The New Zealand Kauri (Agathis Australis) Research Project: A Radiocarbon Dating Intercomparison of Younger Dryas Wood and Implications for IntCal13

We describe here the New Zealand kauri (Agathis australis) Younger Dryas (YD) research project, which aims to undertake Δ14C analysis of ~140 decadal floating wood samples spanning the time interval ~13.1–11.7 kyr cal BP. We report 14C intercomparison measurements being undertaken by the carbon dating laboratories at University of Waikato (Wk), University of California at Irvine (UCI), and University of Oxford (OxA). The Wk, UCI, and OxA laboratories show very good agreement with an interlaboratory comparison of 12 successive decadal kauri samples (average offsets from consensus values of –7 to +4 14C yr). A University of Waikato/University of Heidelberg (HD) intercomparison involving measurement of the YD-age Swiss larch tree Ollon505, shows a HD/Wk offset of ~10–20 14C yr (HD younger), and strong evidence that the positioning of the Ollon505 series is incorrect, with a recommendation that the 14C analyses be removed from the IntCal calibration database

IntCal09 and Marine09 radiocarbon age calibration curves, 0-50,000yeats cal BP

The IntCal04 and Marine04 radiocarbon calibration curves have been updated from 12 cal kBP (cal kBP is here defined as thousands of calibrated years before AD 1950), and extended to 50 cal kBP, utilizing newly available data sets that meet the IntCal Working Group criteria for pristine corals and other carbonates and for quantification of uncertainty in both the 14C and calendar timescales as established in 2002. No change was made to the curves from 0–12 cal kBP. The curves were constructed using a Markov chain Monte Carlo (MCMC) implementation of the random walk model used for IntCal04 and Marine04. The new curves were ratified at the 20th International Radiocarbon Conference in June 2009 and are available in the Supplemental Material at www.radiocarbon.org

An Empirical Study on Cross-data Transference of Adversarial Attacks on Object Detectors

Objektdetektorer blir stadig mer brukt i sikkerhetskritiske scenarier, inkludert autonome kjøretøy. Nyere studier har funnet at nevrale nettverk har en grunnleggende svakhet for fiendtlig støy . Å angripe objektdetektorer med fiendtlig støy, innebærer å legge til nøye valgt støy i inputen, som får objektdetektoren til å gjøre feil. For å sikre at disse sikkerhetskritiske systemene er pålitelige, må risikoen for slike angrep være kjent. Denne avhandlingen undersøker fiendtlig støy angrep der angriperen ikke har tilgang til objektdetektoren under angrep, eller dens treningssett. Å utarbeide et angrep i dette scenariet krever at angriperen trener sin egen modell på data som ligner målet sitt treningssett, så mye som mulig. Ved å bruke sin egen modell som surrogat, kan angriperen generere fiendtlig støy uten direkte tilgang til målet. Eksperimenter med denne typen angrep vil avgjøre om man effektivt kan angripe den private modellen ved hjelp av offentlige data. For å angripe Darknet modeller med Targeted Objectness Gradient (TOG) familien av angrep, ble rammeverket utviklet av Chow et al.[1] modifisert. Modifikasjonene gjorde at rammeverket generert bilder med fiendtlig støy som Darknet-modellene kunne gjøre deteksjoner på. Angrepsytelsen på fiendtlig støy overført mellom angreps- og målmodellen, er lav til å begynne med. Derimot, hvis en øker epsilon fra 8 til 24 under L_inf avstandsmetrikken, styrker overføringen og reduserer måldetektoren sin mAP til omtrent halvparten. Overføring studeres også når datasettene for den angripende modellen og målmodellen har overlapp. Angrepsytelse er funnet å være proporsjonal med størrelse på overlappen. Med sterkere overføring grunnet overlapp i datasettene, kan epsilon settes ned til 16 og beholde angrepsytelsen.Object detectors are increasingly deployed in safety-critical scenarios, including autonomous vehicles. Recent studies have found that neural networks are fundamentally weak to adversarial attacks. Adversarial attacks on object detectors involve adding a carefully chosen perturbation to the input, which causes the object detector to make mistakes. To make sure these safety-critical systems are trustworthy, the risks of adversarial attacks must be known. This thesis investigates adversarial attacks where the attacker does not have access to the target detector or its training set. Devising an attack in this scenario requires the attacker training their own model on data which resembles the target detector's training set as much as possible. Using their own model as a surrogate, lets the attacker generate adversarial attacks without accessing the target detector. Experiments with this type of attack will establish whether one can effectively attack the private model using public data. To attack Darknet models with the Targeted Objectness Gradient (TOG) family of attacks, the attack framework developed by Chow et al.[1] was modified. The modifications made the framework output adversarial samples, Darknet models could make predictions on. Though initial transference between the attacking and target model is low, increasing epsilon from 8 to 24 under the L_inf distance metric strengthens transference, and reduces the target detector mAP by about half. Transference is also studied when the datasets for the attacking and the target model intersect. Attack performance is found to be proportional with the intersection. With the stronger transference afforded by intersecting datasets, epsilon can be dropped to 16 and retain the attack performance

Segmentation of Coronary Arteries using Transformers

Koronar hjertesykdom (CAD) er en betydelig helseutfordring på verdensbasis. Tilstanden diagnostiseres tradisjonelt med invasive, kostbare metoder som Invasive Coronary Angiography (ICA) og invasive Fractional Flow Reserve (FFR)- målinger. Disse prosedyrene medfører imidlertid assosierte risikoer. Som et resultat har det vært en overgang mot å bruke den tryggere, mer kostnadseffektive Coronary Computed Tomography Angiography (CCTA), en ikke-invasiv avbildningsteknikk. De siste årene har det vært økende forskningsinteresse for å øke CCTA’s diagnostiske potensial gjennom automatisk segmentering av koronararteriene. Denne masteroppgaven fokuserte på å evaluere ytelsen til Shifted Window U- Net Transformer (SWIN UNETR), en transformer-basert arkitektur, sammenlignet med nåværende Convolutional Neural Network (CNN)-baserte metoder som no new U-Net (nnU-Net) for segmentering av koronararterier. Våre eksperimenter avdekket at SWIN UNETR-modellen overgikk tidligere resultater med en Dice Score (DSC) på 0.8614 mot 0.8296 på ImageCAS datasettet. Videre sikret SWIN UNETR 7. plass i ASOCA Challenge med en konkurransedyktig DSC på 0.8663. Når den ble sammenlignet med nnU-Net på St. Olavs Hospital datasettet, demonstrerte SWIN UNETR overlegen ytelse i form av DSC og med færre store artefakter i sine prediksjoner. Videre ble integrasjonen av automatisk segmentering av koronararterier med tid- ligere FFR estimeringsarbeid undersøkt. Selv om noen områder trengte manuelle korreksjoner, ble SWIN UNETR-modellen vellykket brukt som input til FFR-estimeringsmetoden og ga en sterk korrelasjon med fysisk målte FFR-verdier. Dens anvendelse i klassifisering av stenose som funksjonelt betydelig (FFR < 0.8), viste en lovende følsomhet på 85,7% sammenlignet med fysiske målinger. Dette resultatet overgikk følsomheten ved bruk av klinisk segmenterte arterier som input. Oppsummert ble det funnet at SWIN UNETR utmerket seg til segmentering av koronararterier fra CCTA-bilder sammenlignet med nåværende CNN-metoder. I tillegg ga kombinasjonen av automatisk segmentering og FFR-estimering lovende resultater når den ble kombinert med noen mindre manuelle korreksjoner. Både segmenteringen og kombinasjonen med FFR-estimering kan derfor være verdifulle verktøy for klinisk vurdering av CAD fra CCTA bilder.Coronary Artery Disease (CAD) is a significant health issue worldwide. The condition is traditionally diagnosed with invasive, costly methods such as Invasive Coronary Angiography (ICA) and invasive Fractional Flow Reserve (FFR) measurements. These procedures, however, carry associated risks. As a result, there’s been a shift towards using the safer, more cost-effective Coronary Computed Tomography Angiography (CCTA), a non-invasive imaging technique. Recent years have seen growing research interest in boosting CCTA’s diagnostic potential via auto-mated coronary artery segmentation. This thesis focused on evaluating the performance of Shifted Window U-Net Trans- former (SWIN UNETR), a transformer-based architecture, and contrasting it with current Convolutional Neural Network (CNN)-based methods like no new U-Net (nnU-Net) for coronary artery segmentation. Our experiments revealed that the SWIN UNETR model surpassed previous benchmarks with a Dice Score (DSC) of 0.8614 versus the earlier 0.8296 on the ImageCAS dataset. Moreover, it secured 7th place in the ASOCA Challenge with a competitive DSC of 0.8663. When compared to nnU-Net on the St. Olavs Hospital dataset, SWIN UNETR demonstrated superior performance in terms of DSC and with fewer large artifacts in its predictions. Furthermore, the integration of automatic coronary artery segmentation with prior FFR estimation work was examined. Although a few areas needed manual corrections, the SWIN UNETR model was successfully used as the input to the FFR estimation method and yielded a strong correlation with physically measured FFR values. Its application in classifying stenosis as functionally significant (FFR < 0.8), demonstrated a promising sensitivity of 85.7% compared to physical measurements. This result exceeded the sensitivity of using clinically segmented arteries as input. In summary, SWIN UNETR was found to excel at the task of coronary artery segmentation from CCTA images compared to current CNN methods. Additionally, the combination of automatic segmentation and FFR estimation gave promising results when combined with some minor manual corrections. Both the segmentation and combination with FFR estimation could therefore be valuable tools for clinical assessment of CAD from CCTA

Hypoxia induces dilated cardiomyopathy in the chick embryo: mechanism, intervention, and long-term consequences

Background: Intrauterine growth restriction is associated with an increased future risk for developing cardiovascular diseases. Hypoxia in utero is a common clinical cause of fetal growth restriction. We have previously shown that chronic hypoxia alters cardiovascular development in chick embryos. The aim of this study was to further characterize cardiac disease in hypoxic chick embryos. Methods: Chick embryos were exposed to hypoxia and cardiac structure was examined by histological methods one day prior to hatching (E20) and at adulthood. Cardiac function was assessed in vivo by echocardiography and ex vivo by contractility measurements in isolated heart muscle bundles and isolated cardiomyocytes. Chick embryos were exposed to vascular endothelial growth factor (VEGF) and its scavenger soluble VEGF receptor-1 (sFlt-1) to investigate the potential role of this hypoxia-regulated cytokine. Principal Findings: Growth restricted hypoxic chick embryos showed cardiomyopathy as evidenced by left ventricular (LV) dilatation, reduced ventricular wall mass and increased apoptosis. Hypoxic hearts displayed pump dysfunction with decreased LV ejection fractions, accompanied by signs of diastolic dysfunction. Cardiomyopathy caused by hypoxia persisted into adulthood. Hypoxic embryonic hearts showed increases in VEGF expression. Systemic administration of rhVEGF165 to normoxic chick embryos resulted in LV dilatation and a dose-dependent loss of LV wall mass. Lowering VEGF levels in hypoxic embryonic chick hearts by systemic administration of sFlt-1 yielded an almost complete normalization of the phenotype. Conclusions/Significance: Our data show that hypoxia causes a decreased cardiac performance and cardiomyopathy in chick embryos, involving a significant VEGF-mediated component. This cardiomyopathy persists into adulthood

Who Cares?: The Official Newsletter of the Student Bar Association

https://scholarship.shu.edu/law_newspapers/1017/thumbnail.jp

Hypoxia induces dilated cardiomyopathy in the chick embryo: mechanism; intervention; and long-term consequences.

Background Intrauterine growth restriction is associated with an increased future risk for developing cardiovascular diseases. Hypoxia in utero is a common clinical cause of fetal growth restriction. We have previously shown that chronic hypoxia alters cardiovascular development in chick embryos. The aim of this study was to further characterize cardiac disease in hypoxic chick embryos. Methods Chick embryos were exposed to hypoxia and cardiac structure was examined by histological methods one day prior to hatching (E20) and at adulthood. Cardiac function was assessed in vivo by echocardiography and ex vivo by contractility measurements in isolated heart muscle bundles and isolated cardiomyocytes. Chick embryos were exposed to vascular endothelial growth factor (VEGF) and its scavenger soluble VEGF receptor-1 (sFlt-1) to investigate the potential role of this hypoxia-regulated cytokine. Principal Findings Growth restricted hypoxic chick embryos showed cardiomyopathy as evidenced by left ventricular (LV) dilatation, reduced ventricular wall mass and increased apoptosis. Hypoxic hearts displayed pump dysfunction with decreased LV ejection fractions, accompanied by signs of diastolic dysfunction. Cardiomyopathy caused by hypoxia persisted into adulthood. Hypoxic embryonic hearts showed increases in VEGF expression. Systemic administration of rhVEGF165 to normoxic chick embryos resulted in LV dilatation and a dose-dependent loss of LV wall mass. Lowering VEGF levels in hypoxic embryonic chick hearts by systemic administration of sFlt-1 yielded an almost complete normalization of the phenotype. Conclusions/Significance Our data show that hypoxia causes a decreased cardiac performance and cardiomyopathy in chick embryos, involving a significant VEGF-mediated component. This cardiomyopathy persists into adulthood