Nanophotonics, the field that merges photonics and nanotechnology, has in recent years revolutionized the field of optics by enabling the manipulation of light–matter interactions with subwavelength structures. However, despite the many advances in this field, the design, fabrication and characterization has remained widely an iterative process in which the designer guesses a structure and solves the Maxwell’s equations for it. In contrast, the inverse problem, i.e., obtaining a geometry for a desired electromagnetic response, remains a challenging and time-consuming task within the boundaries of very specific assumptions. Here, we experimentally demonstrate that a novel Deep Neural Network trained with thousands of synthetic experiments is not only able to retrieve subwavelength dimensions from solely far-field measurements but is also capable of directly addressing the inverse problem. Our approach allows the rapid design and characterization of metasurface-based optical elements as well as optimal nanostructures for targeted chemicals and biomolecules, which are critical for sensing, imaging and integrated spectroscopy applications.
ABSTRACTconventional chemotherapy, we conducted a retrospective comparative study assessing the outcome of 473 adult patients with Ph + ALL who, between 2000 and 2010, underwent allo-SCT in CR1 with HLA-identical siblings or matched unrelated donors, with a special emphasis on the impact of TKIs. Methods Study design, data collection and selection criteriaPatients with Ph + ALL reported to the registry of the Acute Leukemia Working Party of the European Group for Blood and Marrow Transplantation (EBMT) were included in this study. For the purpose of this specific analysis, participating centers were requested to enroll consecutive Ph+ALL cases diagnosed between January 2000 and December 2010. The study aimed to include cases of Ph+ B-ALL receiving first allo-SCT from an HLA-identical sibling donor (MSD) or HLA-matched unrelated donor (at least 6/6 HLA matching) (MUD) who: i) were aged 18 years or over at time of transplant; ii) were in CR1; iii) were transplanted between 2000 and 2010; iv) received allogeneic unmanipulated bone marrow (BM) or peripheral blood stem cells (PBSC) as stem cell source; v) received a standard myeloablative conditioning (MAC) regimen or a reduced-intensity conditioning (RIC) regimen according to Bacigalupo's criteria; 28 and vi) whose complete clinical data and outcomes were available. A total of 473 allo-SCT recipients from 77 participating centers met these eligibility criteria. Institutional review board approval was obtained from all participating institutions. Minimal residual disease assessmentInvestigators were asked to provide minimal residual disease (MRD) data at time of transplant. As the most commonly accepted level of sensitivity for a given sample to be considered PCR negative is 10 -4 BCR/ABL copies, we distinguished two groups for the purpose of this analysis: a "low-risk" group with MRD ≤10 -4, and a "high-risk" group with a ratio >10 -4 . 29,30 Statistical analyses and definitionsThe primary end points were leukemia-free survival (LFS), relapse incidence (RI), and non-relapse mortality (NRM). Secondary end points were overall survival (OS), acute graft-versus-host disease (aGvHD) and chronic graft-versus-host-disease (cGvHD).Patient-related, disease-related, and transplant-related variables were compared between the 2 groups receiving or not TKI before transplantation using the χ 2 statistics for categorical variables and the Mann-Whitney test for continuous variables. Factors that differed significantly between the two groups with P values less than 0.05, and all factors associated with a P value less than 0.10 by univariate analysis were included in the final models. Cumulative incidence functions (CIF) were used to estimate RI and NRM in a competing risk setting, because death and relapse compete with each other. To study cGvHD, we considered relapse and death to be competing events. Probabilities of LFS and OS were calculated using Kaplan-Meier estimates. Univariate analyses were performed using Gray's test for CIF and the log rank test for LFS and OS. Association...
Nanoparticles, and more specifically gold nanoparticles (AuNPs), have attracted much scientific and technological interest in the last few decades. Their popularity is attributed to their unique optical, catalytic, electrical and magnetic properties when compared with the bulk. However, one of the main problems with AuNPs is their long-term stability. Two-dimensional materials like MoS (WS) are semiconductors that exhibit a combination of properties which make them suitable for electronic, optical and (photo)catalytic devices. Few-layer MoS (WS) nanoparticles (NPs), and in particular single-layer ones, show intriguing optical and electrical properties which are very different from those of the bulk compounds. Here we demonstrate the synthesis of AuNPs sheathed by a single layer of MoS (WS), i.e. a core-shell nanostructure (AuNP@1L-MoS). The hybrid NPs exhibit optical properties that are different from those of either constituent and are amenable for modulation via their chemistry, offering a myriad of applications.
These authors contributed equally to this workOur visual perception of our surroundings is ultimately limited by the diffraction-limit, which stipulates that optical information smaller than roughly half the illumination wavelength is not retrievable. Over the past decades, many breakthroughs have led to unprecedented imaging capabilities beyond the diffraction-limit, with applications in biology and nanotechnology. In this context, nano-photonics has revolutionized the field of optics in recent years by enabling the manipulation of light-matter interaction with subwavelength structures (1-3). However, despite the many advances in this field, its impact and penetration in our daily life has been hindered by a convoluted and iterative process, cycling through modeling, nanofabrication and nano-characterization. The fundamental reason is the fact that not only the prediction of the optical response is very time consuming and requires solving Maxwell's equations with dedicated numerical packages (4-6).But, more significantly, the inverse problem, i.e. designing a nanostructure with an on-demand optical response, is currently a prohibitive task even with the most advanced numerical tools due to the high nonlinearity of the problem (7-8). Here, we harness the power of Deep Learning, a new path in modern machine learning, and show its ability to predict the geometry of nanostructures based solely on their far-field response. This approach also addresses in a direct way the currently inaccessible inverse problem breaking the ground for on-demand design of optical response with applications such as sensing, imaging and also for Plasmon's mediated cancer thermotherapy.While computer science has been harnessed to address the diffraction limit in imaging and characterization on one hand (super-resolution techniques such as PALM and STORM techniques and more (9-12)) and to assist with the design process on the other hand (13-19) to date no computational technique is capable of addressing both aspects in an integrated manner.Here, we present an integrated deep learning (DL) approach and show how deep neural networks
and IL2. Using this approach we consistently generated PRAME-CTLs in 12/14 HLA-A*02 healthy donors (526 6 101 SFC/10 5 cells as assessed by IFNg Elispot assay). Similarly, PRAME-CTLs were generated from all 5 CML patients (630 6 120 SFC/ 10 5 cells). These PRAME-CTLs were also able to target autologous tumor blasts (57 6 6 IFNg SFC/10 5 ), demonstrating that the same peptides were presented physiologically. A Cr 51 release assay confirmed that the PRAME-reactive T cells were cytotoxic, lysing autologous-PHA blasts loaded with PRAME-library (63 6 14% at a 20:1 E:T ratio), but not with irrelevant library. Using subpools, we found that the responses of our expanded PRAME-CTLs were polyclonal, since they consistently released IFNg in response to 1 to 6 pentadecapeptides pools. Moreover, this approach has allowed to identify 6 potential new immunogenic 15mer peptides that are processed and presented by tumor cells, and should facilitate expansion of polyclonal PRAME-CTLs for adoptive transfer in patients with PRAME1 malignancy.
We use an arbitrary waveform generator to generate a clean sinusoidal modulation from the otherwise nonlinear acousto-optic modulator (AOM). A closed loop optimization script is applied to reduce high order harmonic distortion to less than 0.05% in a high AOM diffraction efficiency regime. This low level of distortion allows us to measure the nonlinear response to photoexcitation of many materials. We demonstrate this technique in a pump-probe experiment to measure the Nonlinear Photo-Modulated Reflectivity (NPMR) of surfaces. NPMR served us as the basis for developing super-resolution microscopy for non-fluorescence samples (label-free) as well as a tool in studying the ultrafast nonlinear response of photo-excited plasmonic nano-structures. Our methodology could be applied to other imaging systems in which measuring nonlinearity is desirable, such as fluorescence and photoacoustic microscopy.
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