Segmenting the retinal layers in optical coherence tomography (OCT) images helps to quantify the layer information in early diagnosis of retinal diseases, which are the main cause of permanent ...blindness. Thus, the segmentation process plays a critical role in preventing vision impairment. However, because there is a lack of practical automated techniques, expert ophthalmologists still have to manually segment the retinal layers. In this paper, we propose an automated segmentation method for OCT images based on a feature-learning regression network without human bias. The proposed deep neural network regression takes the intensity, gradient, and adaptive normalized intensity score (ANIS) of an image segment as features for learning, and then predicts the corresponding retinal boundary pixel. Reformulating the segmentation as a regression problem obviates the need for a huge dataset and reduces the complexity significantly, as shown in the analysis of computational complexity given here. In addition, assisted by ANIS, the method operates robustly on OCT images containing intensity variances, low-contrast regions, speckle noise, and blood vessels, yet remains accurate and time-efficient. In the evaluation of the method conducted using 114 images, the processing time was approximately 10.596 s per image for identifying eight boundaries, and the training phase for each boundary line took only 30 s. Further, the Dice similarity coefficient used for assessing accuracy gave a computed value of approximately 0.966. The absolute pixel distance of manual and automatic segmentation using the proposed scheme was 0.612, which is less than a one-pixel difference, on average.
Adjuvant chemotherapy after surgery improves survival of patients with stage II–III, resectable gastric cancer. However, the overall survival benefit observed after adjuvant chemotherapy is moderate, ...suggesting that not all patients with resectable gastric cancer treated with adjuvant chemotherapy benefit from it. We aimed to develop and validate a predictive test for adjuvant chemotherapy response in patients with resectable, stage II–III gastric cancer.
In this multi-cohort, retrospective study, we developed through a multi-step strategy a predictive test consisting of two rule-based classifier algorithms with predictive value for adjuvant chemotherapy response and prognosis. Exploratory bioinformatics analyses identified biologically relevant candidate genes in gastric cancer transcriptome datasets. In the discovery analysis, a four-gene, real-time RT-PCR assay was developed and analytically validated in formalin-fixed, paraffin-embedded (FFPE) tumour tissues from an internal cohort of 307 patients with stage II–III gastric cancer treated at the Yonsei Cancer Center with D2 gastrectomy plus adjuvant fluorouracil-based chemotherapy (n=193) or surgery alone (n=114). The same internal cohort was used to evaluate the prognostic and chemotherapy response predictive value of the single patient classifier genes using associations with 5-year overall survival. The results were validated with a subset (n=625) of FFPE tumour samples from an independent cohort of patients treated in the CLASSIC trial (NCT00411229), who received D2 gastrectomy plus capecitabine and oxaliplatin chemotherapy (n=323) or surgery alone (n=302). The primary endpoint was 5-year overall survival.
We identified four classifier genes related to relevant gastric cancer features (GZMB, WARS, SFRP4, and CDX1) that formed the single patient classifier assay. In the validation cohort, the prognostic single patient classifier (based on the expression of GZMB, WARS, and SFRP4) identified 79 (13%) of 625 patients as low risk, 296 (47%) as intermediate risk, and 250 (40%) as high risk, and 5-year overall survival for these groups was 83·2% (95% CI 75·2–92·0), 74·8% (69·9–80·1), and 66·0% (60·1–72·4), respectively (p=0·012). The predictive single patient classifier (based on the expression of GZMB, WARS, and CDX1) assigned 281 (45%) of 625 patients in the validation cohort to the chemotherapy-benefit group and 344 (55%) to the no-benefit group. In the predicted chemotherapy-benefit group, 5-year overall survival was significantly improved in those patients who had received adjuvant chemotherapy after surgery compared with those who received surgery only (80% 95% CI 73·5–87·1 vs 64·5% 56·8–73·3; univariate hazard ratio 0·47 95% CI 0·30–0·75, p=0·0015), whereas no such improvement in 5-year overall survival was observed in the no-benefit group (72·9% 66·5–79·9 in patients who received chemotherapy plus surgery vs 72·5% 65·8–79·9 in patients who only had surgery; 0·93 0·62–1·38, p=0·71). The predictive single patient classifier groups (chemotherapy benefit vs no-benefit) could predict adjuvant chemotherapy benefit in terms of 5-year overall survival in the validation cohort (pinteraction=0·036 in univariate analysis). Similar results were obtained in the internal evaluation cohort.
The single patient classifiers validated in this study provide clinically important prognostic information independent of standard risk-stratification methods and predicted chemotherapy response after surgery in two independent cohorts of patients with resectable, stage II–III gastric cancer. The single patient classifiers could complement TNM staging to optimise decision making in patients with resectable gastric cancer who are eligible for adjuvant chemotherapy after surgery. Further validation of these results in prospective studies is warranted.
Ministry of ICT and Future Planning; Ministry of Trade, Industry, and Energy; and Ministry of Health and Welfare.
The proliferation of portable and wearable electroencephalography (EEG) devices has encouraged EEG research in various areas. These devices, while convenient, often come with limited computational ...capabilities. However, the challenge of minimizing network complexity for such edge devices was not fully addressed in previous studies. To tackle this, a scalable hybrid network is proposed to classify EEG signals with different demographic factors on edge devices. This model blends a convolutional neural network (CNN) with a self‐attention mechanism in a hybrid block structure. This design alternates between CNN layers and self‐attention layers to efficiently capture both local and global features. In this study, EEG signals acquired using a portable EEG device during gaming session is classified particularly into pre‐puberty and puberty stages. The developed scalable hybrid network (SH‐Net) has shown promising results in distinguishing between pre‐puberty and puberty EEG signals. As a result, the first stage of this model showed higher accuracy compared to other models in 10‐fold cross‐validation: 93.57% for four channels, 89.04% for the frontal lobe channels (AF), and 81.46% for the temporal lobe channels (TP). Notably, the third stage of this model, using the AF channel, achieved higher accuracy compared to other evaluated models that utilized four channels.
‐ This study develops, a scalable hybrid network that integrates both convolutional neural network (CNN) and self‐attention mechanisms for demographic classification based on electroencephalography signals.
‐ This model shows better accuracy with the frontal lobe channels (AF) alone in comparison to other previous models with all four channels.
‐ It is expected that this will contribute to simplifying medical devices and reducing costs, as effective analysis can be achieved with fewer channels.
Ocular axial length (AL) is an important property of eyes used for determining their health prior to surgery. Estimation of AL is also crucial while making artificial lenses to replace impaired ...natural lenses. However, accurate measurement of AL requires a costly and bulky benchtop optical system. The complex structural features of eyes can be captured by fundus images, which can be easily captured nowadays with portable cameras. Here, we suggest a deep learning method for predicting AL based on fundus images with evidence of decision. This visual interpretation of predictions is achieved by post-processing, separated from the training process, to ensure that the architecture can be freely designed. Through the visualization technique, discriminative regions on input images can be localized to demonstrate specific areas of interest for predictions. In the experiments, we found a significant relationship between the fundus images and AL with achieving a coefficient of determination (R 2 ) of 0.67 and accuracy of 90%, within an error margin of ± mm. Furthermore, visual evidence proves that the network uses consistent regions for predicting AL. The visual results of this study also point to a link between AL and biological structure of eyes, which paves the way for future research.
•Ni-doped branched ZnO nanowires were grown using three-step vapor phase reaction.•Gas response was significantly enhanced by the formation of Ni-doped ZnO branches.•Doped Ni acted as catalyst for ...the selective detection of p-xylene.•A novel method to grow catalyst-doped hierarchical ZnO nanowires is suggested.
Branched ZnO nanowires (NWs) doped with Ni were grown by a three-step vapor phase method for the sensitive and selective detection of p-xylene. ZnO NWs were directly grown on sensor substrates with Au electrodes, which were transformed into NiO NWs by the thermal evaporation of NiCl2 powder at 700°C. ZnO branches doped with Ni were grown from NiO NWs by the thermal evaporation of Zn metal powder at 500°C. The stem NiO NWs played the role of catalyst for the growth of ZnO branches through vapor–liquid–solid mechanism. The Ni-doped branched ZnO NWs showed enhanced gas response (S=resistance ratio) to methyl benzenes, especially to 5ppm p-xylene (S=42.44) at 400°C. This value is 1.7 and 2.5 times higher than the responses to 5ppm toluene (S=25.73) and C2H5OH (S=16.72), respectively, and significantly higher than the cross-responses to other interfering gases such as benzene, HCHO, trimethylamine, H2, and CO. The selective detection of xylene was attributed to the catalytic role of the Ni component. This novel method to form catalyst-doped hierarchical ZnO NWs provides a promising approach to accomplish superior gas sensing characteristics by the synergetic combination of enhanced chemiresistive variation due to the increased number of branch-to-branch Schottky barrier contacts and the catalytic function of the Ni dopant.
We recently demonstrated that activation of spinal sigma-1 receptors (Sig-1Rs) induces pain hypersensitivity via the activation of neuronal nitric oxide synthase (nNOS) and nicotinamide adenine ...dinucleotide phosphate (NADPH) oxidase 2 (Nox2). However, the potential direct interaction between nNOS-derived nitric oxide (NO) and Nox2-derived reactive oxygen species (ROS) is poorly understood, particularly with respect to the potentiation of N-methyl-D-aspartate (NMDA) receptor activity in the spinal cord associated with the development of central sensitization. Thus, the main purpose of this study was to investigate whether Sig-1R-induced and nNOS-derived NO modulates spinal Nox2 activation leading to an increase in ROS production and ultimately to the potentiation of NMDA receptor activity and pain hypersensitivity. Intrathecal pretreatment with the nNOS inhibitor, 7-nitroindazole or with the Nox inhibitor, apocynin significantly inhibited the mechanical and thermal hypersensitivity induced by intrathecal administration of the Sig-1R agonist, 2-(4-morpholinethyl) 1-phenylcyclohexanecarboxylate hydrochloride (PRE084). Conversely, pretreatment with 5,10,15,20-tetrakis-(4-sulphonatophenyl)-porphyrinato iron(III) (FeTPPS; a scavenger of peroxynitrite, a toxic reaction product of NO and superoxide) had no effect on the PRE084-induced pain hypersensitivity. Pretreatment with 7-nitroindazole significantly reduced the PRE084-induced increase in Nox2 activity and concomitant ROS production in the lumbar spinal cord dorsal horn, whereas apocynin did not alter the PRE084-induced changes in nNOS phosphorylation. On the other hand pretreatment with apocynin suppressed the PRE084-induced increase in the protein kinase C (PKC)-dependent phosphorylation of NMDA receptor GluN1 subunit (pGluN1) at Ser896 site in the dorsal horn. These findings demonstrate that spinal Sig-1R-induced pain hypersensitivity is mediated by nNOS activation, which leads to an increase in Nox2 activity ultimately resulting in a ROS-induced increase in PKC-dependent pGluN1 expression.
Excavating the molecular details of many diverse enzymes from metagenomes remains challenging in agriculture, food, health, and environmental fields. We present a versatile method that accelerates ...metabolic enzyme discovery for highly selective gene capture in metagenomes using next‐generation sequencing. Culture‐independent enzyme mining of environmental DNA is based on a set of short identifying degenerate sequences specific for a wide range of enzyme superfamilies, followed by multiplexed DNA barcode sequencing. A strategy of ‘focused identification of next‐generation sequencing‐based definitive enzyme research’ enabled us to generate targeted enzyme datasets from metagenomes, resulting in minimal hands‐on obtention of high‐throughput biological diversity and potential function profiles, without being time‐consuming. This method also provided a targeted inventory of predicted proteins and molecular features of metabolic activities from several metagenomic samples. We suggest that the efficiency and sensitivity of this method will accelerate the decryption of microbial diversity and the signature of proteins and their metabolism from environmental samples.
Focused identification of next‐generation sequencing‐based definitive enzyme research (FINDER), as a culture‐independent high‐throughput enzyme screening platform technology, enabled us to quickly determine the value of metagenomic samples and to collect a large amount of information regarding useful enzyme profiles. FINDER could also allow us to rapidly characterize environmental microbiota at any given time, and yield established catalogs of functional genes in situ. This large‐scale screening method is a versatile approach for identifying novel biocatalysts and excavating natural deposits of novel enzymes at a metagenomic scale.
Testing process in industrial profiling depends on the characterization of three-dimensional (3-D) objects with high sensitivity in spatial and temporal domains. Ordinary 3-D measurement instruments ...scan the image area in the temporal domain; therefore, these techniques experience low temporal stability especially for industrial and biomedical sensing. We propose a novel scan-free extended image instrument for sensing the area of 3-D microscopic objects using an interferometric technique with fixed optical parameters, such as resolution, and without mechanical movement. The technique could accelerate the control process in industrial fault detection and images of biological samples could be obtained in a shorter time. First, a stable system for doubling the image area is introduced. Second, the principles underlying the two-dimensional sampling scheme are introduced to record the maximum image area using a dual multiplexing technique at subsampling frequency. Moreover, a standard factor is presented as a figure of merit to determine the exact image area enhancement. Finally, the feasibility of this technique was demonstrated by sensing reflective and transparent objects with image area of up to 4.3-times that of a single-hologram recording using the square scheme. Furthermore, scan-free monitoring of the photolithography process was demonstrated in real-time. The standard deviation of thickness is 0.48 nm, which demonstrates the subnanometer temporal sensitivity of this technique.
Image area for multiple-color three-dimensional (3-D) off-axis interferometry is extremely restricted because of the Nyquist sampling rate, autocorrelation term, and twin cross-correlation term in ...the frequency domain for each wavelength. Furthermore, the image area is more restricted in dual-wavelength diffraction phase microscopy, which is an important tool for 3-D biological imaging with subnanometer sensitivity. The reason for this extra restriction is the use of only one pinhole for generating two uniform reference beams, which is not sufficient for imaging large areas. Here, we developed large field-of-view double-pinhole dual-wavelength diffraction phase microscopy as a novel approach to capture maximum possible information using two arbitrary wavelengths in the off-axis arrangement. The rules to optimize the two-dimensional sampling scheme without any crosstalk for two arbitrary wavelengths are theoretically presented. We demonstrate that the loss in the image area of the dual-wavelength holographic system designed with this approach is limited to 0-11% of the maximum possible image area using single-wavelength off-axis interferometry. Total amount of information is more than 170 times that of previously reported dual-wavelength diffraction phase microscopy employing single grating, single pinhole, and no sampling scheme optimization. Feasibility of the technique with sub-nanometer sensitivity is demonstrated by measuring optical thickness of polystyrene microspheres.
A novel blue thermally activated delayed fluorescence (TADF) organic light-emitting diode with an emitting layer made up of a TADF assistant dopant and a pure blue-emitting TADF emitter was developed ...to demonstrate a pure blue color, a high external quantum efficiency, suppressed efficiency roll-off, and an improved lifetime. Two fused B–N type blue TADF emitters with a narrow emission spectrum were used as the blue emitters and the narrow blue emission was harvested by a TADF assistant dopant through a reverse intersystem crossing mediated cascade energy transfer process. A high external quantum efficiency of 31.4%, pure blue emission color of (0.13, 0.15), significant reduction of the efficiency roll-off and more than 10 times lifetime extension were simultaneously achieved using the TADF assisted TADF process.
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