Physical Activity Improves Borderline Ankle-Brachial Index Values in a Cardiovascular Risk Population

Background: Peripheral, arterial disease (PAD) is an underdiagnosed and undertreated disease because it remains asymptomatic for so long. The ankle brachial index (ABI) is a valid method for detecting PAD: in lower extremities. ABI Methods: A total of 193 subjects with borderline ABI were examined in 2005-2006. None of them had previously diagnosed diabetes, cardiovascular or renal disease or intermittent claudication. They were given conventional treatment for multiple risk factors of cardiovascular diseases (hypertension, hypercholesterolemia, elevated blood glucose, smoking, and overweight). Sixty-four percent of these subjects (n = 1.23) attended a follow-up visit in 2012. Results: Of the 123 subjects with borderline ABI (mean age 59.0 years, 62% female) at baseline, 18 (15%, 95% confidence intervals {Cl] 9%-22%) developed incident PAD during the follow-up. The mean ABI was 0.97 +/- 0.03 at baseline and 1.01 +/- 0.12 at 7-year follow-up visit. The change in mean ABI was 70:04 (95% CI: 0.03-0.07), P <0.001. ABI improved significantly in 25 (20%) subjects. In multivariate ordered logistic regression analyses high and even moderate leisure-time physical activity :(LTPA; odds ratio 6.15; 95% CI: 1.99-19.1) predicted a rise in ABI in comparison to low LTPA. Conclusions: Physical activity seems to improve significantly ABI values among men and women with borderline ABI (0.91-1.00).Peer reviewe

Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network

One of the precepts of food security is the proper functioning of the global food markets. This calls for open and timely intelligence on crop production on an agroclimatically meaningful territorial scale. We propose an operationally suitable method for large-scale in-season crop yield estimations from a satellite image time series (SITS) for statistical production. As an object-based method, it is spatially scalable from parcel to regional scale, making it useful for prediction tasks in which the reference data are available only at a coarser level, such as counties. We show that deep learning-based temporal convolutional network (TCN) outperforms the classical machine learning method random forests and produces more accurate results overall than published national crop forecasts. Our novel contribution is to show that mean-aggregated regional predictions with histogram-based features calculated from farm-level observations perform better than other tested approaches. In addition, TCN is robust to the presence of cloudy pixels, suggesting TCN can learn cloud masking from the data. The temporal compositing of information do not improve prediction performance. This indicates that with end-to-end learning less preprocessing in SITS tasks seems viable

Sub-millimeter Observations of Giant Molecular Clouds in the Large Magellanic Cloud: Temperature and Density as Determined from J=3-2 and J=1-0 transitions of CO

We have carried out sub-mm 12CO(J=3-2) observations of 6 giant molecular clouds (GMCs) in the Large Magellanic Cloud (LMC) with the ASTE 10m sub-mm telescope at a spatial resolution of 5 pc and very high sensitivity. We have identified 32 molecular clumps in the GMCs and revealed significant details of the warm and dense molecular gas with n(H2) $\sim$ 10$^{3-5}$ cm$^{-3}$ and Tkin $\sim$ 60 K. These data are combined with 12CO(J=1-0) and 13CO(J=1-0) results and compared with LVG calculations. We found that the ratio of 12CO(J=3-2) to 12CO(J=1-0) emission is sensitive to and is well correlated with the local Halpha flux. We interpret that differences of clump propeties represent an evolutionary sequence of GMCs in terms of density increase leading to star formation.Type I and II GMCs (starless GMCs and GMCs with HII regions only, respectively) are at the young phase of star formation where density does not yet become high enough to show active star formation and Type III GMCs (GMCs with HII regions and young star clusters) represents the later phase where the average density is increased and the GMCs are forming massive stars. The high kinetic temperature correlated with \Halpha flux suggests that FUV heating is dominant in the molecular gas of the LMC.Comment: 74 pages, including 41 figures, accepted for publication in ApJ

Annexin A1 expression in a pooled breast cancer series : association with tumor subtypes and prognosis

Background: Annexin A1 (ANXA1) is a protein related with the carcinogenesis process and metastasis formation in many tumors. However, little is known about the prognostic value of ANXA1 in breast cancer. The purpose of this study is to evaluate the association between ANXA1 expression, BRCA1/2 germline carriership, specific tumor subtypes and survival in breast cancer patients. Methods: Clinical-pathological information and follow-up data were collected from nine breast cancer studies from the Breast Cancer Association Consortium (BCAC) (n = 5,752) and from one study of familial breast cancer patients with BRCA1/2 mutations (n = 107). ANXA1 expression was scored based on the percentage of immunohistochemical staining in tumor cells. Survival analyses were performed using a multivariable Cox model. Results: The frequency of ANXA1 positive tumors was higher in familial breast cancer patients with BRCA1/2 mutations than in BCAC patients, with 48.6 % versus 12.4 %, respectively; P <0.0001. ANXA1 was also highly expressed in BCAC tumors that were poorly differentiated, triple negative, EGFR-CK5/6 positive or had developed in patients at a young age. In the first 5 years of follow-up, patients with ANXA1 positive tumors had a worse breast cancer-specific survival (BCSS) than ANXA1 negative (HRadj = 1.35; 95 % CI = 1.05-1.73), but the association weakened after 10 years (HRadj = 1.13; 95 % CI = 0.91-1.40). ANXA1 was a significant independent predictor of survival in HER2+ patients (10-years BCSS: HRadj = 1.70; 95 % CI = 1.17-2.45). Conclusions: ANXA1 is overexpressed in familial breast cancer patients with BRCA1/2 mutations and correlated with poor prognosis features: triple negative and poorly differentiated tumors. ANXA1 might be a biomarker candidate for breast cancer survival prediction in high risk groups such as HER2+ cases.Peer reviewe

Potentially increased sawmill yield from hardwoods using X-ray computed tomography for knot detection

International audienceAbstractContextOne of the most important wood defects affecting the value yield from European beech (Fagus sylvatica [L.]) logs is knots that are visible on the sawn wood surface. The non-invasive technology of X-ray computed tomography (CT) can be used for the assessment of log internal features, especially the geometry and position of knots before primary breakdown to support the decision of value-optimised log rotation in sawmills.AimsThe objective of this study was to test whether value-optimised log rotation can be performed successfully by using the CT-derived knowledge of internal knottiness for the hardwood species beech.MethodsSize parameters of 670 knots were measured and their position was marked in CT images from 33 logs. The 3D-reconstructed logs were virtually sawn in 12 different rotational angles using the software InnoSIM. This allowed visual grading of the simulated sawn wood and the calculation of product volume and value.ResultsThe results show that if optimal rotation was applied to each single log, both total volume as well as total product value yield could be improved by up to 24 % compared with the average yield of all simulated rotational angles.ConclusionIn this small-scale study, it is demonstrated that CT technology could be used to support the decision about optimal rotational angle of beech logs to maximise volume and value yield

Genes associated with histopathologic features of triple negative breast tumors predict molecular subtypes

Distinct subtypes of triple negative (TN) breast cancer have been identified by tumor expression profiling. However, little is known about the relationship between histopathologic features of TN tumors, which reflect aspects of both tumor behavior and tumor microenvironment, and molecular TN subtypes. The histopathologic features of TN tumors were assessed by central review and 593 TN tumors were subjected to whole genome expression profiling using the Illumina Whole Genome DASL array. TN molecular subtypes were defined based on gene expression data associated with histopathologic features of TN tumors. Gene expression analysis yielded signatures for four TN subtypes (basal-like, androgen receptor positive, immune, and stromal) consistent with previous studies. Expression analysis also identified genes significantly associated with the 12 histological features of TN tumors. Development of signatures using these markers of histopathological features resulted in six distinct TN subtype signatures, including an additional basal-like and stromal signature. The additional basal-like subtype was distinguished by elevated expression of cell motility and glucose metabolism genes and reduced expression of immune signaling genes, whereas the additional stromal subtype was distinguished by elevated expression of immunomodulatory pathway genes. Histopathologic features that reflect heterogeneity in tumor architecture, cell structure, and tumor microenvironment are related to TN subtype. Accounting for histopathologic features in the development of gene expression signatures, six major subtypes of TN breast cancer were identified.Peer reviewe

Annexin A1 expression in a pooled breast cancer series: Association with tumor subtypes and prognosis

Background: Annexin A1 (ANXA1) is a protein related with the carcinogenesis process and metastasis formation in many tumors. However, little is known about the prognostic value of ANXA1 in breast cancer. The purpose of this study is to evaluate the association between ANXA1 expression, BRCA1/2 germline carriership, specific tumor subtypes and survival in breast cancer patients. Methods: Clinical-pathological information and follow-up data were collected from nine breast cancer studies from the Breast Cancer Association Consortium (BCAC) (n = 5,752) and from one study of familial breast cancer patients with BRCA1/2 mutations (n = 107). ANXA1 expression was scored based on the percentage of immunohistochemical staining in tumor cells. Survival analyses were performed using a multivariable Cox model. Results: The frequency of ANXA1 positive tumors was higher in familial breast cancer patients with BRCA1/2 mutations than in BCAC patients, with 48.6% versus 12.4 %, respectively;

Genome-wide association study identifies 25 known breast cancer susceptibility loci as risk factors for triple-negative breast cancer

In a genome-wide scan, we show that 30 variants in 25 genomic regions are associated with risk of TN breast cancer. Women carrying many of the risk variants may have 4-fold increased risk relative to women with few variants.Triple-negative (TN) breast cancer is an aggressive subtype of breast cancer associated with a unique set of epidemiologic and genetic risk factors. We conducted a two-stage genome-wide association study of TN breast cancer (stage 1: 1529 TN cases, 3399 controls; stage 2: 2148 cases, 1309 controls) to identify loci that influence TN breast cancer risk. Variants in the 19p13.1 and PTHLH loci showed genome-wide significant associations (P &lt; 5 x 10(-) (8)) in stage 1 and 2 combined. Results also suggested a substantial enrichment of significantly associated variants among the single nucleotide polymorphisms (SNPs) analyzed in stage 2. Variants from 25 of 74 known breast cancer susceptibility loci were also associated with risk of TN breast cancer (P &lt; 0.05). Associations with TN breast cancer were confirmed for 10 loci (LGR6, MDM4, CASP8, 2q35, 2p24.1, TERT-rs10069690, ESR1, TOX3, 19p13.1, RALY), and we identified associations with TN breast cancer for 15 additional breast cancer loci (P &lt; 0.05: PEX14, 2q24.1, 2q31.1, ADAM29, EBF1, TCF7L2, 11q13.1, 11q24.3, 12p13.1, PTHLH, NTN4, 12q24, BRCA2, RAD51L1-rs2588809, MKL1). Further, two SNPs independent of previously reported signals in ESR1 [rs12525163 odds ratio (OR) = 1.15, P = 4.9 x 10(-) (4)] and 19p13.1 (rs1864112 OR = 0.84, P = 1.8 x 10(-) (9)) were associated with TN breast cancer. A polygenic risk score (PRS) for TN breast cancer based on known breast cancer risk variants showed a 4-fold difference in risk between the highest and lowest PRS quintiles (OR = 4.03, 95% confidence interval 3.46-4.70, P = 4.8 x 10(-) (69)). This translates to an absolute risk for TN breast cancer ranging from 0.8% to 3.4%, suggesting that genetic variation may be used for TN breast cancer risk prediction