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Mechanical Force Enables an Anomalous Dual Ring-Opening Reaction of Naphthodipyran
No full textMultimodal mechanophores that exhibit complex mechanochromic behavior beyond the typical binary response are capable of distinguishing between multiple stress states through discrete changes in color. Naphthodipyran photoswitches contain two pyran rings fused to a central naphthalene core and represent a potentially promising framework for multimodal reactivity. However, the concurrent ring opening of both pyran moieties has previously proven inaccessible via photochemical activation. Here, we demonstrate that mechanical force supplied to naphthodipyran through covalently linked polymer chains generates the elusive dual ring-opened dimerocyanine product with unique near-infrared absorption properties. Trapping with boron trifluoride renders the merocyanine dyes thermally persistent and reveals apparent sequential ring-opening behavior that departs from the reactivity of previously studied mechanophores under the high strain rates imposed by ultrasound-induced solvodynamic chain extension
A fibrous neuromorphic device for multi-level nerve pathways implementing knee jerk reflex and cognitive activities
No full textHere, we present fibrous neuromorphic devices (FNDs) that serves as multi-level nerve pathways to implement a biomimetic knee-jerk reflex and cognitive activities. By the tunable charge-carrier polarity of the fibrous electrolyte, FNDs successfully simulate the competition between glutamate and γ-aminobutyric acid (GABA) in a multiplexed transmission process in the human nervous system. To emulate action signals that respond to environmental stimuli in a low-level nerve pathway, a fiber-level neurologically integrated muscular system was constructed by cascading with FNDs and artificial muscle fibers; the system realized unconditioned reflex, even under loads of several Newtons. To emulate the high-level nerve pathway, multiple conductive states of FNDs were used to construct flexible neuromorphic networks; the recognition accuracy for the Fashion MNIST dataset was > 83%, with < 0.1% loss of accuracy even after 100 bending cycles, which represents the most stable recognition result for flexible neuromorphic electronics so far. The presented FNDs provide an excellent basis for the development of human-compatible artificial neurological systems
Mapping the circumnuclear regions of the Circinus galaxy with the Imaging X-ray Polarimetry Explorer
No full textWe report on the Imaging X-ray Polarimetry Explorer (IXPE) observation of the closest and X-ray brightest Compton-thick active galactic nucleus (AGN), the Circinus galaxy. We find the source to be significantly polarized in the 2–6 keV band. From previous studies, the X-ray spectrum is known to be dominated by reflection components, both neutral (torus) and ionized (ionization cones). Our analysis indicates that the polarization degree is 28 ± 7 per cent (at 68 per cent confidence level) for the neutral reflector, with a polarization angle of 18° ± 5°, roughly perpendicular to the radio jet. The polarization of the ionized reflection is unconstrained. A comparison with Monte Carlo simulations of the polarization expected from the torus shows that the neutral reflector is consistent with being an equatorial torus with a half-opening angle of 45°–55°. This is the first X-ray polarization detection in a Seyfert galaxy, demonstrating the power of X-ray polarimetry in probing the geometry of the circumnuclear regions of AGNs, and confirming the basic predictions of standard Unification Models
Surrogate model for gravitational wave signals from nonspinning, comparable-to large-mass-ratio black hole binaries built on black hole perturbation theory waveforms calibrated to numerical relativity
No full textWe present a reduced-order surrogate model of gravitational waveforms from nonspinning binary black hole systems with comparable to large mass-ratio configurations. This surrogate model, BHPTNRSur1dq1e4, is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT) with mass ratios varying from 2.5 to 10,000. BHPTNRSur1dq1e4 extends an earlier waveform model, EMRISur1dq1e4, by using an updated transition-to-plunge model, covering longer durations up to 30,500 m₁ (where
m₁ is the mass of the primary black hole), includes several more spherical harmonic modes up to ℓ = 10 , and calibrates subdominant modes to numerical relativity (NR) data. In the comparable mass-ratio regime, including mass ratios as low as 2.5, the gravitational waveforms generated through ppBHPT agree surprisingly well with those from NR after this simple calibration step. We also compare our model to recent SXS and RIT NR simulations at mass ratios ranging from 15 to 32, and find the dominant quadrupolar modes agree to better than ≈ 10⁻³. We expect our model to be useful to study intermediate-mass-ratio binary systems in current and future gravitational-wave detectors
One-stop function for gravitational-wave detection, identification, and inference
No full textI define here a novel function on a modeled space of gravitational-wave signals, before studying its properties as a statistic for detection, as an objective function for identification, and as an effective likelihood function for inference. The main motivation behind this work is the open data-analysis problem for signals from extreme-mass-ratio inspirals, which is severely hindered by the presence of strong nonlocal parameter degeneracy in the signal space. I demonstrate the utility of the proposed function for the analysis of such signals, and suggest various possible directions for future research
The apparent prevalence of outcome variation from hidden "dark methods" is a challenge for social science
No full textEvery working scientist knows that in the details are both devils and angels. Lots of small design decisions have to be made in collecting and analyzing data, and those decisions affect conclusions. But beginning scientists, from rookies in school science fairs to students in early years of a rigorous Ph.D. program, are often surprised how much small decisions matter. Despite this recognition that details matter, when science is communicated, many small decisions made privately by a science team are hidden from view. It is difficult to disclose every detail (and usually little disclosure is required). Such hidden decisions can be thought of as “dark methods,” like dark matter which cannot be directly seen because it does not reflect light, but which is evident from its other effects. The Herculean effort resulting in the new many-analyst study (1) which is the subject of my Commentary should force a painful reckoning about the extent of these dark method choices and their influence on conclusions. Design decisions of each team that were coded (107 of them) explained at most 10 to 20% of the outcome variance. Assuming that the coding itself is not too noisy, it seems that hidden decisions account for the lion’s share of what different teams conclude
1478 A phase I study of personalized adoptive TCR T cell therapy in patients with solid tumors: safety, efficacy, and T cell trafficking to tumors of non-virally gene edited T cells
No full textBackground: NeoTCR-P1 is a personalized autologous T cell therapy for treatment of patients with solid tumors. Neoantigen-specific T cell receptors (neoTCRs) were isolated from the patients’ own circulating CD8 T cells using the imPACT Isolation Technology®, followed by non-viral precision genome engineering into an autologous apheresis product for infusion back into the patient.
Methods: This phase 1 trial is a first-in-human, multi-center, dose-escalation study to evaluate the safety, tolerability, and manufacturing feasibility of NeoTCR-P1 alone or in combination with IL-2 in solid tumors.
Patients with TCRs identified at screening and meeting eligibility criteria underwent apheresis to manufacture personalized NeoTCR-P1 cell product. Lymphodepleted patients received a single dose of up-to-three distinct NeoTCR cell products at dose levels of 0.4, 1.2, or 4×10⁹ NeoTCR-edited T cells.
Pre- and post-treatment blood and biopsy samples were collected to evaluate NeoTCR-P1 pharmacokinetics, tumor trafficking, signs of T cell engagement or potential mechanisms of resistance.
Results: Sixteen patients were infused with NeoTCR-P1 T cells including patients with MSS-colorectal cancer (11), breast cancer (2), ovarian cancer (1), melanoma (1), or non-small cell lung cancer (1). Four of the sixteen patients were treated with NeoTCR-P1 + IL-2.
Two patients experienced toxicities associated with NeoTCR-P1 cell infusions: a grade 1 CRS and a grade 2 ICANS. Five patients had stable disease as their best response at their first tumor assessment (day 28).
NeoTCR+ T cells detected in the peripheral blood had an average peak of 3.6% (range 0.9-7.3%) for DL1, 11.7% (7.7-20.8%) for DL2, and 19.8% (12.0-37.3%) for DL3. Increases in NeoTCR T cells were observed at higher dose levels, stronger lymphodepletion, or higher gene editing rates of the infused product.
Eight post-infusion biopsies were available for sequencing and imaging analysis; 17 of 22 neoTCR-T cells were detected in post-infusion biopsies with 12 neoTCRs among the top 4% of CDR3 sequences detected. The targeted neoantigens were detected in 7 of 8 post-treatment biopsies (15 of 22 targets), and personalized ctDNA confirmed targeting of a predicted sub-clonal mutation. An APOBEC signature and HLA-LOH were identified as potential mechanisms of resistance. By single-cell, spatial molecular imaging, neoTCR-T cells were visualized in post-treatment biopsies and found to differentially express potential markers of engagement.
Conclusions: This study demonstrates the feasibility of isolating and manufacturing NeoTCR-T cells using non-viral precision genome engineering, the safety of infusing up-to-three gene edited NeoTCR-T cell products, and T cell persistence and trafficking to a variety of solid tumors
Strong kinetic-inductance Kerr nonlinearity with titanium nitride nanowires
No full textThin films of disordered superconductors such as titanium nitride (TiN) exhibit large kinetic inductance (KI), high critical temperature, and large quality factors at the single-photon level. KI nonlinearity can be exploited as an alternative to Josephson junctions for creating novel nonlinear quantum devices with the potential to operate at higher frequencies and at elevated temperatures. We study a means of magnifying KI nonlinearity by confining the current density of resonant electromagnetic modes in nanowires with a small volume V ≃ 10⁻⁴ um³. Using this concept, we realize microwave-frequency Kerr cavities with a maximum Kerr-shift per photon of K/2π = 123.5 ± 3 kHz and report a nonlinearity-to-linewidth ratio K/γ = 21%. With improved design, our devices are expected to approach the regime of strong quantum nonlinearity in the millimeter-wave spectrum
Stochastic Geometry to Generalize the Mondrian Process
No full textThe stable under iteration (STIT) tessellation process is a stochastic process that produces a recursive partition of space with cut directions drawn independently from a distribution over the sphere. The case of random axis-aligned cuts is known as the Mondrian process. Random forests and Laplace kernel approximations built from the Mondrian process have led to efficient online learning methods and Bayesian optimization. In this work, we utilize tools from stochastic geometry to resolve some fundamental questions concerning STIT processes in machine learning. First, we show that STIT processes can be efficiently simulated by lifting to a higher-dimensional axis-aligned Mondrian process. Second, we characterize all possible kernels that STIT processes and their mixtures can approximate. We also give a uniform convergence rate for the approximation error of the STIT kernels to the targeted kernels, completely generalizing the work of Balog et al. [The Mondrian kernel, 2016] from the Mondrian case. Third, we obtain consistency results for STIT forests in density estimation and regression. Finally, we give a precise formula for the density estimator arising from a STIT forest. This allows for precise comparisons between the STIT forest, the STIT kernel, and the targeted kernel in density estimation. Our paper calls for further developments at the novel intersection of stochastic geometry and machine learning
Confinement effects facilitate low-concentration carbon dioxide capture with zeolites
No full textEngineered systems designed to remove CO₂ from the atmosphere need better adsorbents. Here, we report on zeolite-based adsorbents for the capture of low-concentration CO₂. Synthetic zeolites with the mordenite (MOR)-type framework topology physisorb CO₂ from low concentrations with fast kinetics, low heat of adsorption, and high capacity. The MOR-type zeolites can have a CO₂ capacity of up to 1.15 and 1.05 mmol/g for adsorption from 400 ppm CO₂ at 30 °C, measured by volumetric and gravimetric methods, respectively. A structure–performance study demonstrates that Na⁺ cations in the O33 site located in the side-pocket of the MOR-type framework, that is accessed through a ring of eight tetrahedral atoms (either Si⁴⁺ or Al³⁺: eight-membered ring [8MR]), is the primary site for the CO₂ uptake at low concentrations. The presence of N₂ and O₂ shows negligible impact on CO₂ adsorption in MOR-type zeolites, and the capacity increases to ∼2.0 mmol/g at subambient temperatures. By using a series of zeolites with variable topologies, we found the size of the confining pore space to be important for the adsorption of trace CO₂. The results obtained here show that the MOR-type zeolites have a number of desirable features for the capture of CO₂ at low concentrations