Indo-Pacific Powers: Internalization, Interpretation, and Implementation of International Law

This article examines how the Indo-Pacific powers, China and India, respond to international law and evaluates how effectively international law influences each state’s behavior. The role of norms and international legal regimes in the major Indo-Pacific flashpoints has become an inseparable justification of contestants’ claims over the years. We suggest that a state actor’s response to international law can be assessed using three criteria: the internalization, interpretation, and implementation of international law. The article investigates China and India as state actors and the United Nations Convention on the Law of the Sea as a case of international law. We assess these criteria by comparing the development of domestic laws by China and India in accordance with the United Nations Convention on the Law of the Sea (internalization), their declarations submitted to the United Nations Convention on the Law of the Sea provisions (interpretation), and their reaction to third-party arbitrations (implementation). By connecting the domestic and international legal actions of rising powers in the Indo-Pacific region, the article suggests that a state actor’s internalization, interpretation, and implementation of international law significantly indicate how international law impacts an individual state’s behavior in the international security arena. Thus, this article establishes critical connections between emerging security order, regional politics, and normative developments in the Indo-Pacific

Uncertainty and equifinality in environmental modelling of organic pollutants with specific focus on cyclic volatile methyl siloxanes

Multi-media fate and transport models (MFTMs) are invaluable tools in understanding and predicting the likely behaviour of organic pollutants in the environment. However, some parameters describing the properties of both the environmental system and the chemical pollutant under consideration are uncertain and or variable in space and time. Furthermore, model performance is often evaluated using sparse data sets on chemical concentrations in different media. This can result in equifinality-the phenomenon in which several different combinations of model parameters can result in similar predictions of environmental concentrations. We explore this idea for MFTMs for the first time using, as examples, three cyclic volatile methyl siloxanes (cVMS: D4, D5 and D6) and the QWASI lake model applied to Tokyo Bay. Monte Carlo simulation was employed with parameters selected from probability distributions representing estimated uncertainty in a large number of iterations. This generated distributions of predicted chemical concentrations in water (CW) and sediment (CS) which represent the aleatory uncertainty envelope but which also demonstrate significant equifinality. For all three compounds, the uncertainty implied in the CW was lower (coefficient of variation, CV, of the order of 20%) than for CS (CV ca. 45%), reflecting the propensity of cVMS compounds to sorb to sediment and the sensitivity of the model to KOC. Confidence intervals were particularly high for the persistence of D5 and D6 in sediment which both ranged between approximately 1.7 years and approximately 26 years for Tokyo Bay. Predicted concentration distributions matched observations well for D5 and D6 not for D4. Equifinality could be reduced by better constraining acceptable parameter sets using additional measured data from different environmental compartments

Decision fusion-based fetal ultrasound image plane classification using convolutional neural networks

Machine learning for ultrasound image analysis and interpretation can be helpful in automated image classification in large-scale retrospective analyses to objectively derive new indicators of abnormal fetal development that are embedded in ultrasound images. Current approaches to automatic classification are limited to the use of either image patches (cropped images) or the global (whole) image. As many fetal organs have similar visual features, cropped images can misclassify certain structures such as the kidneys and abdomen. Also, the whole image does not encode sufficient local information about structures to identify different structures in different locations. Here we propose a method to automatically classify 14 different fetal structures in 2-D fetal ultrasound images by fusing information from both cropped regions of fetal structures and the whole image. Our method trains two feature extractors by fine-tuning pre-trained convolutional neural networks with the whole ultrasound fetal images and the discriminant regions of the fetal structures found in the whole image. The novelty of our method is in integrating the classification decisions made from the global and local features without relying on priors. In addition, our method can use the classification outcome to localize the fetal structures in the image. Our experiments on a data set of 4074 2-D ultrasound images (training: 3109, test: 965) achieved a mean accuracy of 97.05%, mean precision of 76.47% and mean recall of 75.41%. The Cohen κ of 0.72 revealed the highest agreement between the ground truth and the proposed method. The superiority of the proposed method over the other non-fusion-based methods is statistically significant (p < 0.05). We found that our method is capable of predicting images without ultrasound scanner overlays with a mean accuracy of 92%. The proposed method can be leveraged to retrospectively classify any ultrasound images in clinical research

An automated framework for large scale retrospective analysis of ultrasound images

Objective: Large scale retrospective analysis of fetal ultrasound (US) data is important in the understanding of the cumulative impact of antenatal factors on offspring's health outcomes. Although the benefits are evident, there is a paucity of research into such large scale studies as it requires tedious and expensive effort in manual processing of large scale data repositories. This study presents an automated framework to facilitate retrospective analysis of large scale US data repositories. Method: Our framework consists of four modules: (1) an image classifier to distinguish the Brightness (B)-mode images; (2) a fetal image structure identifier to select US images containing user-defined fetal structures of interest (fSOI); (3) a biometry measurement algorithm to measure the fSOIs in the images and, (4) a visual evaluation module to allow clinicians to validate the outcomes. Results: We demonstrated our framework using thalamus as the fSOI from a hospital repository of more than 80,000 patients, consisting of 3,816,967 antenatal US files (DICOM objects). Our framework classified 1,869,105 B-mode images and from which 38,786 thalamus images were identified. We selected a random subset of 1290 US files with 558 B-mode (containing 19 thalamus images and the rest being other US data) and evaluated our framework performance. With the evaluation set, B-mode image classification resulted in accuracy, precision, and recall (APR) of 98.67%, 99.75% and 98.57% respectively. For fSOI identification, APR was 93.12%, 97.76% and 80.78% respectively. Conclusion: We introduced a completely automated approach designed to analyze a large scale data repository to enable retrospective clinical research. © 2013 IEEE

Plane identification in fetal ultrasound images using saliency maps and convolutional neural networks

Fetal development is noninvasively assessed by measuring the size of different structures in ultrasound (US) images. The reliability of these measurements is dependent upon the identification of the correct anatomical viewing plane, each of which contains different fetal structures. However, the automatic classification of the anatomical planes in fetal US images is challenging due to a number of factors, such as low signal-to-noise-ratios and the small size of the fetus. Current approaches for plane classification are limited to simpler subsets of the problem: only classifying planes within specific body regions or using temporal information from videos. In this paper, we propose a new general method for the classification of anatomical planes in fetal US images. Our method trains two convolutional neural networks to learn the best US and saliency features. The fusion of these features overcomes the challenges associated with US fetal imaging by emphasising the salient features within US images that best discriminate different planes. Our method achieved higher classification accuracy than a state-of-the-art baseline for 12 of the 13 different planes found in a clinical dataset of fetal US images. © 2016 IEEE

Effect of boron purity on superconducting properties of SiC doped MgB2

The effect of purity of amorphous boron raw materials on properties of the hot pressed SiC doped MgB2 superconductor was investigated. MgB2 superconductors with magnetic Jc over 106A/cm2 and remaining Jc of 105 A/cm2 at 4.2 K and 5 T were fabricated by hot pressing using both high purity (99.00%) and low purity (88.84%) boron powders. XRD analysis shows that purity of the boron powders has little effect on phase component of the MgB2 samples. If the main impurity in amorphous boron is Mg, low purity low cost boron powder is suitable as one of the raw materials for fabricating MgB2. Particle sizes of boron has significant effect on microstructure and properties ofMgB2. Smaller boron particle size leads to smaller grain size of MgB2, higher density, higher lattice distortion, and thus higher magnetic Jc

Organic radical-induced Cu²⁺ selective sensing based on thiazolothiazole derivatives

As a new approach to detect Cu2+, colorimetric detection of Cu2+ via organo radical formation of thiazolothiazole is reported. Upon the addition of Cu2+, thiazolothiazole derivatives 1 and 2 form relatively stable organo radicals, resulting in a distinct colorimetric change from greenish yellow to blue. Among the various metal ions, only Cu2+ showed a new peak appearance at 610 nm. In addition, a selective fluorescence quenching was also observed with Cu2+. To understand the origin of the Cu2+ selectivity of 1 and 2, series of electrochemical data are reported. Finally, EPR (electron paramagnetic resonance) data clearly support the formation of this unique organic radical formation. © 2013 Elsevier B.V. All rights reserved

Towards better reporting of the proportion of days covered method in cardiovascular medication adherence: A scoping review and new tool TEN-SPIDERS

Although medication adherence is commonly measured in electronic datasets using the proportion of days covered (PDC), no standardized approach is used to calculate and report this measure. We conducted a scoping review to understand the approaches taken to calculate and report the PDC for cardiovascular medicines to develop improved guidance for researchers using this measure. After prespecifying methods in a registered protocol, we searched Ovid Medline, Embase, Scopus, CINAHL Plus and grey literature (1 July 2012 to 14 December 2020) for articles containing the terms “proportion of days covered” and “cardiovascular medicine”, or synonyms and subject headings. Of the 523 articles identified, 316 were reviewed in full and 76 were included (93% observational studies; 47% from the USA; 2 grey literature articles). In 45 articles (59%), the PDC was measured from the first dispensing/claim date. Good adherence was defined as 80% PDC in 61 articles, 56% of which contained a rationale for selecting this threshold. The following parameters, important for deriving the PDC, were often not reported/unclear: switching (53%), early refills (45%), in-hospital supplies (45%), presupply (28%) and survival (7%). Of the 46 articles where dosing information was unavailable, 59% reported how doses were imputed. To improve the transparent and systematic reporting of the PDC, we propose the TEN-SPIDERS tool, covering the following PDC parameters: Threshold, Eligibility criteria, Numerator and denominator, Survival, Presupply, In-hospital supplies, Dosing, Early Refills, and Switching. Use of this tool will standardize reporting of the PDC to facilitate reliable comparisons of medication adherence estimates between studies

The cyclic peptide ecumicin targeting clpC1 is active against Mycobacterium tuberculosis in vivo

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Fires in the deep: The luminosity distribution of early-time gamma-ray-burst afterglows in light of the Gamow Explorer sensitivity requirements

Context. Gamma-ray bursts (GRBs) are ideal probes of the Universe at high redshift (ɀ), pinpointing the locations of the earliest star-forming galaxies and providing bright backlights with simple featureless power-law spectra that can be used to spectrally fingerprint the intergalactic medium and host galaxy during the period of reionization. Future missions such as Gamow Explorer (hereafter Gamow) are being proposed to unlock this potential by increasing the rate of identification of high-ɀ (ɀ > 5) GRBs in order to rapidly trigger observations from 6 to 10 m ground telescopes, the James Webb Space Telescope (JWST), and the upcoming Extremely Large Telescopes (ELTs). Aims. Gamow was proposed to the NASA 2021 Medium-Class Explorer (MIDEX) program as a fast-slewing satellite featuring a wide-field lobster-eye X-ray telescope (LEXT) to detect and localize GRBs with arcminute accuracy, and a narrow-field multi-channel photo-ɀ infrared telescope (PIRT) to measure their photometric redshifts for > 80% of the LEXT detections using the Lyman-α dropout technique. We use a large sample of observed GRB afterglows to derive the PIRT sensitivity requirement. Methods. We compiled a complete sample of GRB optical–near-infrared (optical-NIR) afterglows from 2008 to 2021, adding a total of 66 new afterglows to our earlier sample, including all known high-ɀ GRB afterglows. This sample is expanded with over 2837 unpublished data points for 40 of these GRBs. We performed full light-curve and spectral-energy-distribution analyses of these after-glows to derive their true luminosity at very early times. We compared the high-ɀ sample to the comparison sample at lower redshifts. For all the light curves, where possible, we determined the brightness at the time of the initial finding chart of Gamow, at different high redshifts and in different NIR bands. This was validated using a theoretical approach to predicting the afterglow brightness. We then followed the evolution of the luminosity to predict requirements for ground- and space-based follow-up. Finally, we discuss the potential biases between known GRB afterglow samples and those to be detected by Gamow. Results. We find that the luminosity distribution of high-ɀ GRB afterglows is comparable to those at lower redshift, and we therefore are able to use the afterglows of lower-ɀ GRBs as proxies for those at high ɀ. We find that a PIRT sensitivity of 15 µJy (21 mag AB) in a 500 s exposure simultaneously in five NIR bands within 1000 s of the GRB trigger will meet the Gamow mission requirements. Depending on the ɀ and NIR band, we find that between 75% and 85% of all afterglows at ɀ > 5 will be recovered by Gamow at 5σ detection significance, allowing the determination of a robust photo-ɀ. As a check for possible observational biases and selection effects, we compared the results with those obtained through population-synthesis models, and find them to be consistent. Conclusions. Gamow and other high-ɀ GRB missions will be capable of using a relatively modest 0.3 m onboard NIR photo-ɀ telescope to rapidly identify and report high-ɀ GRBs for further follow-up by larger facilities, opening a new window onto the era of reionization and the high-redshift Universe.</p