNet and Key.Net) using the previously unused EuroSDR RPAS benchmark datasets. Analysis is conducted with DJI Zenmuse P1 imagery acquired at Wards Hill quarry in Northumberland, UK. The research firstly extracts keypoints using the aforementioned methods, before importing them into COLMAP for incremental reconstruction. The image coordinates of signalised ground control points (GCPs) and independent checkpoints (CPs) are automatically detected using an OpenCV algorithm, and then triangulated for comparison with accurate geometric ground-truth. The tests showed that learning-based local features are capable of outperforming traditional methods in terms of geometric accuracy, but several issues remain: few deep learning local features are trained to be rotation invariant, significant computational resources are required for large format imagery, and poor performance emerged in cases of repetitive patterns

CXCR6 marks a novel subset of T-bet(lo)Eomes(hi) natural killer cells residing in human liver

This work was funded by a Wellcome Trust Investigator Award to MKM funding KS and LP, MRC Career Development Award to CD, MRC Clinical Research Training Fellowship to DP, Wellcome Trust Henry Dale Fellowship to VM

Characterization of humoral and SARS-CoV-2 specific T cell responses in people living with HIV

There is an urgent need to understand the nature of immune responses against SARS-CoV-2, to inform risk-mitigation strategies for people living with HIV (PLWH). Here we show that the majority of PLWH with ART suppressed HIV viral load, mount a detectable adaptive immune response to SARS-CoV-2. Humoral and SARS-CoV-2-specific T cell responses are comparable between HIV-positive and negative subjects and persist 5-7 months following predominately mild COVID-19 disease. T cell responses against Spike, Membrane and Nucleoprotein are the most prominent, with SARS-CoV-2-specific CD4 T cells outnumbering CD8 T cells. We further show that the overall magnitude of SARS-CoV-2-specific T cell responses relates to the size of the naive CD4 T cell pool and the CD4:CD8 ratio in PLWH. These findings suggest that inadequate immune reconstitution on ART, could hinder immune responses to SARS-CoV-2 with implications for the individual management and vaccine effectiveness in PLWH

Patterns in metabolite profile are associated with risk of more aggressive prostate cancer: A prospective study of 3,057 matched case-control sets from EPIC

Metabolomics may reveal novel insights into the etiology of prostate cancer, for which few risk factors are established. We investigated the association between patterns in baseline plasma metabolite profile and subsequent prostate cancer risk, using data from 3,057 matched case-control sets from the European Prospective Investigation into Cancer and Nutrition (EPIC). We measured 119 metabolite concentrations in plasma samples, collected on average 9.4 years before diagnosis, by mass spectrometry (AbsoluteIDQ p180 Kit, Biocrates Life Sciences AG). Metabolite patterns were identified using treelet transform, a statistical method for identification of groups of correlated metabolites. Associations of metabolite patterns with prostate cancer risk (OR1SD ) were estimated by conditional logistic regression. Supplementary analyses were conducted for metabolite patterns derived using principal component analysis and for individual metabolites. Men with metabolite profiles characterized by higher concentrations of either phosphatidylcholines or hydroxysphingomyelins (OR1SD  = 0.77, 95% confidence interval 0.66-0.89), acylcarnitines C18:1 and C18:2, glutamate, ornithine and taurine (OR1SD  = 0.72, 0.57-0.90), or lysophosphatidylcholines (OR1SD  = 0.81, 0.69-0.95) had lower risk of advanced stage prostate cancer at diagnosis, with no evidence of heterogeneity by follow-up time. Similar associations were observed for the two former patterns with aggressive disease risk (the more aggressive subset of advanced stage), while the latter pattern was inversely related to risk of prostate cancer death (OR1SD  = 0.77, 0.61-0.96). No associations were observed for prostate cancer overall or less aggressive tumor subtypes. In conclusion, metabolite patterns may be related to lower risk of more aggressive prostate tumors and prostate cancer death, and might be relevant to etiology of advanced stage prostate cancer

Intake of individual fatty acids and risk of prostate cancer in the European prospective investigation into cancer and nutrition

The associations of individual dietary fatty acids with prostate cancer risk have not been examined comprehensively. We examined the prospective association of individual dietary fatty acids with prostate cancer risk overall, by tumor subtypes, and prostate cancer death. 142,239 men from the European Prospective Investigation into Cancer and Nutrition who were free from cancer at recruitment were included. Dietary intakes of individual fatty acids were estimated using center-specific validated dietary questionnaires at baseline and calibrated with 24-hour recalls. Multivariable Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). After an average follow-up of 13.9 years, 7,036 prostate cancer cases and 936 prostate cancer deaths were ascertained. Intakes of individual fatty acids were not related to overall prostate cancer risk. There was evidence of heterogeneity in the association of some short chain saturated fatty acids with prostate cancer risk by tumor stage (Pheterogeneity <0.015), with a positive association with risk of advanced stage disease for butyric acid (4:0; HR1SD =1.08; 95%CI=1.01-1.15; P-trend=0.026). There were no associations with fatal prostate cancer, with the exception of a slightly higher risk for those who consumed more eicosenoic acid (22:1n-9c; HR1SD =1.05; 1.00-1.11; P-trend=0.048) and eicosapentaenoic acid (20:5n-3c; HR1SD =1.07; 1.00-1.14; P-trend=0.045). There was no evidence that dietary intakes of individual fatty acids were associated with overall prostate cancer risk. However, a higher intake of butyric acid might be associated with a higher risk of advanced, whereas intakes of eicosenoic and eicosapentaenoic acids might be positively associated with fatal prostate cancer risk. This article is protected by copyright. All rights reserved

Predicting circulating CA125 levels among healthy premenopausal women

Background: Cancer antigen 125 (CA125) is the most promising ovarian cancer screening biomarker to date. Multiple studies reported CA125 levels vary by personal characteristics, which could inform personalized CA125 thresholds. However, this has not been well described in premenopausal women. Methods: We evaluated predictors of CA125 levels among 815 premenopausal women from the New England Case Control Study (NEC). We developed linear and dichotomous (≥35 U/mL) CA125 prediction models and externally validated an abridged model restricting to available predictors among 473 premenopausal women in the European Prospective Investigation into Cancer and Nutrition Study (EPIC). Results: The final linear CA125 prediction model included age, race, tubal ligation, endometriosis, menstrual phase at blood draw, and fibroids, which explained 7% of the total variance of CA125. The correlation between observed and predicted CA125 levels based on the abridged model (including age, race, and menstrual phase at blood draw) had similar correlation coefficients in NEC (r = 0.22) and in EPIC (r = 0.22). The dichotomous CA125 prediction model included age, tubal ligation, endometriosis, prior personal cancer diagnosis, family history of ovarian cancer, number of miscarriages, menstrual phase at blood draw, and smoking status with AUC of 0.83. The abridged dichotomous model (including age, number of miscarriages, menstrual phase at blood draw, and smoking status) showed similar AUCs in NEC (0.73) and in EPIC (0.78). Conclusions: We identified a combination of factors associated with CA125 levels in premenopausal women. Impact: Our model could be valuable in identifying healthy women likely to have elevated CA125 and consequently improve its specificity for ovarian cancer screening

Patterns in metabolite profile are associated with risk of more aggressive prostate cancer: a prospective study of 3057 matched case‐control sets from EPIC

Metabolomics may reveal novel insights into the etiology of prostate cancer, for which few risk factors are established. We investigated the association between patterns in baseline plasma metabolite profile and subsequent prostate cancer risk, using data from 3,057 matched case-control sets from the European Prospective Investigation into Cancer and Nutrition (EPIC). We measured 119 metabolite concentrations in plasma samples, collected on average 9.4 years before diagnosis, by mass spectrometry (AbsoluteIDQ p180 Kit, Biocrates Life Sciences AG). Metabolite patterns were identified using treelet transform, a statistical method for identification of groups of correlated metabolites. Associations of metabolite patterns with prostate cancer risk (OR1SD ) were estimated by conditional logistic regression. Supplementary analyses were conducted for metabolite patterns derived using principal component analysis and for individual metabolites. Men with metabolite profiles characterized by higher concentrations of either phosphatidylcholines or hydroxysphingomyelins (OR1SD = 0.77, 95% confidence interval 0.66-0.89), acylcarnitines C18:1 and C18:2, glutamate, ornithine and taurine (OR1SD = 0.72, 0.57-0.90), or lysophosphatidylcholines (OR1SD = 0.81, 0.69-0.95) had lower risk of advanced stage prostate cancer at diagnosis, with no evidence of heterogeneity by follow-up time. Similar associations were observed for the two former patterns with aggressive disease risk (the more aggressive subset of advanced stage), while the latter pattern was inversely related to risk of prostate cancer death (OR1SD = 0.77, 0.61-0.96). No associations were observed for prostate cancer overall or less aggressive tumor subtypes. In conclusion, metabolite patterns may be related to lower risk of more aggressive prostate tumors and prostate cancer death, and might be relevant to etiology of advanced stage prostate cancer