Deep learning-based segmentation of multisite disease in ovarian cancer

PURPOSE

To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods.

METHODS

A deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established "no-new-Net" framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test.

RESULTS

Our model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (p values being 4 × 10-7, 3 × 10-4, 4 × 10-2, respectively), and for the omental lesions on the evaluation set (p = 1 × 10-3). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (p = 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions.

CONCLUSION

Automated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions.

RELEVANCE STATEMENT

Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines.

KEY POINTS

• The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented. • Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists. • Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines.

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer

High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.

Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation

Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of the models can play a critical role for example in active learning or machine human interaction. Uncertainty quantification is especially difficult when using deep learning-based models, which are the state-of-the-art in many imaging applications. The current uncertainty quantification approaches do not scale well in high-dimensional real-world problems. Scalable solutions often rely on classical techniques, such as dropout, during inference or training ensembles of identical models with different random seeds to obtain a posterior distribution. In this paper, we present the following contributions. First, we show that the classical approaches fail to approximate the classification probability. Second, we propose a scalable and intuitive framework for uncertainty quantification in medical image segmentation that yields measurements that approximate the classification probability. Third, we suggest the usage of k-fold cross-validation to overcome the need for held out calibration data. Lastly, we motivate the adoption of our method in active learning, creating pseudo-labels to learn from unlabeled images and human-machine collaboration.

Integrating Artificial Intelligence Tools in the Clinical Research Setting: The Ovarian Cancer Use Case

Artificial intelligence (AI) methods applied to healthcare problems have shown enormous potential to alleviate the burden of health services worldwide and to improve the accuracy and reproducibility of predictions. In particular, developments in computer vision are creating a paradigm shift in the analysis of radiological images, where AI tools are already capable of automatically detecting and precisely delineating tumours. However, such tools are generally developed in technical departments that continue to be siloed from where the real benefit would be achieved with their usage. Significant effort still needs to be made to make these advancements available, first in academic clinical research and ultimately in the clinical setting. In this paper, we demonstrate a prototype pipeline based entirely on open-source software and free of cost to bridge this gap, simplifying the integration of tools and models developed within the AI community into the clinical research setting, ensuring an accessible platform with visualisation applications that allow end-users such as radiologists to view and interact with the outcome of these AI tools.

Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study

Background

High-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity of clinically-actionable biomarkers due to high degrees of multi-level heterogeneity. Radiogenomics markers have the potential to improve prediction of patient outcome and treatment response, but require accurate multimodal spatial registration between radiological imaging and histopathological tissue samples. Previously published co-registration work has not taken into account the anatomical, biological and clinical diversity of ovarian tumours.

Methods

In this work, we developed a research pathway and an automated computational pipeline to produce lesion-specific three-dimensional (3D) printed moulds based on preoperative cross-sectional CT or MRI of pelvic lesions. Moulds were designed to allow tumour slicing in the anatomical axial plane to facilitate detailed spatial correlation of imaging and tissue-derived data. Code and design adaptations were made following each pilot case through an iterative refinement process.

Results

Five patients with confirmed or suspected HGSOC who underwent debulking surgery between April and December 2021 were included in this prospective study. Tumour moulds were designed and 3D-printed for seven pelvic lesions, covering a range of tumour volumes (7 to 133 cm3) and compositions (cystic and solid proportions). The pilot cases informed innovations to improve specimen and subsequent slice orientation, through the use of 3D-printed tumour replicas and incorporation of a slice orientation slit in the mould design, respectively. The overall research pathway was compatible with implementation within the clinically determined timeframe and treatment pathway for each case, involving multidisciplinary clinical professionals from Radiology, Surgery, Oncology and Histopathology Departments.

Conclusions

We developed and refined a computational pipeline that can model lesion-specific 3D-printed moulds from preoperative imaging for a variety of pelvic tumours. This framework can be used to guide comprehensive multi-sampling of tumour resection specimens.

Photoacoustic imaging radiomics in patient-derived xenografts: a study on feature sensitivity and model discrimination

Photoacoustic imaging is an increasingly popular method of exploring the tumour microenvironment, which can provide insight into tumour oxygenation status and potentially treatment response assessment. Currently, the measurements most commonly performed on such images are the mean and median of the pixel values of the tumour volumes of interest. We investigated expanding the set of measurements that can be extracted from these images by adding radiomic features. In particular, we found that Skewness was sensitive to differences between basal and luminal patient derived xenograft cancer models with an [Formula: see text] of 0.86, and that it was robust to variations in confounding factors such as reconstruction type and wavelength. We also built discriminant models with radiomic features that were correlated with the underlying tumour model and were independent from each other. We then ranked features by their importance in the model. Skewness was again found to be an important feature, as were 10th Percentile, Root Mean Squared, and several other texture-based features. In summary, this paper proposes a methodology to select radiomic features extracted from photoacoustic images that are robust to changes in acquisition and reconstruction parameters, and discusses features found to have discriminating power between the underlying tumour models in a pre-clinical dataset.

Search for Subsolar-Mass Binaries in the First Half of Advanced LIGO's and Advanced Virgo's Third Observing Run

We report on a search for compact binary coalescences where at least one binary component has a mass between 0.2  M_{⊙} and 1.0  M_{⊙} in Advanced LIGO and Advanced Virgo data collected between 1 April 2019 1500 UTC and 1 October 2019 1500 UTC. We extend our previous analyses in two main ways: we include data from the Virgo detector and we allow for more unequal mass systems, with mass ratio q≥0.1. We do not report any gravitational-wave candidates. The most significant trigger has a false alarm rate of 0.14  yr^{-1}. This implies an upper limit on the merger rate of subsolar binaries in the range [220-24200]  Gpc^{-3} yr^{-1}, depending on the chirp mass of the binary. We use this upper limit to derive astrophysical constraints on two phenomenological models that could produce subsolar-mass compact objects. One is an isotropic distribution of equal-mass primordial black holes. Using this model, we find that the fraction of dark matter in primordial black holes in the mass range 0.2  M_{⊙}<m_{PBH}<1.0  M_{⊙} is f_{PBH}≡Ω_{PBH}/Ω_{DM}≲6%. This improves existing constraints on primordial black hole abundance by a factor of ∼3. The other is a dissipative dark matter model, in which fermionic dark matter can collapse and form black holes. The upper limit on the fraction of dark matter black holes depends on the minimum mass of the black holes that can be formed: the most constraining result is obtained at M_{min}=1  M_{⊙}, where f_{DBH}≡Ω_{DBH}/Ω_{DM}≲0.003%. These are the first constraints placed on dissipative dark models by subsolar-mass analyses.

Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma

Background

Pathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy response score (CRS) for omental tumor deposits. The main limitation of CRS is that it requires surgical sampling after initial neoadjuvant chemotherapy (NACT) treatment. Earlier and non-invasive response predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures to predict neoadjuvant response before NACT using CRS as a gold standard.

Methods

Omental CT-based radiomics models, yielding a simplified fully interpretable radiomic signature, were developed using Elastic Net logistic regression and compared to predictions based on omental tumor volume alone. Models were developed on a single institution cohort of neoadjuvant-treated HGSOC (n = 61; 41% complete response to NCT) and tested on an external test cohort (n = 48; 21% complete response).

Results

The performance of the comprehensive radiomics models and the fully interpretable radiomics model was significantly higher than volume-based predictions of response in both the discovery and external test sets when assessed using G-mean (geometric mean of sensitivity and specificity) and NPV, indicating high generalizability and reliability in identifying non-responders when using radiomics. The performance of a fully interpretable model was similar to that of comprehensive radiomics models.

Conclusions

CT-based radiomics allows for predicting response to NACT in a timely manner and without the need for abdominal surgery. Adding pre-NACT radiomics to volumetry improved model performance for predictions of response to NACT in HGSOC and was robust to external testing. A radiomic signature based on five robust predictive features provides improved clinical interpretability and may thus facilitate clinical acceptance and application.

Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies

Purpose: XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import, archiving, processing and secure distribution facilities for image and related study data. Until recently, however, modern data visualisation and annotation tools were lacking on the XNAT platform. We describe the background to, and implementation of, an integration of the Open Health Imaging Foundation (OHIF) Viewer into the XNAT environment. We explain the challenges overcome and discuss future prospects for quantitative imaging studies. Materials and methods: The OHIF Viewer adopts an approach based on the DICOM web protocol. To allow operation in an XNAT environment, a data-routing methodology was developed to overcome the mismatch between the DICOM and XNAT information models and a custom viewer panel created to allow navigation within the viewer between different XNAT projects, subjects and imaging sessions. Modifications to the development environment were made to allow developers to test new code more easily against a live XNAT instance. Major new developments focused on the creation and storage of regions-of-interest (ROIs) and included: ROI creation and editing tools for both contour- and mask-based regions; a "smart CT" paintbrush tool; the integration of NVIDIA's Artificial Intelligence Assisted Annotation (AIAA); the ability to view surface meshes, fractional segmentation maps and image overlays; and a rapid image reader tool aimed at radiologists. We have incorporated the OHIF microscopy extension and, in parallel, introduced support for microscopy session types within XNAT for the first time. Results: Integration of the OHIF Viewer within XNAT has been highly successful and numerous additional and enhanced tools have been created in a programme started in 2017 that is still ongoing. The software has been downloaded more than 3700 times during the course of the development work reported here, demonstrating the impact of the work. Conclusions: The OHIF open-source, zero-footprint web viewer has been incorporated into the XNAT platform and is now used at many institutions worldwide. Further innovations are envisaged in the near future.

Point Absorber Limits to Future Gravitational-Wave Detectors

High-quality optical resonant cavities require low optical loss, typically on the scale of parts per million. However, unintended micron-scale contaminants on the resonator mirrors that absorb the light circulating in the cavity can deform the surface thermoelastically and thus increase losses by scattering light out of the resonant mode. The point absorber effect is a limiting factor in some high-power cavity experiments, for example, the Advanced LIGO gravitational-wave detector. In this Letter, we present a general approach to the point absorber effect from first principles and simulate its contribution to the increased scattering. The achievable circulating power in current and future gravitational-wave detectors is calculated statistically given different point absorber configurations. Our formulation is further confirmed experimentally in comparison with the scattered power in the arm cavity of Advanced LIGO measured by in situ photodiodes. The understanding presented here provides an important tool in the global effort to design future gravitational-wave detectors that support high optical power and thus reduce quantum noise.

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

Approaching the motional ground state of a 10-kg object

The motion of a mechanical object, even a human-sized object, should be governed by the rules of quantum mechanics. Coaxing them into a quantum state is, however, difficult because the thermal environment masks any quantum signature of the object's motion. The thermal environment also masks the effects of proposed modifications of quantum mechanics at large mass scales. We prepared the center-of-mass motion of a 10-kilogram mechanical oscillator in a state with an average phonon occupation of 10.8. The reduction in temperature, from room temperature to 77 nanokelvin, is commensurate with an 11 orders-of-magnitude suppression of quantum back-action by feedback and a 13 orders-of-magnitude increase in the mass of an object prepared close to its motional ground state. Our approach will enable the possibility of probing gravity on massive quantum systems.

Constraints on Cosmic Strings Using Data from the Third Advanced LIGO-Virgo Observing Run

We search for gravitational-wave signals produced by cosmic strings in the Advanced LIGO and Virgo full O3 dataset. Search results are presented for gravitational waves produced by cosmic string loop features such as cusps, kinks, and, for the first time, kink-kink collisions. A template-based search for short-duration transient signals does not yield a detection. We also use the stochastic gravitational-wave background energy density upper limits derived from the O3 data to constrain the cosmic string tension Gμ as a function of the number of kinks, or the number of cusps, for two cosmic string loop distribution models. Additionally, we develop and test a third model that interpolates between these two models. Our results improve upon the previous LIGO-Virgo constraints on Gμ by 1 to 2 orders of magnitude depending on the model that is tested. In particular, for the one-loop distribution model, we set the most competitive constraints to date: Gμ≲4×10^{-15}. In the case of cosmic strings formed at the end of inflation in the context of grand unified theories, these results challenge simple inflationary models.

Constraints on Cosmic Strings Using Data from the Third Advanced LIGO-Virgo Observing Run

We search for gravitational-wave signals produced by cosmic strings in the Advanced LIGO and Virgo full O3 dataset. Search results are presented for gravitational waves produced by cosmic string loop features such as cusps, kinks, and, for the first time, kink-kink collisions. A template-based search for short-duration transient signals does not yield a detection. We also use the stochastic gravitational-wave background energy density upper limits derived from the O3 data to constrain the cosmic string tension Gμ as a function of the number of kinks, or the number of cusps, for two cosmic string loop distribution models. Additionally, we develop and test a third model that interpolates between these two models. Our results improve upon the previous LIGO-Virgo constraints on Gμ by 1 to 2 orders of magnitude depending on the model that is tested. In particular, for the one-loop distribution model, we set the most competitive constraints to date: Gμ≲4×10^{-15}. In the case of cosmic strings formed at the end of inflation in the context of grand unified theories, these results challenge simple inflationary models.

Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients

Computed Tomography (CT) is widely used in oncology for morphological evaluation and diagnosis, commonly through visual assessments, often exploiting semi-automatic tools as well. Well-established automatic methods for quantitative imaging offer the opportunity to enrich the radiologist interpretation with a large number of radiomic features, which need to be highly reproducible to be used reliably in clinical practice. This study investigates feature reproducibility against noise, varying resolutions and segmentations (achieved by perturbing the regions of interest), in a CT dataset with heterogeneous voxel size of 98 renal cell carcinomas (RCCs) and 93 contralateral normal kidneys (CK). In particular, first order (FO) and second order texture features based on both 2D and 3D grey level co-occurrence matrices (GLCMs) were considered. Moreover, this study carries out a comparative analysis of three of the most commonly used interpolation methods, which need to be selected before any resampling procedure. Results showed that the Lanczos interpolation is the most effective at preserving original information in resampling, where the median slice resolution coupled with the native slice spacing allows the best reproducibility, with 94.6% and 87.7% of features, in RCC and CK, respectively. GLCMs show their maximum reproducibility when used at short distances.

Robustness of radiomic features in CT images with different slice thickness, comparing liver tumour and muscle

Radiomic image features are becoming a promising non-invasive method to obtain quantitative measurements for tumour classification and therapy response assessment in oncological research. However, despite its increasingly established application, there is a need for standardisation criteria and further validation of feature robustness with respect to imaging acquisition parameters. In this paper, the robustness of radiomic features extracted from computed tomography (CT) images is evaluated for liver tumour and muscle, comparing the values of the features in images reconstructed with two different slice thicknesses of 2.0 mm and 5.0 mm. Novel approaches are presented to address the intrinsic dependencies of texture radiomic features, choosing the optimal number of grey levels and correcting for the dependency on volume. With the optimal values and corrections, feature values are compared across thicknesses to identify reproducible features. Normalisation using muscle regions is also described as an alternative approach. With either method, a large fraction of features (75-90%) was found to be highly robust (< 25% difference). The analyses were performed on a homogeneous CT dataset of 43 patients with hepatocellular carcinoma, and consistent results were obtained for both tumour and muscle tissue. Finally, recommended guidelines are included for radiomic studies using variable slice thickness.

GW190521: A Binary Black Hole Merger with a Total Mass of 150 M_{⊙}

On May 21, 2019 at 03:02:29 UTC Advanced LIGO and Advanced Virgo observed a short duration gravitational-wave signal, GW190521, with a three-detector network signal-to-noise ratio of 14.7, and an estimated false-alarm rate of 1 in 4900 yr using a search sensitive to generic transients. If GW190521 is from a quasicircular binary inspiral, then the detected signal is consistent with the merger of two black holes with masses of 85_{-14}^{+21}  M_{⊙} and 66_{-18}^{+17}  M_{⊙} (90% credible intervals). We infer that the primary black hole mass lies within the gap produced by (pulsational) pair-instability supernova processes, with only a 0.32% probability of being below 65  M_{⊙}. We calculate the mass of the remnant to be 142_{-16}^{+28}  M_{⊙}, which can be considered an intermediate mass black hole (IMBH). The luminosity distance of the source is 5.3_{-2.6}^{+2.4}  Gpc, corresponding to a redshift of 0.82_{-0.34}^{+0.28}. The inferred rate of mergers similar to GW190521 is 0.13_{-0.11}^{+0.30}  Gpc^{-3} yr^{-1}.

Quantum-Enhanced Advanced LIGO Detectors in the Era of Gravitational-Wave Astronomy

The Laser Interferometer Gravitational Wave Observatory (LIGO) has been directly detecting gravitational waves from compact binary mergers since 2015. We report on the first use of squeezed vacuum states in the direct measurement of gravitational waves with the Advanced LIGO H1 and L1 detectors. This achievement is the culmination of decades of research to implement squeezed states in gravitational-wave detectors. During the ongoing O3 observation run, squeezed states are improving the sensitivity of the LIGO interferometers to signals above 50 Hz by up to 3 dB, thereby increasing the expected detection rate by 40% (H1) and 50% (L1).

Search for Subsolar Mass Ultracompact Binaries in Advanced LIGO's Second Observing Run

We present a search for subsolar mass ultracompact objects in data obtained during Advanced LIGO's second observing run. In contrast to a previous search of Advanced LIGO data from the first observing run, this search includes the effects of component spin on the gravitational waveform. We identify no viable gravitational-wave candidates consistent with subsolar mass ultracompact binaries with at least one component between 0.2  M_{⊙}-1.0  M_{⊙}. We use the null result to constrain the binary merger rate of (0.2  M_{⊙}, 0.2  M_{⊙}) binaries to be less than 3.7×10^{5}  Gpc^{-3} yr^{-1} and the binary merger rate of (1.0  M_{⊙}, 1.0  M_{⊙}) binaries to be less than 5.2×10^{3}  Gpc^{-3} yr^{-1}. Subsolar mass ultracompact objects are not expected to form via known stellar evolution channels, though it has been suggested that primordial density fluctuations or particle dark matter with cooling mechanisms and/or nuclear interactions could form black holes with subsolar masses. Assuming a particular primordial black hole (PBH) formation model, we constrain a population of merging 0.2  M_{⊙} black holes to account for less than 16% of the dark matter density and a population of merging 1.0  M_{⊙} black holes to account for less than 2% of the dark matter density. We discuss how constraints on the merger rate and dark matter fraction may be extended to arbitrary black hole population models that predict subsolar mass binaries.

Tests of General Relativity with GW170817

The recent discovery by Advanced LIGO and Advanced Virgo of a gravitational wave signal from a binary neutron star inspiral has enabled tests of general relativity (GR) with this new type of source. This source, for the first time, permits tests of strong-field dynamics of compact binaries in the presence of matter. In this Letter, we place constraints on the dipole radiation and possible deviations from GR in the post-Newtonian coefficients that govern the inspiral regime. Bounds on modified dispersion of gravitational waves are obtained; in combination with information from the observed electromagnetic counterpart we can also constrain effects due to large extra dimensions. Finally, the polarization content of the gravitational wave signal is studied. The results of all tests performed here show good agreement with GR.

Constraining the p-Mode-g-Mode Tidal Instability with GW170817

We analyze the impact of a proposed tidal instability coupling p modes and g modes within neutron stars on GW170817. This nonresonant instability transfers energy from the orbit of the binary to internal modes of the stars, accelerating the gravitational-wave driven inspiral. We model the impact of this instability on the phasing of the gravitational wave signal using three parameters per star: an overall amplitude, a saturation frequency, and a spectral index. Incorporating these additional parameters, we compute the Bayes factor (lnB_{!pg}^{pg}) comparing our p-g model to a standard one. We find that the observed signal is consistent with waveform models that neglect p-g effects, with lnB_{!pg}^{pg}=0.03_{-0.58}^{+0.70} (maximum a posteriori and 90% credible region). By injecting simulated signals that do not include p-g effects and recovering them with the p-g model, we show that there is a ≃50% probability of obtaining similar lnB_{!pg}^{pg} even when p-g effects are absent. We find that the p-g amplitude for 1.4  M_{⊙} neutron stars is constrained to less than a few tenths of the theoretical maximum, with maxima a posteriori near one-tenth this maximum and p-g saturation frequency ∼70  Hz. This suggests that there are less than a few hundred excited modes, assuming they all saturate by wave breaking. For comparison, theoretical upper bounds suggest ≲10^{3} modes saturate by wave breaking. Thus, the measured constraints only rule out extreme values of the p-g parameters. They also imply that the instability dissipates ≲10^{51}  erg over the entire inspiral, i.e., less than a few percent of the energy radiated as gravitational waves.

Search for Subsolar-Mass Ultracompact Binaries in Advanced LIGO's First Observing Run

We present the first Advanced LIGO and Advanced Virgo search for ultracompact binary systems with component masses between 0.2  M_{⊙}-1.0  M_{⊙} using data taken between September 12, 2015 and January 19, 2016. We find no viable gravitational wave candidates. Our null result constrains the coalescence rate of monochromatic (delta function) distributions of nonspinning (0.2  M_{⊙}, 0.2  M_{⊙}) ultracompact binaries to be less than 1.0×10^{6}  Gpc^{-3}  yr^{-1} and the coalescence rate of a similar distribution of (1.0  M_{⊙}, 1.0  M_{⊙}) ultracompact binaries to be less than 1.9×10^{4}  Gpc^{-3}  yr^{-1} (at 90% confidence). Neither black holes nor neutron stars are expected to form below ∼1  M_{⊙} through conventional stellar evolution, though it has been proposed that similarly low mass black holes could be formed primordially through density fluctuations in the early Universe and contribute to the dark matter density. The interpretation of our constraints in the primordial black hole dark matter paradigm is highly model dependent; however, under a particular primordial black hole binary formation scenario we constrain monochromatic primordial black hole populations of 0.2  M_{⊙} to be less than 33% of the total dark matter density and monochromatic populations of 1.0  M_{⊙} to be less than 5% of the dark matter density. The latter strengthens the presently placed bounds from microlensing surveys of massive compact halo objects (MACHOs) provided by the MACHO and EROS Collaborations.

GW170817: Measurements of Neutron Star Radii and Equation of State

On 17 August 2017, the LIGO and Virgo observatories made the first direct detection of gravitational waves from the coalescence of a neutron star binary system. The detection of this gravitational-wave signal, GW170817, offers a novel opportunity to directly probe the properties of matter at the extreme conditions found in the interior of these stars. The initial, minimal-assumption analysis of the LIGO and Virgo data placed constraints on the tidal effects of the coalescing bodies, which were then translated to constraints on neutron star radii. Here, we expand upon previous analyses by working under the hypothesis that both bodies were neutron stars that are described by the same equation of state and have spins within the range observed in Galactic binary neutron stars. Our analysis employs two methods: the use of equation-of-state-insensitive relations between various macroscopic properties of the neutron stars and the use of an efficient parametrization of the defining function p(ρ) of the equation of state itself. From the LIGO and Virgo data alone and the first method, we measure the two neutron star radii as R_{1}=10.8_{-1.7}^{+2.0}  km for the heavier star and R_{2}=10.7_{-1.5}^{+2.1}  km for the lighter star at the 90% credible level. If we additionally require that the equation of state supports neutron stars with masses larger than 1.97  M_{⊙} as required from electromagnetic observations and employ the equation-of-state parametrization, we further constrain R_{1}=11.9_{-1.4}^{+1.4}  km and R_{2}=11.9_{-1.4}^{+1.4}  km at the 90% credible level. Finally, we obtain constraints on p(ρ) at supranuclear densities, with pressure at twice nuclear saturation density measured at 3.5_{-1.7}^{+2.7}×10^{34}  dyn cm^{-2} at the 90% level.

Search for Tensor, Vector, and Scalar Polarizations in the Stochastic Gravitational-Wave Background

The detection of gravitational waves with Advanced LIGO and Advanced Virgo has enabled novel tests of general relativity, including direct study of the polarization of gravitational waves. While general relativity allows for only two tensor gravitational-wave polarizations, general metric theories can additionally predict two vector and two scalar polarizations. The polarization of gravitational waves is encoded in the spectral shape of the stochastic gravitational-wave background, formed by the superposition of cosmological and individually unresolved astrophysical sources. Using data recorded by Advanced LIGO during its first observing run, we search for a stochastic background of generically polarized gravitational waves. We find no evidence for a background of any polarization, and place the first direct bounds on the contributions of vector and scalar polarizations to the stochastic background. Under log-uniform priors for the energy in each polarization, we limit the energy densities of tensor, vector, and scalar modes at 95% credibility to Ω_{0}^{T}<5.58×10^{-8}, Ω_{0}^{V}<6.35×10^{-8}, and Ω_{0}^{S}<1.08×10^{-7} at a reference frequency f_{0}=25  Hz.

GW170817: Implications for the Stochastic Gravitational-Wave Background from Compact Binary Coalescences

The LIGO Scientific and Virgo Collaborations have announced the event GW170817, the first detection of gravitational waves from the coalescence of two neutron stars. The merger rate of binary neutron stars estimated from this event suggests that distant, unresolvable binary neutron stars create a significant astrophysical stochastic gravitational-wave background. The binary neutron star component will add to the contribution from binary black holes, increasing the amplitude of the total astrophysical background relative to previous expectations. In the Advanced LIGO-Virgo frequency band most sensitive to stochastic backgrounds (near 25 Hz), we predict a total astrophysical background with amplitude Ω_{GW}(f=25  Hz)=1.8_{-1.3}^{+2.7}×10^{-9} with 90% confidence, compared with Ω_{GW}(f=25  Hz)=1.1_{-0.7}^{+1.2}×10^{-9} from binary black holes alone. Assuming the most probable rate for compact binary mergers, we find that the total background may be detectable with a signal-to-noise-ratio of 3 after 40 months of total observation time, based on the expected timeline for Advanced LIGO and Virgo to reach their design sensitivity.

GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral

On August 17, 2017 at 12∶41:04 UTC the Advanced LIGO and Advanced Virgo gravitational-wave detectors made their first observation of a binary neutron star inspiral. The signal, GW170817, was detected with a combined signal-to-noise ratio of 32.4 and a false-alarm-rate estimate of less than one per 8.0×10^{4}  years. We infer the component masses of the binary to be between 0.86 and 2.26  M_{⊙}, in agreement with masses of known neutron stars. Restricting the component spins to the range inferred in binary neutron stars, we find the component masses to be in the range 1.17-1.60  M_{⊙}, with the total mass of the system 2.74_{-0.01}^{+0.04}M_{⊙}. The source was localized within a sky region of 28  deg^{2} (90% probability) and had a luminosity distance of 40_{-14}^{+8}  Mpc, the closest and most precisely localized gravitational-wave signal yet. The association with the γ-ray burst GRB 170817A, detected by Fermi-GBM 1.7 s after the coalescence, corroborates the hypothesis of a neutron star merger and provides the first direct evidence of a link between these mergers and short γ-ray bursts. Subsequent identification of transient counterparts across the electromagnetic spectrum in the same location further supports the interpretation of this event as a neutron star merger. This unprecedented joint gravitational and electromagnetic observation provides insight into astrophysics, dense matter, gravitation, and cosmology.

GW170814: A Three-Detector Observation of Gravitational Waves from a Binary Black Hole Coalescence

On August 14, 2017 at 10∶30:43 UTC, the Advanced Virgo detector and the two Advanced LIGO detectors coherently observed a transient gravitational-wave signal produced by the coalescence of two stellar mass black holes, with a false-alarm rate of ≲1 in 27 000 years. The signal was observed with a three-detector network matched-filter signal-to-noise ratio of 18. The inferred masses of the initial black holes are 30.5_{-3.0}^{+5.7}M_{⊙} and 25.3_{-4.2}^{+2.8}M_{⊙} (at the 90% credible level). The luminosity distance of the source is 540_{-210}^{+130}  Mpc, corresponding to a redshift of z=0.11_{-0.04}^{+0.03}. A network of three detectors improves the sky localization of the source, reducing the area of the 90% credible region from 1160   deg^{2} using only the two LIGO detectors to 60  deg^{2} using all three detectors. For the first time, we can test the nature of gravitational-wave polarizations from the antenna response of the LIGO-Virgo network, thus enabling a new class of phenomenological tests of gravity.

Relationship of obesity to depression: a family-based study

OBJECTIVE

To examine the relationship between obesity and depression in a sample of extremely obese individuals and their siblings and parents.

SUBJECTS

A total of 1730 European Americans (558 men, 1172 women, aged 49.29+/-15.42 y, body mass index (BMI) of 35.57+/-11.53 kg/m(2)) and 373 African Americans (103 men, 270 women, aged 44.85+/-15.08 years, BMI of 36.83+/-11.31 kg/m(2)) in a sample of 482 nuclear families segregating extreme obesity and normal weight.

MEASUREMENTS

Individual BMI, history of depression treatment and covariates (age, sex, race, education, marital status, socioeconomic status, chronic medical conditions and exercise program).

RESULTS

Greater odds for depression were found for the obese, European American, women, the unmarried, the more educated, those with chronic physical disorder(s) and the offspring of depressed parents. A trend test found that the odds ratios for depression increased with BMI and number of chronic medical conditions (P<0.0001). Multivariate logistic regression analyses indicated that BMI, race, marital status, chronic medical conditions and family history were the predicators of depression for both the genders. Hierarchical analyses revealed that BMI significantly increased the risk above that predicated by the combined effects of all other variables.

CONCLUSIONS

Extreme obesity was associated with the increased risk for depression across gender and racial groups, even after controlling for chronic physical disease, familial depression and demographic risk factors. More detailed research is needed to determine the underlying mechanisms.

Suicide in mood disorders

A selective literature review was conducted to determine the link between mood disorders and suicide in children and adolescents. On-line searches of Medline and PubMed were performed and research articles from 1978 to 2001 were reviewed. Mood disorders are reported to be the most common psychiatric illnesses in children and adolescents who attempt or commit suicide. Reports suggest that depression co-morbid with any other psychiatric illness, externalizing disorders, or substance abuse further increases the risk for suicide completion. Mood disorders in children and adolescents are frequently underdiagnosed, misdiagnosed, and undertreated. Data suggest that very early identification combined with aggressive and sustained treatment of mood disorders in youth may actually lessen the risk for suicide.

A prospective cohort study of genetic and perinatal influences in the etiology of schizophrenia

In this study, we examined whether fetal hypoxia and other obstetric complications (OCs) are related to risk for adult schizophrenia; whether such effects are specific to cases with an early age at onset; and whether the obstetric influences depend on, covary with, or are independent of familial risk. Subjects were 72 patients with schizophrenia or schizoaffective disorder; 63 of their siblings not diagnosed with schizophrenia; and 7,941 nonpsychiatric controls, whose gestations and births were monitored prospectively with standard research protocols as part of the National Collaborative Perinatal Project. Adult psychiatric morbidity was ascertained via a longitudinal treatment data base indexing regional public health service utilization, and diagnoses were made by review of all pertinent medical records according to DSM-IV criteria. We found that the odds of schizophrenia increased linearly with increasing number of hypoxia-associated OCs and that this effect was specific to cases with an early age at onset/first treatment contact. There were no relationships between schizophrenia and birth weight or other (prenatal/nonhypoxic) OCs. Siblings of patients with schizophrenia were no more likely to have suffered hypoxia-associated OCs than were nonpsychiatric cohort controls. Because the majority of individuals exposed to fetal hypoxia did not develop schizophrenia, such factors likely are incapable of causing schizophrenia on their own. Together, these findings suggest that hypoxia acts additively or interactively with genetic factors in influencing liability to schizophrenia. We propose a model in which the neurotoxic effects of fetal hypoxia may lead to an earlier onset of psychosis because of premature pruning of cortical synapses.

Childhood neuromotor dysfunction in schizophrenia patients and their unaffected siblings: a prospective cohort study

Neuromotor dysfunction is a consistent finding in high-risk and archival studies of schizophrenia, but the sources of this dysfunction and its role in the developmental course of the disorder remain poorly understood. This study examined childhood motor predictors of adult psychiatric outcome in a birth cohort sample (72 patients with schizophrenia or schizoaffective disorder, 63 unaffected siblings, and 7,941 nonpsychiatric controls), evaluated prospectively with neurologic examinations at 8 months, 4 years, and 7 years of age. Deviance on motor coordination measures at 7 years was associated with both adult schizophrenia and unaffected sibling status, suggesting that a cofamilial (and perhaps genetic) factor underlies motor coordination deficits in schizophrenia. Unusual movements at ages 4 and 7 predicted adult schizophrenia but not unaffected sibling status, indicating that these deficits may be specific to those who will develop the clinical phenotype. None of the motor precursors were confined to patients with an early age at first treatment contact. Fetal hypoxia predicted unusual movements at 4 but not 7 years among the preschizophrenia subjects, suggesting neurodevelopmental dependence of its functional effects. Neither prenatal complications nor birth weight were associated with motor dysfunction in preschizophrenia subjects or their unaffected siblings at any age. Finally, preschizophrenia children did not show the expected developmental decline in unusual movements, perhaps reflecting aberrant functional maturation of cortical-subcortical pathways.

A prospective cohort study of childhood behavioral deviance and language abnormalities as predictors of adult schizophrenia

Language and behavioral deviance in early childhood in preschizophrenia individuals suggests that the pathologic processes predisposing to schizophrenia are present from early in life. However, the etiologic antecedents of such impairments, and the degree to which they predict adult schizophrenia, have not been conclusively demonstrated. To address this, we examined language and behavioral predictors of adult psychiatric outcome in a population cohort (72 individuals with schizophrenia or schizoaffective disorder, 63 of their unaffected siblings, and 7,941 with no diagnosis) evaluated prospectively with behavioral examinations and a speech and language evaluation at 8 months, 4 years, and 7 years of age. Psychiatric outcome was ascertained via adult treatment contacts, and diagnoses were made by chart review according to DSM-IV criteria. Social maladjustment at age 7 was found to predict adult schizophrenia, and focal deviant behaviors (e.g., echolalia, meaningless laughter) at ages 4 and 7 were significantly associated with both schizophrenia and sibling status. Unintelligible speech at age 7 was a highly significant predictor of adult schizophrenia (odds ratio = 12.7), and poor expressive language ability predicted both schizophrenia and unaffected sibling outcome. Early behavioral and language dysfunction did not differentially characterize preschizophrenia subjects with a history of fetal hypoxia or an early age of first treatment contact. Given that unaffected siblings show similar signs of deviance, such problems may indicate genotypic susceptibility to the disorder, or shared environmental influences, or both.

Childhood cognitive functioning in schizophrenia patients and their unaffected siblings: a prospective cohort study

While it is known that children of schizophrenia parents perform more poorly on tests of cognitive functioning than children of normal parents, less certain is the degree to which such deficits predict schizophrenia outcome, whether cognitive functioning deteriorates during childhood in preschizophrenia individuals, and whether nongenetic etiologic factors (such as obstetric complications) contribute to these deficits. In the present study, 72 patients with schizophrenia or schizoaffective disorder, 63 of their siblings not diagnosed with schizophrenia, and 7,941 controls with no diagnosis were ascertained from a birth cohort whose members had been evaluated with standardized tests of cognitive functioning at 4 and 7 years of age. Adult psychiatric morbidity was ascertained via a longitudinal treatment data base indexing regional public health service utilization, and diagnoses were made by review of all pertinent medical records according to DSM-IV criteria. Both the patients with schizophrenia and their unaffected siblings performed significantly worse than the nonpsychiatric controls (but did not differ from each other) on verbal and nonverbal cognitive tests at 4 and 7 years of age. Preschizophrenia cases and their siblings were increasingly overrepresented across decreasing quartiles of the performance distributions. There was not significant intra-individual decline, and there were no significant relationships between obstetric complications and test performance among the preschizophrenia subjects. These results suggest that during the period from age 4 to age 7 years, premorbid cognitive dysfunction in schizophrenia represents a relatively stable indicator of vulnerability deriving from primarily genetic (and/or shared environmental) etiologic influences.

A prospective cohort study of neurodevelopmental processes in the genesis and epigenesis of schizophrenia

A number of lines of evidence converge in implicating neurodevelopmental processes in the etiology and epigenesis of schizophrenia. In this study we used a prospective, longitudinal design to examine whether adverse obstetric experiences predict schizophrenia and whether there is a deviant functional-developmental trajectory during the first 7 years of life among individuals who manifest schizophrenia as adults. The 9,236 members of the Philadelphia cohort of the National Collaborative Perinatal Project were screened for mental health service utilization in adulthood, and chart reviews were performed to establish diagnoses according to DSM-IV criteria. The risk for schizophrenia increased linearly with the number of hypoxia-associated obstetric complications but was unrelated to maternal infection during pregnancy or fetal growth retardation. Preschizophrenic cases (and their unaffected siblings who were also cohort members) manifested cognitive impairment, abnormal involuntary movements and coordination deficits, and poor social adjustment during childhood. There was no evidence of intraindividual decline in any domain, but preschizophrenic cases did show deviance on an increasing number of functional indicators with age. Together, these findings suggest that both genetic and obstetric factors participate in creating a neural diathesis to schizophrenia, the phenotypic expressions of which are age dependent, probably reflecting the maturational status of a number of interconnected brain systems.

A pilot study of clomipramine in young autistic children

OBJECTIVE

To assess the short-term efficacy and safety of clomipramine in hospitalized young children with autism.

METHOD

This was an open pilot study; after a 1-week placebo baseline, subjects were treated with clomipramine for 5 weeks. Dosage was individually regulated; starting dose was 25 mg/day; increments were 25 mg/day. Maximum dose was 250 mg/day or 5.0 mg/kg per day, whichever was less. Multiple raters, under several conditions, used the Children's Psychiatric Rating Scale, Clinical Global Impressions, Conners Parent Teacher Questionnaire, and the Clinical Global Consensus Ratings.

RESULTS

Eight children, aged 3.5 to 8.7 years, were enrolled in the study; seven of these completed the study. A 3.5-year-old boy was excluded during the third week of treatment after having urinary retention on two occasions. At doses ranging from 2.50 to 4.64 mg/kg per day (mean = 3.14), one child improved moderately and six were rated as worse on the Clinical Global Consensus Ratings. Untoward effects were common. CONCLUSIONS; Clomipramine was not therapeutic and was associated with serious untoward effects in this sample. Young autistic children may be more prone to experience untoward effects than older patients.

Heterokaryosis in Fusarium oxysporum f.sp. lycopersici

Intra-isolate and inter-isolate heterokaryons were synthesized between auxotrophic mutants of Fusarium crown rot, 'purple variant' and the wilt isolates. This is the first report of intra- and inter-isolate heterokaryons in Fusarium oxysporum f.sp. lycopersici. Conidial ratios determined for several heterokaryons between different mutants of the Fusarium crown rot organism showed that the ratio is constant for each heterokaryon and that the ratio usually is in favour of one of the mutants.

Chemical mutagenesis of Fusarium oxysporum f. sp. lycopersici: non-selected changes in pathogenicity of auxotrophic mutants

Single and multiple auxotrophic mutants of the Fusarium oxysporum f. sp.lycopersici strains which cause Fusarium crown-rot and Fusarium wilt of tomato were obtained by chemical mutagenesis with nitrous acid, nitrosoguandidine and ethylmethaneulphonate. The mutagenesis and selection procedures, adapted for use with a plant pathogenic fungus, are described. Changes in pathogenicity were observed when the auxotrophs were compared with the wild type but no correlation was observed between changes in pathogenicity and the particular nutritional requirement.