Nanoparticle-induced negative differential resistance and memory effect in polymer bistable light-emitting device

Recently, electrical bistability was demonstrated in polymer thin films incorporated with metal nanoparticles [J. Ouyang, C. W. Chu, C. R. Szmanda, L. P. Ma, and Y. Yang, Nat. Mater. 3, 918 (2004)]. In this letter, we show the evidence that electrons are the dominant charge carriers in these bistable devices. Direct integration of bistable polymer layer with a light-emitting polymer layer shows a unique light-emitting property modulated by the electrical bistability. A unique negative differential resistance induced by the charged gold nanoparticles is observed due to the charge trapping effect from the nanoparticles when interfaced with the light-emitting layer

Three Years after Legalization of Nonprescription Pharmacy Syringe Sales in California: Where Are We Now?

In January 2005, passage of California Senate Bill 1159 enabled California’s county or city governments to establish disease prevention demonstration projects (DPDPs) through which pharmacies could subsequently register to legally sell up to 10 syringes to adults without a prescription. California’s 61 local health jurisdictions (LHJs) were surveyed annually in 2005–2007 to monitor the progress of DPDP implementation and assess program coverage, facilitators, and barriers. Completed surveys were returned by mail, fax, e-mail, phone, or internet. We analyzed 2007 survey data to describe current DPDP status; data from all years were analyzed for trends in approval and implementation status. By 2007, 17 (27.9%) LHJs approved DPDPs, of which 14 (82.4%) had registered 532 (17.8%) of the 2,987 pharmacies in these 14 LHJs. Although only three LHJs added DPDPs since 2006, the number of registered pharmacies increased 102% from 263 previously reported. Among the LHJs without approved DPDPs in 2007, one (2.3%) was in the approval process, seven (16.3%) planned to seek approval, and 35 (81.4%) reported no plans to seek approval. Of 35 LHJs not planning to seek approval, the top four reasons were: limited health department time (40%) or interest (34%), pharmacy disinterest (31%), and law enforcement opposition (26%). Among eight LHJs pursuing approval, the main barriers were “time management” (13%), educating stakeholders (13%), and enlisting pharmacy participation (13%). The17 LHJs with DPDP represent 52% of California’s residents; they included 62% of persons living with HIV and 59% of IDU-related HIV cases, suggesting that many LHJs with significant numbers of HIV cases have approved DPDPs. Outcome studies are needed to determine whether SB 1159 had the desired impact on increasing syringe access and reducing blood-borne viral infection risk among California IDUs

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

Highly efficient 7,8,10-triphenylfluoranthene-doped blue organic light-emitting diodes for display application

We have demonstrated an organic light-emitting diode based on blue-fluorescent dopant 7,8,10-triphenylfluoranthene in a host of dipyrenylfluorene derivatives. The device shows pure blue emission with a peak wavelength of 456 nm and Commission International de L’Eclairage coordinate at (0.164, 0.188). An electroluminescence efficiency as high as 3.33 cd/A and external quantum efficiency of 2.48% can be achieved. Comparison of the photoluminescence and electroluminescence spectra reveals a nearly identical exciton relaxation and efficient energy transfer from the host to the dopant.

Nanofabrication module integrated with optical aligner

In this article, we describe a simple module that can be integrated with a commercial optical aligner for nanoimprint lithography or optical lithography. The module provides a convenient low-cost technique to transform an optical aligner for microfabrication into a nanofabrication machine. This combination enables the creation of nanoscale features and alignment of multiple-layer lithographic patterns with submicron accuracy within one instrument. Imprinting of 30 nm half-pitch lines has been demonstrated by the module, as well as submicron alignment. The module has also been used to fabricate micro- and nanoscale patterns simultaneously by the combination of optical and imprint lithography