First narrow-band search for continuous gravitational waves from known pulsars in advanced detector data

Spinning neutron stars asymmetric with respect to their rotation axis are potential sources of continuous gravitational waves for ground-based interferometric detectors. In the case of known pulsars a fully coherent search, based on matched filtering, which uses the position and rotational parameters obtained from electromagnetic observations, can be carried out. Matched filtering maximizes the signalto- noise (SNR) ratio, but a large sensitivity loss is expected in case of even a very small mismatch between the assumed and the true signal parameters. For this reason, narrow-band analysis methods have been developed, allowing a fully coherent search for gravitational waves from known pulsars over a fraction of a hertz and several spin-down values. In this paper we describe a narrow-band search of 11 pulsars using data from Advanced LIGO’s first observing run. Although we have found several initial outliers, further studies show no significant evidence for the presence of a gravitational wave signal. Finally, we have placed upper limits on the signal strain amplitude lower than the spin-down limit for 5 of the 11 targets over the bands searched; in the case of J1813-1749 the spin-down limit has been beaten for the first time. For an additional 3 targets, the median upper limit across the search bands is below the spin-down limit. This is the most sensitive narrow-band search for continuous gravitational waves carried out so far

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Regret affects the choice between neoadjuvant therapy and upfront surgery for potentially resectable pancreatic cancer

Background: When treating potentially resectable pancreatic adenocarcinoma, therapeutic decisions are left to the sensibility of treating clinicians who, faced with a decision that post hoc can be proven wrong, may feel a sense of regret that they want to avoid. A regret-based decision model was applied to evaluate attitudes toward neoadjuvant therapy versus upfront surgery for potentially resectable pancreatic adenocarcinoma. Methods: Three clinical scenarios describing high-, intermediate-, and low-risk disease-specific mortality after upfront surgery were presented to 60 respondents (20 oncologists, 20 gastroenterologists, and 20 surgeons). Respondents were asked to report their regret of omission and commission regarding neoadjuvant chemotherapy on a scale between 0 (no regret) and 100 (maximum regret). The threshold model and a multilevel mixed regression were applied to analyze respondents' attitudes toward neoadjuvant therapy. Results: The lowest regret of omission was elicited in the low-risk scenario, and the highest regret in the high-risk scenario (P < .001). The regret of the commission was diametrically opposite to the regret of omission (P ≤ .001). The disease-specific threshold mortality at which upfront surgery is favored over the neoadjuvant therapy progressively decreased from the low-risk to the high-risk scenarios (P ≤ .001). The nonsurgeons working in or with lower surgical volume centers (P = .010) and surgeons (P = .018) accepted higher disease-specific mortality after upfront surgery, which resulted in the lower likelihood of adopting neoadjuvant therapy. Conclusion: Regret drives decision making in the management of pancreatic adenocarcinoma. Being a surgeon or a specialist working in surgical centers with lower patient volumes reduces the likelihood of recommending neoadjuvant therapy

Incorporating weekly carboplatin in anthracycline and paclitaxel-containing neoadjuvant chemotherapy for triple-negative breast cancer: propensity-score matching analysis and TIL evaluation

Background The generation of data capturing the risk-benefit ratio of incorporating carboplatin (Cb) to neoadjuvant chemotherapy (NACT) for triple-negative breast cancer (TNBC) in a clinical practice setting is urgently needed. Tumour-infiltrating lymphocytes (TILs) have an established role in TNBC receiving NACT, however, the role of TIL dynamics under NACT exposure in patients receiving the current standard of care is largely uncharted. Methods Consecutive TNBC patients receiving anthracycline-taxane [A-T] +/- Cb NACT at three Institutions were enrolled. Stromal-TILs were evaluated on pre-NACT and residual disease (RD) specimens. In the clinical cohort, propensity-score-matching was used to control selection bias. Results In total, 247 patients were included (A-T = 40.5%, A-TCb = 59.5%). After propensity-score-matching, pCR was significantly higher for A-TCb vs A-T (51.9% vs 34.2%, multivariate: OR = 2.40, P = 0.01). No differences in grade >= 3 haematological toxicities were observed. TILs increased from baseline to RD in the overall population and across A-T/A-TCb subgroups. TIL increase from baseline to RD was positively and independently associated with distant disease-free survival (multivariate: HR = 0.43, P = 0.05). Conclusions We confirmed in a clinical practice setting of TNBC patients receiving A-T NACT that the incorporation of weekly Cb significantly improved pCR. In addition, A-T +/- Cb enhanced immune infiltration from baseline to RD. Finally, we reported a positive independent prognostic role of TIL increase after NACT exposure

The seismic Superattenuators of the Virgo gravitational waves interferometer

The Virgo experiment, located near Pisa, Italy, is a large laser Michelson interferometer aiming at the first direct detection of gravitational waves. The interferometer monitors the relative distance of its mirrors placed at the ends of two 3 km-long perpendicular arms. The goal is to measure spectral differential variations of the arm lengths of 10(-18) m/Hz(1/2) in the frequency range from 10 Hz to 10 kHz. Avoiding spurious motions of the optical components is therefore essential to detect gravitational waves. Since the ground motion is 9 orders of magnitude larger than the arm length variations induced by gravitational waves, the seismic noise is the dominant low frequency noise source for terrestrial gravitational wave interferometers. The seismic isolation is obtained suspending the mirrors by an 8-meter tall chain of cascaded mechanical filters, called "Superattenuator" (SA). The Superattenuator is a passive device acting as a low pass filter in all six degrees of freedom, capable of attenuating the ground motion by more than 10 orders of magnitude, starting from a few Hz. To further reduce the seismic disturbances, the filter chain is suspended from an actively stabilized platform that compensates for low frequency and large amplitude oscillations caused by the mechanical resonances of the chain. In this article we describe the Superattenuator together with its control system, and we report about its performance