Hot Carrier Transport and Photocurrent Response in Graphene

Strong electron-electron interactions in graphene are expected to result in multiple-excitation generation by the absorption of a single photon. We show that the impact of carrier multiplication on photocurrent response is enhanced by very inefficient electron cooling, resulting in an abundance of hot carriers. The hot-carrier-mediated energy transport dominates the photoresponse and manifests itself in quantum efficiencies that can exceed unity, as well as in a characteristic dependence of the photocurrent on gate voltages. The pattern of multiple photocurrent sign changes as a function of gate voltage provides a fingerprint of hot-carrier-dominated transport and carrier multiplication.Comment: 4 pgs, 2 fg

The XMM Cluster Survey: Active Galactic Nuclei and Starburst Galaxies in XMMXCS J2215.9-1738 at z=1.46

We use Chandra X-ray and Spitzer infrared observations to explore the AGN and starburst populations of XMMXCS J2215.9-1738 at z=1.46, one of the most distant spectroscopically confirmed galaxy clusters known. The high resolution X-ray imaging reveals that the cluster emission is contaminated by point sources that were not resolved in XMM observations of the system, and have the effect of hardening the spectrum, leading to the previously reported temperature for this system being overestimated. From a joint spectroscopic analysis of the Chandra and XMM data, the cluster is found to have temperature T=4.1_-0.9^+0.6 keV and luminosity L_X=(2.92_-0.35^+0.24)x10^44 erg/s extrapolated to a radius of 2 Mpc. As a result of this revised analysis, the cluster is found to lie on the sigma_v-T relation, but the cluster remains less luminous than would be expected from self-similar evolution of the local L_X-T relation. Two of the newly discovered X-ray AGN are cluster members, while a third object, which is also a prominent 24 micron source, is found to have properties consistent with it being a high redshift, highly obscured object in the background. We find a total of eight >5 sigma 24 micron sources associated with cluster members (four spectroscopically confirmed, and four selected using photometric redshifts), and one additional 24 micron source with two possible optical/near-IR counterparts that may be associated with the cluster. Examining the IRAC colors of these sources, we find one object is likely to be an AGN. Assuming that the other 24 micron sources are powered by star formation, their infrared luminosities imply star formation rates ~100 M_sun/yr. We find that three of these sources are located at projected distances of <250 kpc from the cluster center, suggesting that a large amount of star formation may be taking place in the cluster core, in contrast to clusters at low redshift.Comment: Accepted for publication in ApJ, 16 pages, 10 figure

Comparison of ADC metrics and their association with outcome for patients with newly diagnosed glioblastoma being treated with radiation therapy, temozolomide, erlotinib and bevacizumab

To evaluate metrics that describe changes in apparent diffusion coefficient (ADC) and to examine their association with clinical outcome for patients with newly diagnosed GBM who were participating in a Phase II clinical trial of treatment with radiation (RT), temozolomide, erlatonib and bevacizumab. Thirty six patients were imaged after surgery but prior to therapy and at regular follow-up time points. The following ADC metrics were evaluated: (1) histogram percentiles within the T2-hyperintense lesion (T2L) at serial follow-ups; (2) parameters obtained by fitting a two-mixture normal distribution to the histogram within the contrast-enhancing lesion (CEL) at baseline; (3) parameters obtained using both traditional and graded functional diffusion maps within the CEL and T2L. Cox Proportional Hazards models were employed to assess the association of the ADC parameters with overall survival (OS) and progression-free survival (PFS). A lower ADC percentile value within the T2L at early follow-up time points was associated with worse outcome. Of particular interest is that, even when adjusting for clinical prognostic factors, the ADC(10%) within the T2L at 2 months was strongly associated with OS (p < 0.001) and PFS (p < 0.007). fDM metrics showed an association with OS and PFS within the CEL when considered by univariate analysis, but not in the T2L. Our study emphasizes the value of ADC metrics obtained from the T2L at the post-RT time point as non-invasive biomarkers for assessing residual tumor in patients with newly diagnosed GBM being treated with combination therapy that includes the anti-angiogenic agent bevacizumab

The incidence of unpleasant dreams after sub-anaesthetic ketamine

Ketamine is an N-methyl-D-aspartate (NMDA)receptor antagonist with psychotogenic effects and for whichthere are diverse reports of whether pleasant or unpleasantdreams result during anaesthesia, post-operatively or aftersub-anaesthetic use. The aim was to assess in healthy volunteers the incidence ofunpleasant dreams over the three nights after receiving asub-anaesthetic dose of ketamine, in comparison to placebo,and with retrospective home nightmare frequency as acovariate.Thirty healthy volunteers completed questionnairesabout retrospective home dream recall and were then giveneither ketamine or placebo. Ketamine resulted in significantly more meandream unpleasantness relative to placebo and caused athreefold increase in the odds ratio for the incidence of anunpleasant dream. The number of dreams reported over thethree nights did not differ between the groups. Theincidence of unpleasant dreams after ketamine use waspredicted by retrospectively assessed nightmare frequencyat home.Ketamine causes unpleasant dreams over thethree post-administration nights. This may be evidence of aresidual psychotogenic effect that is not found on standardself-report symptomatology measures or a result of disturbedsleep electrophysiology. The results have theoretical implications for the relationship between nightmares and schizotypy

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

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