DEEPOMICS FFPE, a deep neural network model, identifies DNA sequencing artifacts from formalin fixed paraffin embedded tissue with high accuracy.

Formalin-fixed, paraffin-embedded (FFPE) tissue specimens are routinely used in pathological diagnosis, but their large number of artifactual mutations complicate the evaluation of companion diagnostics and analysis of next-generation sequencing data. Identification of variants with low allele frequencies is challenging because existing FFPE filtering tools label all low-frequency variants as artifacts. To address this problem, we aimed to develop DEEPOMICS FFPE, an AI model that can classify a true variant from an artifact. Paired whole exome sequencing data from fresh frozen and FFPE samples from 24 tumors were obtained from public sources and used as training and validation sets at a ratio of 7:3. A deep neural network model with three hidden layers was trained with input features using outputs of the MuTect2 caller. Contributing features were identified using the SHapley Additive exPlanations algorithm and optimized based on training results. The performance of the final model (DEEPOMICS FFPE) was compared with those of existing models (MuTect filter, FFPolish, and SOBDetector) by using well-defined test datasets. We found 41 discriminating properties for FFPE artifacts. Optimization of property quantification improved the model performance. DEEPOMICS FFPE removed 99.6% of artifacts while maintaining 87.1% of true variants, with an F1-score of 88.3 in the entire dataset not used for training, which is significantly higher than those of existing tools. Its performance was maintained even for low-allele-fraction variants with a specificity of 0.995, suggesting that it can be used to identify subclonal variants. Different from existing methods, DEEPOMICS FFPE identified most of the sequencing artifacts in the FFPE samples while retaining more of true variants, including those of low allele frequencies. The newly developed tool DEEPOMICS FFPE may be useful in designing capture panels for personalized circulating tumor DNA assay and identifying candidate neoepitopes for personalized vaccine design. DEEPOMICS FFPE is freely available on the web ( http://deepomics.co.kr/ffpe ) for research.

Siloxane Hybrid Material-Encapsulated Highly Robust Flexible µLEDs for Biocompatible Lighting Applications.

Flexible micro-light-emitting diodes (f-µLEDs) have been regarded as an attractive light source for the next-generation human-machine interfaces, thanks to their noticeable optoelectronic performances. However, when it comes to their practical utilizations fulfilling industrial standards, there have been unsolved reliability and durability issues of the f-µLEDs, despite previous developments in the high-performance f-µLEDs for various applications. Herein, highly robust flexible µLEDs (f-HµLEDs) with 20 × 20 arrays, which are realized by a siloxane-based organic-inorganic hybrid material (SHM), are reported. The f-HµLEDs are created by combining the f-µLED fabrication process with SHM synthesis procedures (i.e., sol-gel reaction and successive photocuring). The outstanding mechanical, thermal, and environmental stabilities of our f-HµLEDs are confirmed by a host of experimental and theoretical examinations, including a bending fatigue test (105 bending/unbending cycles), a lifetime accelerated stress test (85 °C and 85% relative humidity), and finite element method simulations. Eventually, to demonstrate the potential of our f-HµLEDs for practical applications of flexible displays and/or biomedical devices, their white light emission due to quantum dot-based color conversion of blue light emitted by GaN-based f-HµLEDs is demonstrated, and the biocompatibility of our f-HµLEDs is confirmed via cytotoxicity and cell proliferation tests with muscle, bone, and neuron cell lines. As far as we can tell, this work is the first demonstration of the flexible µLED encapsulation platform based on the SHM, which proved its mechanical, thermal, and environmental stabilities and biocompatibility, enabling us to envisage biomedical and/or flexible display applications using our f-HµLEDs.

Patterns of viral clearance in the natural course of asymptomatic COVID-19: Comparison with symptomatic non-severe COVID-19.

OBJECTIVES: The aim of this study was to elucidate patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clearance in the natural course of asymptomatic coronavirus disease 2019 (COVID-19). METHODS: Consecutive patients with non-severe COVID-19 were included retrospectively. Asymptomatic patients with a normal body temperature and no evidence of pneumonia throughout the disease course were assigned to the asymptomatic group. The reverse transcription PCR (RT-PCR) assay was repeated every two to five days after the first follow-up RT-PCR assay. Negative conversion was defined as two consecutive negative RT-PCR assay results within a 24-h interval. Rebound of the cycle threshold (Ct) value was defined as negative from the single RT-PCR assay and positive from the following assay. RESULTS: Among a total of 396 patients identified (median age 42.5 years (interquartile range (IQR) 25.0-55.0 years), 35.6% male), 68 (17.2%) were assigned to the asymptomatic group and 328 (82.8%) to the symptomatic group. The time until negative conversion was significantly shorter in the asymptomatic group than in the symptomatic group: median 14.5 days (IQR 11.0-21.0 days) and 18.0 days (IQR 15.0-22.0 days), respectively (p = 0.001). Rebound of Ct values was observed in 78 patients (19.7%). CONCLUSIONS: Time until negative conversion is shorter in asymptomatic COVID-19 than in symptomatic COVID-19. Rebound of Ct values is not uncommon.

An optimal network screening method of hotspot identification for highway crashes with dynamic site length.

We propose a novel network screening method for hotspot (i.e., sites that suffer from high collision concentration and have high potential for safety improvement) identification based on the optimization framework to maximize the total summation of a selected safety measure for all hotspots considering a resource constraint for conducting detailed engineering studies (DES). The proposed method allows the length of each hotspot to be determined dynamically based on constraints the users impose. The calculation of the Dynamic Site Length (DSL) method is based on Dynamic Programming, and it is shown to be effective to find the close-to-optimal solution with computationally feasible complexity. The screening method has been demonstrated using historical crash data from extended freeway routes in San Francisco, California. Using the Empirical Bayesian (EB) estimate as a safety measure, we compare the performance of the proposed DSL method with other conventional screening methods, Sliding Window (SW) and Continuous Risk Profile (CRP), in terms of their optimal objective value (i.e., performance of detecting sites under the highest risk). Moreover, their spatio-temporal consistency is compared through the site and method consistency tests. Findings show that DSL can outperform SW and CRP in investigating more hotspots under the same amount of resources allocated to DES by pinpointing hotspot locations with greater accuracy and showing improved spatio-temporal consistency.

Photo-Patternable Quantum Dots/Siloxane Composite with Long-Term Stability for Quantum Dot Color Filters.

Incorporation of quantum dots (QDs) into color filters (CFs) are desired for less energy loss and wider viewing angle compared to a conventional display. However, aggregation and vulnerability to heat, moisture, and chemicals in the photo-patternable matrix are critical issues of the QD-CFs with high QDs concentration. Herein, we fabricated red (10 wt %) and green (20 wt %) QD-CFs using photolithography of QD/siloxane ink containing secondary thiol monomer. Ligand-exchanged QDs were chemically incorporated in methacrylate oligosiloxane resin. QD/siloxane composite showed superior stability under harsh heat and moisture (85 °C/5% RH and 85 °C/85% RH) conditions and chemicals (EtOH, HCl, and NaOH) compared to conventional QD/PR (commercial negative photoresist). QD-CFs (10 µm thick) effectively converted blue light emitted from LED chip into red and green light, and the obtained white PL through QD-CF showed wide color gamut, which was 108% relative to NTSC. From these advantages, QD/siloxane composite will be beneficial as color-conversion photoresists are to be used as color filters in liquid crystal displays, micro light-emitting diodes, and organic light-emitting diodes.

Exceptionally stable quantum dot/siloxane hybrid encapsulation material for white light-emitting diodes with a wide color gamut.

Herein, we report a luminescent light-emitting diode (LED) encapsulating material using a thermally curable quantum dot (QD)/siloxane hybrid (TSE-QD) color converter, which has superior long-term stability even at elevated temperatures, in high humidity, and in various chemicals. The TSE-QD is cured by a thermal-induced hydrosilylation reaction of an in situ sol-gel synthesized QD dispersed siloxane resin (QD/siloxane resin) without additional ligand-exchange processes. QDs are successfully encapsulated by highly condensed and linear structured siloxane networks with additional chemical linkages between the surface ligands of the QDs and organic functional groups of the siloxane matrix. Moreover, QDs are uniformly distributed within the siloxane matrix retaining their optical properties during the fabrication processes of the TSE-QD. The result is that the stability, as evaluated by the photoluminescence (PL) quantum yield (QY), is greatly improved under harsh conditions, for example, 120 °C/5% relative humidity (RH), ethanol and acetone for 30 days. Based on the exceptionally stable TSE-QD, we demonstrate a white LED using a blue LED chip directly encapsulated by a yellow emitting TSE-QD that shows excellent spectral stability, outstanding reliability at 85 °C/85% RH and a wide color gamut (116% of NTSC).

Two-Step-Enhanced Stability of Quantum Dots via Silica and Siloxane Encapsulation for the Long-Term Operation of Light-Emitting Diodes.

Despite innovative optical properties of quantum dots (QDs) for QDs-converted light-emitting diodes (QD-LEDs), the vulnerability of the QDs, against heat and moisture, has been a critical issue for commercialization and long-term use. To overcome the instabilities, we fabricated a thermally and photostable QDs-embedded silica/siloxane (S-QD/siloxane) film by embedding QDs in silica and siloxane encapsulation through a two-step sol-gel reaction. S-QDs were stably dispersed in the oligo-siloxane resin with even a QD concentration of 5 wt % without aggregation. The two-step physical barriers of silica and siloxane acted to decrease the toxicity of QDs and improve the stability against heat and moisture [85 °C/5% relative humidity (RH), 85 °C/85% RH, and 120 °C/5% RH], light (50 and 100 mA), and chemicals (ethanol, HCl, and NaOH). Our S-QD/siloxane film was applied as a color-conversion material on a blue LED chip without additional solidification and encapsulation processes for red and white QD-LEDs, exhibiting a wider color gamut (107% in CIE 1931) compared to NTSC. These enhancements indicate that our S-QD/siloxane film is a suitable material for long-term operation of QD-enhanced films and QD-LEDs in next-generation displays.

Evaluating and addressing the effects of regression to the mean phenomenon in estimating collision frequencies on urban high collision concentration locations.

Two different methods for addressing the regression to the mean phenomenon (RTM) were evaluated using empirical data: Data from 110 miles of freeway located in California were used to evaluate the performance of the EB and CRP methods in addressing RTM. CRP outperformed the EB method in estimating collision frequencies in selected high collision concentration locations (HCCLs). Findings indicate that the performance of the EB method can be markedly affected when SPF is biased, while the performance of CRP remains much less affected. The CRP method was more effective in addressing RTM.

Impact of traffic states on freeway crash involvement rates.

Freeway traffic accidents are complicated events that are influenced by multiple factors including roadway geometry, drivers' behavior, traffic conditions and environmental factors. Among the various factors, crash occurrence on freeways is supposed to be strongly influenced by the traffic states representing driving situations that are changed by road geometry and cause the change of drivers' behavior. This paper proposes a methodology to investigate the relationship between traffic states and crash involvements on the freeway. First, we defined section-based traffic states: free flow (FF), back of queue (BQ), bottleneck front (BN) and congestion (CT) according to their distinctive patterns; and traffic states of each freeway section are determined based on actual measurements of traffic data from upstream and downstream ends of the section. Next, freeway crash data are integrated with the traffic states of a freeway section using upstream and downstream traffic measurements. As an illustrative study to show the applicability, we applied the proposed method on a 32-mile section of I-880 freeway. By integrating freeway crash occurrence and traffic data over a three-year period, we obtained the crash involvement rate for each traffic state. The results show that crash involvement rate in BN, BQ, and CT states are approximately 5 times higher than the one in FF. The proposed method shows promise to be used for various safety performance measurement including hot spot identification and prediction of the number of crash involvements on freeway sections.