Search

Multimodal emotion recognition has emerged as a promising approach to capture the complex nature of human emotions by integrating information from various sources such as physiological signals, visual behavioral cues, and audio-visual content. However, current methods often struggle with effectively processing redundant or conflicting information across modalities and may overlook implicit inter-modal correlations. To address these challenges, this paper presents a novel multimodal emotion recognition framework which integrates audio-visual features with viewers' EEG data to enhance emotion classification accuracy. The proposed approach employs modality-specific encoders to extract spatiotemporal features, which are then aligned through contrastive learning to capture inter-modal relationships. Additionally, cross-modal attention mechanisms are incorporated for effective feature fusion across modalities. The framework, comprising pre-training, fine-tuning, and testing phases, is evaluated on multiple datasets of emotional responses. The experimental results demonstrate that the proposed multimodal approach, which combines audio-visual features with EEG data, is highly effective in recognizing emotions, highlighting its potential for advancing emotion recognition systems. [ABSTRACT FROM AUTHOR]

Object detection in maritime environments is a challenging problem because of the continuously changing background and moving objects resulting in shearing, occlusion, noise, etc. Unluckily, this problem is of critical importance since such failure may result in significant loss of human lives and economic loss. The available object detection methods rely on radar and sonar sensors. Even with the advances in electro-optical sensors, their employment in maritime object detection is rarely considered. The proposed research aims to employ both electro-optical and near-infrared sensors for effective maritime object detection. For this, dedicated deep learning detection models are trained on electro-optical and near-infrared (NIR) sensor datasets. For this, (ResNet-50, ResNet-101, and SSD MobileNet) are utilized in both electro-optical and near-infrared space. Then, dedicated ensemble classifications are constructed on each collection of base learners from electro-optical and near-infrared spaces. After this, decisions about object detection from these spaces are combined using logical-disjunction-based final ensemble classification. This strategy is utilized to reduce false negatives effectively. To evaluate the performance of the proposed methodology, the publicly available standard Singapore Maritime Dataset is used and the results show that the proposed methodology outperforms the contemporary maritime object detection techniques with a significantly improved mean average precision. [ABSTRACT FROM AUTHOR]

The ASEAN Community-based Tourism (CBT) Standard was adopted by 10 ASEAN member states in 2016 in order to collectively promote the ASEAN region as a competitive and sustainable community-based tourism destination and to enhance sustainable tourism provision through ASEAN criteria. Among the ten ASEAN member states, Lao PDR (Laos) has a unique CBT development experience through collaboration with various international development agencies that started in 1999. Such a history brought about issues when the ASEAN CBT Standard was implemented at the provincial and district levels in Laos, including the concerns of related communities on the ASEAN CBT Standard not being easily applicable considering the state of tourism development. While the adoption and application of the regional standard called the ASEAN CBT Standard is a significant collective enforcement involving the tourism industries in ASEAN, there has been a lack of studies regarding the dynamics of the implementation of regional tourism standards at the national level. In order to fill this research gap, this study utilized in-depth interviews with seven executives with significant experiences from the government, international development agency, international organization, and private sectors. Interviewees give a diversity of perspectives on the enabling factors and challenges in implementing the ASEAN CBT Standard at the national level (focusing on Laos), which showed the complexity of CBT dynamics and necessity of good governance. The results also revealed how regional standards can be further recognized, promoted, sustainable to the key stakeholders of the Laos tourism industry, better accepted and utilized by end users, and possibly better customized according to the local circumstances for sustainable tourism development. The findings of this study evidence that collaboration among key stakeholders and the expected roles of these stakeholders are critical when implementing the ASEAN CBT Standard. [ABSTRACT FROM AUTHOR]

In many machine learning applications, there are many scenarios when performance is not satisfactory by single classifiers. In this case, an ensemble classification is constructed using several weak base learners to achieve satisfactory performance. Unluckily, the construction of the ensemble classification is empirical, i.e., to try an ensemble classification and if performance is not satisfactory then discard it. In this paper, a challenging analytical problem of the estimation of ensemble classification using the prediction performance of the base learners is considered. The proposed formulation is aimed at estimating the performance of ensemble classification without physically developing it, and it is derived from the perspective of probability theory by manipulating the decision probabilities of the base learners. For this purpose, the output of a base learner (which is either true positive, true negative, false positive, or false negative) is considered as a random variable. Then, the effects of logical disjunction-based and majority voting-based decision combination strategies are analyzed from the perspective of conditional joint probability. To evaluate the forecasted performance of ensemble classifier by the proposed methodology, publicly available standard datasets have been employed. The results show the effectiveness of the derived formulations to estimate the performance of ensemble classification. In addition to this, the theoretical and experimental results show that the logical disjunction-based decision outperforms majority voting in imbalanced datasets and cost-sensitive scenarios. [ABSTRACT FROM AUTHOR]

During road construction, the accuracy of compaction work is critical for the structural stability and maintenance of the road. Although the plate load test (PLT) is commonly used for quality inspections, it is impractical to test every section due to time and cost constraints. Therefore, simpler testing methods are being extensively developed. This study compared quality inspection results using the commonly used PLT, the relatively simple dynamic cone penetrometer test (DCPT), the lightweight deflectometer (LWD) test, and an intelligent quality control system equipped with accelerometer and global positioning system (GPS) sensors in intelligent compaction (IC) rollers. The results showed a strong correlation between the conventional tests (PLT, DCPT, and LWD) and the values obtained from the intelligent quality control system. The correlation analysis between the intelligent quality control system and PLT, LWDT, and DCPT yielded R-square values of 0.69, 0.91, and 0.95, respectively, indicating significantly high correlations. The implementation of intelligent quality management systems in earthwork construction projects will facilitate a thorough verification of the compaction quality throughout all construction segments, ensuring consistent compaction across the project. By enabling real-time data acquisition and analysis, these systems differ markedly from traditional methods, reducing the frequency and necessity of manual inspections. This approach not only streamlines construction processes, but also enhances operational efficiency. As a result, integrating these intelligent systems is anticipated to significantly increase productivity by optimizing the workflow and resource utilization in earthwork construction. [ABSTRACT FROM AUTHOR]

The development of low-cost and low-power gas sensors for reliable NO2 gas detection is important due to the highly toxic nature of NO2 gas. Herein, initially, SnO2 nanowires (NWs) were synthesized through a simple vapor–liquid–solid growth mechanism. Subsequently, different amounts of SnO2 NWs were composited with MoS2 nanosheets (NSs) to fabricate SnO2 NWs/MoS2 NS nanocomposite gas sensors for NO2 gas sensing. The operation of the sensors in self-heating mode at 1–3.5 V showed that the sensor with 20 wt.% SnO2 (SM-20 nanocomposite) had the highest response of 13 to 1000 ppb NO2 under 3.2 V applied voltage. Furthermore, the SM-20 nanocomposite gas sensor exhibited high selectivity and excellent long-term stability. The enhanced NO2 gas response was ascribed to the formation of n-n heterojunctions between SnO2 NWs and MoS2, high surface area, and the presence of some voids in the SM-20 composite gas sensor due to having different morphologies of SnO2 NWs and MoS2 NSs. It is believed that the present strategy combining MoS2 and SnO2 with different morphologies and different sensing properties is a good approach to realize high-performance NO2 gas sensors with merits such as simple synthesis and fabrication procedures, low cost, and low power consumption, which are currently in demand in the gas sensor market. [ABSTRACT FROM AUTHOR]

Hydrogen (H2) gas, with its high calorimetric combustion energy and cleanness, is a green source of energy and an alternative to fossil fuels. However, it has a small kinetic diameter, with high diffusivity and a highly explosive nature. Hence, the reliable detection of H2 gas is essential in various fields such as fuel cells. Herein, we decorated ZnO nanoparticles (NPs) with Pd noble metal NPs, using UV irradiation to enhance their H2 gas-sensing performance. The synthesized materials were fully characterized in terms of their phases, morphologies, and chemical composition. Then, the sensing layer was deposited on the electrode-patterned glass substrate to make a transparent sensor. The fabricated transparent gas sensor was able to detect H2 gas at various temperatures and humidity levels. At 250 °C, the sensor exhibited the highest response to H2 gas. As a novelty of the present study, we successfully detected H2 gas in mixtures of H2/benzene and H2/toluene gases. The enhanced H2 gas response was related to the catalytic effect of Pd, the formation of heterojunctions between Pd and ZnO, the partial reduction of ZnO to Zn in the presence of H2 gas, and the formation of PdHx. With a high performance in a high response, good selectivity, and repeatability, we believe that the sensor developed in this study can be a good candidate for practical applications where the detection of H2 is necessary. [ABSTRACT FROM AUTHOR]

Tomatoes contain many secondary metabolites such as β-carotene, lycopene, phenols, flavonoids, and vitamin C, which are responsible for antioxidant activity. SlSGR1 encodes a STAY-GREEN protein that plays a critical role in the regulation of chlorophyll degradation in tomato leaves and fruits. Therefore, the present study was conducted to evaluate the sgr1 null lines based on their physicochemical characteristics, the content of secondary metabolites, and the γ-Aminobutyric acid (GABA) content. The total soluble solids (TSS), titrated acidity (TA), and brix acid ratio (BAR) of the sgr1 null lines were higher than those of the wild type(WT). Additionally, the sgr1 null lines accumulated higher levels of flavor-inducing ascorbic acid and total carotenoids compared to WT. Also, the total phenolic content, total flavonoids, GABA content, and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical content of the sgr1 null lines were higher than those of the WT. Therefore, these studies suggest that the knockout of the SGR1 gene by the CRISPR/Cas9 system can improve various functional compounds in tomato fruit, thereby satisfying the antioxidant properties required by consumers. [ABSTRACT FROM AUTHOR]

This study aimed to analyze health management awareness among South Korean elementary school students in COVID-19 endemic areas. Methods. Using convenience sampling, 675 South Korean elementary school students (age 11–12 years old) were selected as participants in July 2023. Data for the study were collected via online and offline surveys between July and August 2023. The collected data were subjected to frequency, reliability, and multicollinearity analyses, independent sample t-tests, and importance-performance analysis (IPA). Results. The findings indicated the following: (1) There was no significant difference in health management performance between male and female children. (2) Children who had not experienced COVID-19 infection, had a higher level of "hygiene management" performance. (3) Among children who did not wear masks during physical activity, "mental health management" and "physical activity management" performance were higher, while "hygiene management" performance was lower. (4) The IPA matrix analysis revealed that, compared to the COVID-19 pandemic period, "physical activity management", "dietary habit management", and "sleep management" still required improvement, while "hygiene management" and "disease management" appeared to have decreased due to the relaxation of epidemic control efforts. Conclusion. As per the study's findings, schools, local communities, and families should make efforts to develop and implement preventive and individualized health management programs that consider the individual characteristics of their children. [ABSTRACT FROM AUTHOR]

Simple Summary: Sialocele-related concurrent diseases or factors in dogs are not well documented, and occasionally, dogs with sialocele have been found to have concurrent hypercortisolism (HC) or a history of long-term glucocorticoid (GC) treatment. In this retrospective case–control study, we investigated the association between HC, long-term GC treatment, and the diagnosis of sialocele in dogs. The occurrence of sialocele was higher in dogs with HC and those receiving long-term GC treatment, that is, treatment lasting for more than 5 months. This suggests that endogenous and exogenous GCs may influence sialocele formation. Therefore, we recommend regular monitoring for sialocele occurrence in dogs with HC or those receiving long-term GC treatment. Dogs with sialocele often have concurrent hypercortisolism or are receiving long-term glucocorticoid treatment. However, their association has not been investigated. This retrospective matched case–control study investigated the association between hypercortisolism, long-term glucocorticoid treatment, and sialocele in dogs. We retrospectively reviewed the records from 1 January 2018 to 31 December 2022. Records of 19 dogs diagnosed with sialocele were investigated for hypercortisolism and long-term glucocorticoid treatment. Two age- and breed-matched controls for each sialocele dog (38 dogs) were investigated for the same concurrent diseases. Logistic regression analysis was used. The odds of sialocele in dogs with hypercortisolism were 15.56 times those of dogs without hypercortisolism (p = 0.02; 95% CI: 1.54–156.79). The odds of sialocele in dogs with long-term glucocorticoid treatment (median, 8 months; range, 5–13) were 7.78 times those of dogs without long-term glucocorticoid treatment (p = 0.03; 95% CI: 1.23–49.40). No associations were found between age, sex, body weight, and the presence of sialocele. The results indicate that sialocele was significantly associated with hypercortisolism and long-term glucocorticoid treatment in dogs. Therefore, dogs with hypercortisolism or receiving long-term glucocorticoid therapy should be screened for possible sialocele. Additionally, dogs with sialocele should be identified for concurrent hypercortisolism and prolonged glucocorticoid exposure. [ABSTRACT FROM AUTHOR]

This study proposed a novel V-bracing system equipped with a laterally layered friction damper (LLFD), which can supplement the shortcomings of conventional vibration control systems and is applicable to existing reinforced concrete (R/C) buildings. A material test was used to evaluate the material performance and energy dissipation capacity of this LLFD. Pseudo-dynamic testing was conducted on two-story frame specimens based on an existing R/C building with non-seismic details to verify the seismic retrofitting effects of applying the LLFD V-bracing system to existing R/C frames, i.e., the restoring force characteristics, energy dissipation capacity, and seismic response control capacity. Based on the results of the material and pseudo-dynamic tests, restoring characteristics were proposed for the non-linear dynamic analysis of a building (frame specimen) retrofitted with the LLFD V-bracing system. A non-linear dynamic analysis was conducted based on the proposed restoring force characteristics, and the results obtained were compared with the pseudo-dynamic test results. Finally, for evaluating the commercialization potential of the LLFD V-bracing system, a non-linear dynamic analysis was conducted on an existing R/C building with non-seismic details retrofitted with the system. The seismic retrofitting effect was verified by examining the seismic response load and displacement characteristics, energy dissipation capacity, and damper load and displacement response before and after seismic retrofitting. The study results showed that the R/C frame (building) with non-seismic details exhibited shear failure at a design basis earthquake scale of 200 cm/s2; however, light seismic damage could be expected for a frame (building) retrofitted with the LLFD V-bracing system. At a maximum considered earthquake scale of 300 cm/s2, insignificant seismic damage was also anticipated, thereby verifying the validity of the newly developed LLFD V-bracing system. [ABSTRACT FROM AUTHOR]

Spiro-OMeTAD is a widely used hole-transporting layer (HTL) material, characterized by high hole mobility and good film-forming properties, in perovskite solar cells (PSCs). However, this material has high synthesis costs, low solubility, dependence on hygroscopic dopants, and a low commercial potential. Recently, we investigated alternative materials with good solubility, simple synthetic methods, and good electrical characteristics for use as hole transport materials (HTM) in triple-cation PSCs. Herein, (E,E,E,E)-4,4′,4″,4′″-[Benzene-1,2,4,5-tetrayltetrakis(ethene-2,1-diyl)]tetrakis[N,N-bis(4-methoxyphenyl)aniline], which has a small molecular weight and similar properties to Spiro-OMeTAD, was assessed for use as a HTM via a pre-test of device performance, including its electrical properties, surface morphology, and coating process method, with PSC efficiencies routinely surpassing 20%. A remarkable open-circuit voltage of 1.111, along with a photovoltaic efficiency of 20.18% was obtained in PSCs using this HTM with dichloromethane (DCM) instead of chlorobenzene, indicative of its potential for the fabrication of resistance components with improved surface uniformity. These results provide insights into DCM as an efficient solvent for small molecule-based HTM. [ABSTRACT FROM AUTHOR]

In the face of increasing irregular temperature patterns and climate shifts, the need for accurate power consumption prediction is becoming increasingly important to ensure a steady supply of electricity. Existing deep learning models have sought to improve prediction accuracy but commonly require greater computational demands. In this research, on the other hand, we introduce DelayNet, a lightweight deep learning model that maintains model efficiency while accommodating extended time sequences. Our DelayNet is designed based on the observation that electronic series data exhibit recurring irregular patterns over time. Furthermore, we present two substantial datasets of electricity consumption records from South Korean buildings spanning nearly two years. Empirical findings demonstrate the model's performance, achieving 21.23%, 43.60%, 17.05% and 21.71% improvement compared to recurrent neural networks, gated-recurrent units, temporal convolutional neural networks and ARIMA models, as well as greatly reducing model complexity and computational requirements. These findings indicate the potential for micro-level power consumption planning, as lightweight models can be implemented on edge devices. [ABSTRACT FROM AUTHOR]

Owing to their large surface area, two-dimensional (2D) semiconducting nanomaterials have been extensively studied for gas-sensing applications in recent years. In particular, the possibility of operating at room temperature (RT) is desirable for 2D gas sensors because it significantly reduces the power consumption of the sensing device. Furthermore, RT gas sensors are among the first choices for the development of flexible and wearable devices. In this review, we focus on the 2D MXenes used for the realization of RT gas sensors. Hence, pristine, doped, decorated, and composites of MXenes with other semiconductors for gas sensing are discussed. Two-dimensional MXene nanomaterials are discussed, with greater emphasis on the sensing mechanism. MXenes with the ability to work at RT have great potential for practical applications such as flexible and/or wearable gas sensors. [ABSTRACT FROM AUTHOR]

Estimating the measurement uncertainty (MU) is becoming increasingly mandatory in analytical toxicology. This study evaluates the uncertainty in the quantitative determination of urinary amphetamine (AP) and 4-hydroxyamphetamine (4HA) using a liquid chromatography–tandem mass spectrometry (LC–MS/MS) method based on the dilute-and-shoot approach. Urine sample dilution, preparation of calibrators, calibration curve, and method repeatability were identified as the sources of uncertainty. To evaluate the MU, the Guide to the Expression of Uncertainty in Measurement (GUM) approach and the Monte Carlo method (MCM) were compared using the R programming language. The MCM afforded a smaller coverage interval for both AP (94.83, 104.74) and 4HA (10.52, 12.14) than that produced by the GUM (AP (92.06, 107.41) and 4HA (10.21, 12.45)). The GUM approach offers an underestimated coverage interval for Type A evaluation, whereas the MCM provides an exact coverage interval under an abnormal probability distribution of the measurand. The MCM is useful in complex settings where the measurand is combined with numerous distributions because it is generated from the uncertainties of input quantities based on the propagation of the distribution. Therefore, the MCM is more practical than the GUM for evaluating the MU of urinary AP and 4HA concentrations using LC–MS/MS. [ABSTRACT FROM AUTHOR]

Capsule networks exhibit the potential to enhance computer vision tasks through their utilization of equivariance for capturing spatial relationships. However, the broader adoption of these networks has been impeded by the computational complexity of their routing mechanism and shallow backbone model. To address these challenges, this paper introduces an innovative hybrid architecture that seamlessly integrates a pretrained backbone model with a task-specific capsule head (CapsHead). Our methodology is extensively evaluated across a range of classification and segmentation tasks, encompassing diverse datasets. The empirical findings robustly underscore the efficacy and practical feasibility of our proposed approach in real-world vision applications. Notably, our approach yields substantial 3.45 % and 6.24 % enhancement in linear evaluation on the CIFAR10 dataset and segmentation on the VOC2012 dataset, respectively, compared to baselines that do not incorporate the capsule head. This research offers a noteworthy contribution by not only advancing the application of capsule networks, but also mitigating their computational complexities. The results substantiate the feasibility of our hybrid architecture, thereby paving the way for a wider integration of capsule networks into various computer vision tasks. [ABSTRACT FROM AUTHOR]

Metal oxide nanowires (NWs) with a high surface area, ease of fabrication, and precise control over diameter and chemical composition are among the best candidates for the realization of resistive gas sensors. Among the different techniques used for the synthesis of materials with NW morphology, approaches based on the vapor–liquid–solid (VLS) mechanism are very popular due to the ease of synthesis, low price of starting materials, and possibility of branching. In this review article, we discuss the gas-sensing features of metal oxide NWs grown by the VLS mechanism, with emphasis on the growth conditions and sensing mechanism. The growth and sensing performance of SnO2, ZnO, In2O3, NiO, CuO, and WO3 materials with NW morphology are discussed. The effects of the catalyst type, growth temperature, and other variables on the morphology and gas-sensing performance of NWs are discussed. [ABSTRACT FROM AUTHOR]

Simple Summary: Different strategies have been explored to counteract immune evasion by shifting the balance in favor of antitumor immune activation, and combination cytotoxic chemotherapies have emerged as potent immune modulators for patients with low programmed death-ligand 1 expression. This study aimed to investigate whether the addition of cyclophosphamide and adriamycin induction therapy prior to nivolumab could enhance the efficacy of immune checkpoint inhibitors in patients previously treated with non-squamous non-small-cell lung cancer with less than 10% programmed death-ligand 1 expression. Patients with a durable response to nivolumab showed higher baseline transferrin receptor protein levels. The predictive role of transferrin receptor protein as a biomarker for immune checkpoint inhibitors in non-squamous non-small-cell lung cancer with low programmed death-ligand 1 expression was validated in an independent cohort. This study aimed to investigate whether cyclophosphamide (C) and adriamycin (A) induction therapy (IT) prior to nivolumab could enhance the efficacy of nivolumab in previously treated patients with non-squamous (NSQ) non-small-cell lung cancer (NSCLC) with less than 10% programmed death-ligand 1 (PD-L1) expression. Twenty-two enrolled patients received four cycles of CA-IT every 3 weeks. Nivolumab was given 360 mg every 3 weeks from the second cycle and 480 mg every 4 weeks after four cycles of CA-IT. The median progression-free survival (PFS) and overall survival (OS) were 2.4 months and 11.6 months, respectively. Fluorescence-activated cell sorting revealed the lowest ratio of myeloid-derived suppressor cells (MDSCs) to CD8+T-cells in the responders. Proteomic analysis identified a consistent upregulation of extracellular matrix-receptor interactions and phagosome pathways in the responders. Among the differentially expressed proteins, the transferrin receptor protein (TFRC) was higher in the responders before treatment (fold change > 1.2). TFRC validation with an independent cohort showed the prognostic significance of either OS or PFS in patients with low PD-L1 expression. In summary, CA-IT did not improve nivolumab efficacy in NSQ-NSCLCs with low PD-L1 expression; however, it induced decreasing MDSC, resulting in a durable response. Higher baseline TFRC levels predicted a favorable response to nivolumab in NSCLC with low PD-L1 expression. [ABSTRACT FROM AUTHOR]

To understand human emotional states, local activities in various regions of the cerebral cortex and the interactions among different brain regions must be considered. This paper proposes a hierarchical emotional context feature learning model that improves multichannel electroencephalography (EEG)-based emotion recognition by learning spatiotemporal EEG features from a local brain region to a global brain region. The proposed method comprises a regional brain-level encoding module, a global brain-level encoding module, and a classifier. First, multichannel EEG signals grouped into nine regions based on the functional role of the brain are input into a regional brain-level encoding module to learn local spatiotemporal information. Subsequently, the global brain-level encoding module improved emotional classification performance by integrating local spatiotemporal information from various brain regions to learn the global context features of brain regions related to emotions. Next, we applied a two-layer bidirectional gated recurrent unit (BGRU) with self-attention to the regional brain-level module and a one-layer BGRU with self-attention to the global brain-level module. Experiments were conducted using three datasets to evaluate the EEG-based emotion recognition performance of the proposed method. The results proved that the proposed method achieves superior performance by reflecting the characteristics of multichannel EEG signals better than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Simple Summary: Although molecular subtypes of small-cell lung cancer (SCLC) have been proposed, their therapeutic implications remain unclear. We dissected SCLC subtypes to delineate the tumor microenvironment (TME) implicated in platinum-drug resistance: ASCL1+ (SCLC-A) subtype of the neuroendocrine type resembled RB1/TP53-mutant non-SCLC; inflammatory (SCLC-I) subtype presented CD8+/PD-L1+ T-cell infiltration and endothelial-to-mesenchymal transition (EndMT); NEUROD1 (SCLC-N) subtype showed neurotransmission process activation; and POU2F3+ (SCLC-P) subtype showed upregulated epithelial-to-mesenchymal transition (EMT). Meanwhile, the EndMT population was abundant in platinum-resistant SCLC. To overcome platinum resistance, we interrogated drug candidates through high-throughput screening. Cell cycle inhibitors were no longer susceptible to platinum resistance, as opposed to SCLC-A/N. The bromodomain and extra-terminal (BET) inhibitor JQ1 exhibited sensitivity to EndMT promoted by platinum resistance. BET inhibitors are therefore novel therapeutic candidates for overcoming platinum resistance. Although molecular subtypes of small-cell lung cancer (SCLC) have been proposed, their clinical relevance and therapeutic implications are not fully understood. Thus, we aimed to refine molecular subtypes and to uncover therapeutic targets. We classified the subtypes based on gene expression (n = 81) and validated them in our samples (n = 87). Non-SCLC samples were compared with SCLC subtypes to identify the early development stage of SCLC. Single-cell transcriptome analysis was applied to dissect the TME of bulk samples. Finally, to overcome platinum resistance, we performed drug screening of patient-derived cells and cell lines. Four subtypes were identified: the ASCL1+ (SCLC-A) subtype identified as TP53/RB-mutated non-SCLC representing the early development stage of SCLC; the immune activation (SCLC-I) subtype, showing high CD8+/PD-L1+ T-cell infiltration and endothelial-to-mesenchymal transition (EndMT); the NEUROD1 (SCLC-N) subtype, which showed neurotransmission process; and the POU2F3+ (SCLC-P) subtype with epithelial-to-mesenchymal transition (EMT). EndMT was associated with the worst prognosis. While SCLC-A/N exhibited platinum sensitivity, the EndMT signal of SCLC-I conferred platinum resistance. A BET inhibitor suppressed the aggressive angiogenesis phenotype of SCLC-I. We revealed that EndMT development contributed to a poor outcome in SCLC-I. Moreover, heterogenous TME development facilitated platinum resistance. BET inhibitors are novel candidates for overcoming platinum resistance. [ABSTRACT FROM AUTHOR]

This study explored the effects of trust in a supervisor (TIS) on social loafing behaviors of employees. In addition, this study examined the mediating effect of perceived organizational support (POS) on the relationship between trust in a supervisor and employees' social loafing behaviors. It also examined the moderating effects of perceived organizational politics (POP) on the relationship between TIS and POS, TIS and social loafing behaviors, and POS and social loafing behaviors. Data were collected from local government employees in Korea, and the final sample was 260. Our results indicate that trust in a supervisor has indirect negative effects on social loafing behaviors mediated by POS. In addition, it was found that the effects of TIS on POS and POS on social loafing behaviors were moderated by POP. The results of this study contribute to the extant literature on social loafing behaviors. Moreover, the findings imply that political behaviors in organizations might induce social loafing behaviors. [ABSTRACT FROM AUTHOR]

Today, resistance gas sensors which are mainly realized from metal oxides are among the most used sensing devices. However, generally, their sensing temperature is high and other materials with a lower operating temperature can be an alternative to them. Graphene and its derivatives with a 2D structure are among the most encouraging materials for gas-sensing purposes, because a 2D lattice with high surface area can maximize the interaction between the surface and gas, and a small variation in the carrier concentration of graphene can cause a notable modulation of electrical conductivity in graphene. However, they show weak sensing performance in pristine form. Hence, doping, and in particular N doping, can be one of the most promising strategies to enhance the gas-sensing features of graphene-based sensors. Herein, we discuss the gas-sensing properties of N-doped graphene and its derivatives. N doping can induce a band gap inside of graphene, generate defects, and enhance the conductivity of graphene, all factors which are beneficial for sensing studies. Additionally, not only is experimental research reviewed in this review paper, but theoretical works about N-doped graphene are also discussed. [ABSTRACT FROM AUTHOR]

Continuous efforts were made in detecting cracks in images. Varied CNN models were developed and tested for detecting or segmenting crack regions. However, most datasets used in previous works contained clearly distinctive crack images. No previous methods were validated on blurry cracks captured in low definitions. Therefore, this paper presented a framework of detecting the regions of blurred, indistinct concrete cracks. The framework divides an image into small square patches which are classified into crack or non-crack. Well-known CNN models were employed for the classification and compared with each other with experimental tests. This paper also elaborated on critical factors—the patch size and the way of labeling patches—which had considerable influences on the training performance. Furthermore, a series of post-processes for measuring crack lengths were introduced. The proposed framework was tested on the images of bridge decks containing blurred thin cracks and showed reliable performance comparable to practitioners. [ABSTRACT FROM AUTHOR]

In this paper, we addressed the challenges in sorting high-yield apple cultivars that traditionally relied on manual labor or system-based defect detection. Existing single-camera methods failed to uniformly capture the entire surface of apples, potentially leading to misclassification due to defects in unscanned areas. Various methods were proposed where apples were rotated using rollers on a conveyor. However, since the rotation was highly random, it was difficult to scan the apples uniformly for accurate classification. To overcome these limitations, we proposed a multi-camera-based apple sorting system with a rotation mechanism that ensured uniform and accurate surface imaging. The proposed system applied a rotation mechanism to individual apples while simultaneously utilizing three cameras to capture the entire surface of the apples. This method offered the advantage of quickly and uniformly acquiring the entire surface compared to single-camera and random rotation conveyor setups. The images captured by the system were analyzed using a CNN classifier deployed on embedded hardware. To maintain excellent CNN classifier performance while reducing its size and inference time, we employed knowledge distillation techniques. The CNN classifier demonstrated an inference speed of 0.069 s and an accuracy of 93.83% based on 300 apple samples. The integrated system, which included the proposed rotation mechanism and multi-camera setup, took a total of 2.84 s to sort one apple. Our proposed system provided an efficient and precise solution for detecting defects on the entire surface of apples, improving the sorting process with high reliability. [ABSTRACT FROM AUTHOR]

This work discusses the challenges of multi-label image classification and presents a novel Efficient Shuffle Net (EffShuffNet) based on a convolutional neural network (CNN) architecture to address these challenges. Multi-label classification is difficult as the complexity of prediction increases with the number of labels and classes, and current multi-model approaches require optimized deep learning models which increase computational costs. The EffShuff block divides the input feature map into two parts and processes them differently, with one half undergoing a lightweight convolution and the other half undergoing average pooling. The EffShuff transition component shuffles the feature maps after lightweight convolution, resulting in a 57.9% reduction in computational cost compared to ShuffleNetv2. Furthermore, we propose EffShuff-Dense architecture, which incorporates Dense connection to further emphasize low-level features. In experiments, the EffShuffNet achieved 96.975% accuracy in age and gender classification, which is 5.83% higher than the state-of-the-art, while EffShuffDenseNet was even better with 97.63% accuracy. Additionally, the proposed models were found to have better classification performance with smaller model sizes in fine-grained image classification experiments. [ABSTRACT FROM AUTHOR]

This study aims to extract topics from news articles on DMZ tourism published between 1990 and 2020. This study uses LDA to extract topics within the contents of news articles on DMZ tourism. Furthermore, this study aims to examine the trends of each topic within news articles and identify the characteristics of topics over the past 30 years by developing graphs. A total of 23,093 news articles published from 1990 to 2020 were collected. The data were analyzed using LDA, descriptive analysis, odd ratios, and visualized graphs. The results found that news articles on DMZ tourism can provide considerable information in terms of political, social, and environmental issues. The results provide profiles of news articles on DMZ tourism and identify the unique topics and characteristics of these news articles. The study presents findings regarding topic trends over the last 30 years. All topics show an upward trend from 1990 to 2020. Significant issues have attracted attention to the DMZ region. Furthermore, the findings demonstrate spatial and temporal differences in the volumes of topics. This study identified topics from the past 30 years and important issues related to DMZ tourism that can strengthen the unique features of the DMZ and surrounding area, promoting tourism products and content. The present study contributes to identifying topics of DMZ tourism and temporal changes from 1990 to 2020. The findings can provide meaningful insight into developing policies for DMZ tourism. [ABSTRACT FROM AUTHOR]

Urban parks are essential for offsetting high carbon emissions in cities, which are known to be high emitters. This study quantified carbon uptake and storage in Daejeon and Daegu, two major metropolitan cities in South Korea, and explored planting strategies to promote carbon offset services. Mean carbon uptake and storage per unit area in the study parks were 2.6 ± 0.1 t/ha/yr and 29.9 ± 1.7 t/ha, respectively. The urban park trees of a metropolitan city in South Korea were estimated to annually sequester and store 50 kt/yr and 572 kt of carbon, respectively. This carbon uptake equaled 1.5% of the total annual carbon emissions from residential energy consumption. The economic value of the carbon uptake was equivalent to $3.3 million/yr, which is 1.50% of the annual establishment budget of urban forests of the Korea Forest Service. Planting strategies included reducing unnecessary grass and pavement areas, the active planting of trees in the potential planting space, multilayered planting, and planting tree species with high growth rates. These results are expected to guide policies related to carbon credits, which have recently emerged as major concerns, and to provide useful information for quantifying carbon offset services in greenspace establishment projects. [ABSTRACT FROM AUTHOR]

Stay-green 1 (SGR1) protein is a critical regulator of chlorophyll degradation and senescence in plant leaves; however, the functions of tomato SGR1 remain ambiguous. Here, we generated an SGR1-knockout (KO) null line via clustered regularly interspaced palindromic repeat (CRISPR)/CRISPR-associated protein 9-mediated gene editing and conducted RNA sequencing and gas chromatography–tandem mass spectrometry analysis to identify the differentially expressed genes (DEGs). Solanum lycopersicum SGR1 (SlSGR1) knockout null line clearly showed a turbid brown color with significantly higher chlorophyll and carotenoid levels than those in the wild-type (WT) fruit. Differential gene expression analysis revealed 728 DEGs between WT and sgr#1-6 line, including 263 and 465 downregulated and upregulated genes, respectively, with fold-change >2 and adjusted p-value < 0.05. Most of the DEGs have functions related to photosynthesis, chloroplasts, and carotenoid biosynthesis. The strong changes in pigment and carotenoid content resulted in the accumulation of key primary metabolites, such as sucrose and its derivatives (fructose, galactinol, and raffinose), glycolytic intermediates (glucose, glucose-6-phosphate, and fructose-6-phosphate), and tricarboxylic acid cycle intermediates (malate and fumarate) in the leaves and fruit of the SGR-KO null lines. Overall, the SGR1-KO null lines developed here provide new evidence for the mechanisms underlying the roles of SGR1 as well as the molecular pathways involved in photosynthesis, chloroplasts, and carotenoid biosynthesis. [ABSTRACT FROM AUTHOR]

We generated an orange-colored (OC) rice callus line by targeted mutagenesis of the orange gene (OsOr) using the CRISPR-Cas9 system. The OC line accumulated more lutein, β-carotene, and two β-carotene isomers compared to the WT callus line. We also analyzed the expression levels of carotenoid biosynthesis genes by qRT-PCR. Among the genes encoding carotenoid metabolic pathway enzymes, the number of transcripts of the PSY2, PSY3, PDS, ZDS and β-LCY genes were higher in the OC line than in the WT line. In contrast, transcription of the ε-LCY gene was downregulated in the OC line compared to the WT line. In addition, we detected increases in the transcript levels of two genes involved in carotenoid oxidation in the OC lines. The developed OC lines also showed increased tolerance to salt stress. Collectively, these findings indicate that targeted mutagenesis of the OsOr gene via CRISPR/Cas9-mediated genome editing results in β-carotene accumulation in rice calli. Accordingly, we believe that this type of genome-editing technology could represent an effective alternative approach for enhancing the β-carotene content of plants. [ABSTRACT FROM AUTHOR]

In this study, in situ polydeoxyribonucleotide-impregnated hydroxyapatite (PDRN/HA) was coated on a magnesium (Mg) substrate to form a biocompatible HA layer by chemical conversion for effective PDRN delivery. The HA layer showed needle-like morphology, and the PDRN impregnation did not affect the coating structure. The loading amount of PDRN via the proposed in situ method was 2.5 times higher than that by the conventional dipping method. An in vitro cell proliferation test demonstrated that the PDRN loading was more effective through this one-step method than through the dipping method. The results indicate that in situ PDRN/HA coating can enhance the potential of Mg-based implants. [ABSTRACT FROM AUTHOR]

Clinically, fungal pneumonia rarely occurs in adults, and invasive fungal infections can cause substantial morbidity, and mortality due to sepsis and septic shock. In the present study, we have designed peptides that exhibit potent antifungal activities against fluconazole-resistant Candida albicans in physiological monovalent, and divalent ionic buffers, with minimal fungicidal concentrations ranging from 16 to 32 µM. None of these tested peptides resulted in the development of drug resistance similar to fluconazole. Among them, the PS1-2 peptide did not induce stimulation of macrophages by C. albicans, and it exerted antifungal and anti-inflammatory effects against C. albicans-induced intratracheal infection, in an acute lung injury mouse model. PS1-2 is likely a novel therapeutic agent for the control, and prevention of drug-resistant C. albicans infection, and our findings may be useful for designing antimicrobial peptides to combat fungal infection. [ABSTRACT FROM AUTHOR]

Renewable energy supply is essential for carbon neutrality; however, technologies aiming to optimally utilize renewable energy sources remain insufficient. Seasonal variability in renewable energy is a key issue, which many studies have attempted to overcome through operating systems and energy storage. Currently, hydrogen is the only technology that can solve this seasonal storage problem. In this study, the amount of hydrogen required to circumvent the seasonal variability in renewable energy supply in Korea was quantified. Spatiotemporal analysis was conducted using renewable energy resource maps and power loads. It was predicted that 50% of the total power demand in the future will be met using solar and wind power, and a scenario was established based on the solar-to-wind ratio. It was found that the required hydrogen production differed by approximately four-times, depending on the scenarios, highlighting the importance of supplying renewable energy at an appropriate ratio. Spatially, wind power was observed to be unsuitable for the physical transport of hydrogen because it has a high potential at mountain peaks and islands. The results of this study are expected to aid future hydrogen research and solve renewable energy variability problems. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to investigate the association between responses to intravitreal bevacizumab injection and renal function in diabetic macular edema (DME) patients. Methods: A retrospective study of the medical records of 104 treatment-naïve DME patients who received intravitreal bevacizumab injection (IVBI) was conducted. Based on the estimated glomerular filtration rate (eGFR, mL/min/1.73 m2), the participants were classified into three groups. Intergroup comparisons of the best-corrected visual acuity (BCVA) and central subfield retinal thickness (CST) changes were performed after three-monthly consecutive IVBIs. In the groups with decreased renal function, the response to further treatment with a different drug was investigated. Results: A total of 104 participants were included in the study: 60 participants in the preserved renal function group (eGFR ≥ 60), 25 participants in the moderate chronic kidney disease (CKD) group (30 ≤ eGFR < 60), and 19 participants in the severe CKD group (eGFR < 30). After three-monthly consecutive IVBIs, BCVA (p < 0.001) and CST (p < 0.001) were significantly improved only in the preserved renal function group. Following further treatment of patients with decreased renal function, the treatment results were significantly better in those who were switched to aflibercept or dexamethasone implant than in those who were maintained on IVBI. Conclusions: From this preliminary study, we observed that renal function might affect the response to IVBI treatment in patients with DME. In the case of a poor response to initial IVBI treatment for DME in patients with moderate to severe CKD, our study supports switching to the aflibercept or dexamethasone implant. [ABSTRACT FROM AUTHOR]

The brain is more sensitive to stress than other organs and can develop many diseases under excessive stress. In this study, we developed a method to improve the accuracy of emotional stress recognition using multi-channel electroencephalogram (EEG) signals. The method combines a three-dimensional (3D) convolutional neural network with an attention mechanism to build a 3D convolutional gated self-attention neural network. Initially, the EEG signal is decomposed into four frequency bands, and a 3D convolutional block is applied to each frequency band to obtain EEG spatiotemporal information. Subsequently, long-range dependencies and global information are learned by capturing prominent information from each frequency band via a gated self-attention mechanism block. Using frequency band mapping, complementary features are learned by connecting vectors from different frequency bands, which is reflected in the final attentional representation for stress recognition. Experiments conducted on three benchmark datasets for assessing the performance of emotional stress recognition indicate that the proposed method outperforms other conventional methods. The performance analysis of proposed methods confirms that EEG pattern analysis can be used for studying human brain activity and can accurately distinguish the state of stress. [ABSTRACT FROM AUTHOR]

Renewable energy forecasting is a key for efficient resource use in terms of power generation and safe grid control. In this study, we investigated a short-term statistical forecasting model with 1 to 3 h horizons using photovoltaic operation data from 215 power plants throughout South Korea. A vector autoregression (VAR) model-based regional photovoltaic power forecasting system is proposed for seven clusters of power plants in South Korea. This method showed better predictability than the autoregressive integrated moving average (ARIMA) model. The normalized root-mean-square errors of hourly photovoltaic generation predictions obtained from VAR (ARIMA) were 8.5–10.9% (9.8–13.0%) and 18.5–22.8% (21.3–26.3%) for 1 h and 3 h horizon, respectively, at 215 power plants. The coefficient of determination, R2 was higher for VAR, at 4–5%, than ARIMA. The VAR model had greater accuracy than ARIMA. This will be useful for economical and efficient grid management. [ABSTRACT FROM AUTHOR]

Utilizing pre-trained models involves fully or partially using pre-trained parameters as initialization. In general, configuring a pre-trained model demands practitioners' knowledge about problems or an exhaustive trial–error experiment according to a given task. In this paper, we propose tuning trainable layers using a genetic algorithm on a pre-trained model that is fine-tuned on single-channel image datasets for a classification task. The single-channel dataset comprises images from grayscale and preprocessed audio signals transformed into a log-Mel spectrogram. Four deep-learning models used in the experimental evaluation employed the pre-trained model with the ImageNet dataset. The proposed genetic algorithm was applied to find the highest fitness for every generation to determine the selective layer tuning of the pre-trained models. Compared to the conventional fine-tuning method and random layer search, our proposed selective layer search with a genetic algorithm achieves higher accuracy, on average, by 9.7% and 1.88% (MNIST-Fashion), 1.31% and 1.14% (UrbanSound8k), and 2.2% and 0.29% (HospitalAlarmSound), respectively. In addition, our searching method can naturally be applied to various datasets of the same task without prior knowledge about the dataset of interest. [ABSTRACT FROM AUTHOR]

Forecasting, commonly used in econometrics, meteorology, or energy consumption prediction, is the field of study that deals with time series data to predict future trends. Former studies have revealed that both traditional statistical models and recent deep learning-based approaches have achieved good performance in forecasting. In particular, temporal convolutional networks (TCNs) have proved their effectiveness in several time series benchmarks. However, presented TCN models are too heavy to deploy on resource-constrained systems, such as edge devices. As a resolution, this study proposes a stride–dilation mechanism for TCN that favors a lightweight model yet still achieves on-pair accuracy with the heavy counterparts. We also present the Chonnam National University (CNU) Electric Power Consumption dataset, the dataset of energy consumption measured at CNU by smart meters every hour. The experimental results indicate that our best model reduces the mean squared error by 32.7%, whereas the model size is only 1.6% compared to the baseline TCN. [ABSTRACT FROM AUTHOR]

Ternary systems are less studied for sensing applications due to complex synthesis procedures. However, they have more sources of resistance modulation, leading to an enhanced gas response. In this study, a ternary system, namely Au-decorated ZnO-polyaniline (PANI) composite nanofibers with different amounts of PANI (10, 25, and 50 wt.%) were synthesized for NO2 gas sensing studies. First, ZnO nanofibers were synthesized by electrospinning, and then an Au layer (9 nm) was coated on the ZnO nanofibers. Finally, PANI was coated onto the prepared Au-decorated ZnO nanofibers. NO2 gas sensing investigations indicated that the sensor with 25 wt.% PANI had the best response to NO2 gas at 300 °C. In addition, the optimized sensor exhibited high selectivity to NO2 gas. The improved performance of the optimal gas sensor was attributed to the role of Au, the formation of ZnO-PANI heterojunctions, and the optimal amount of PANI. The promising effect of this ternary system for NO2 sensing was demonstrated, and it can be extended to other similar systems. [ABSTRACT FROM AUTHOR]

Lycopene epsilon-cyclase (LcyE) is a key enzyme in the carotenoid biosynthetic pathway of higher plants. Using the CRSPR/Cas9 and the geminiviral replicon, we optimized a method for targeted mutagenesis and golden SNP replacement of the LcyE gene in rice. We have exploited the geminiviral replicon amplification as a means to provide a large amount of donor template for the repair of a CRISPR-Cas-induced DNA double-strand break (DSB) in the target gene via homology-directed repair (HDR). Mutagenesis experiments performed on the Donggin variety achieved precise modification of the LcyE loci with an efficiency of up to 90%. In HDR experiments, our target was the LcyE allele (LcyE-H523L) derived from anther culture containing a golden SNP replacement. The phenotype of the homologous recombination (HR) mutant obtained through the geminiviral replicon-based template delivery system was tangerine color, and the frequency was 1.32% of the transformed calli. In addition, the total carotenoid content of the LcyEsg2-HDR1 and LcyEsg2-HDR2 lines was 6.8–9.6 times higher than that of the wild-type (WT) calli, respectively. The reactive oxygen species content was lower in the LcyEsg2-HDR1 and LcyEsg2-HDR2 lines. These results indicate that efficient HDR can be achieved in the golden SNP replacement using a single and modular configuration applicable to different rice targets and other crops. This work demonstrates the potential to replace all genes with elite alleles within one generation and greatly expands our ability to improve agriculturally important traits. [ABSTRACT FROM AUTHOR]

Graph convolutional neural networks (GCNNs) have been successfully applied to a wide range of problems, including low-dimensional Euclidean structural domains representing images, videos, and speech and high-dimensional non-Euclidean domains, such as social networks and chemical molecular structures. However, in computer vision, the existing GCNNs are not provided with positional information to distinguish between graphs of new structures; therefore, the performance of the image classification domain represented by arbitrary graphs is significantly poor. In this work, we introduce how to initialize the positional information through a random walk algorithm and continuously learn the additional position-embedded information of various graph structures represented over the superpixel images we choose for efficiency. We call this method the graph convolutional network with learnable positional embedding applied on images (IMGCN-LPE). We apply IMGCN-LPE to three graph convolutional models (the Chebyshev graph convolutional network, graph convolutional network, and graph attention network) to validate performance on various benchmark image datasets. As a result, although not as impressive as convolutional neural networks, the proposed method outperforms various other conventional convolutional methods and demonstrates its effectiveness among the same tasks in the field of GCNNs. [ABSTRACT FROM AUTHOR]

In this work, we report the synthesis and photovoltaic properties of IEBICO-4F, IEHICO-4F, IOICO-4F, and IDICO-4F non-fullerene acceptors (NFAs) bearing different types of alkyl chains (2-ehtylhexyl (EH), 2-ethylbutyl (EB), n-octyl (O), and n-decyl (D), respectively). These NFAs are based on the central indacenodithiophene (IDT) donor core and the same terminal group of 2-(5,6-difluoro-3-oxo-2,3-dihydro-1H-inden-1-ylidene)malononitrile (IC-2F), albeit with different side chains appended to the thiophene bridge unit. Although the side chains induced negligible differences between the NFAs in terms of optical band gaps and molecular energy levels, they did lead to changes in their melting points and crystallinity. The NFAs with branched alkyl chains exhibited weaker intermolecular interactions and crystallinity than those with linear alkyl chains. Organic solar cells (OSCs) were fabricated by blending these NFAs with the p-type polymer PTB7-Th. The NFAs with appended branched alkyl chains (IEHICO-4F and IEBICO-4F) possessed superior photovoltaic properties than those with appended linear alkyl chains (IOICO-4F and IDICO-4F). This result can be ascribed mainly to the thin-film morphology. Furthermore, the NFA-based blend films with appended branched alkyl chains exhibited the optimal degree of aggregation and miscibility, whereas the NFA-based blend films with appended linear alkyl chains exhibited higher levels of self-aggregation and lower miscibility between the NFA molecule and the PTB7-Th polymer. We demonstrate that changing the alkyl chain on the π-bridging unit in fused-ring-based NFAs is an effective strategy for improving their photovoltaic performance in bulk heterojunction-type OSCs. [ABSTRACT FROM AUTHOR]

The accurate and timely depiction of the state of severe weather is critical for enhancing forecaster situational awareness. This study attempted to develop a hurricane forecasting model with a warm-start run and investigated the impact of winds observed during a tropical cyclone on long-term lead times. The Hurricane Research System initialized with the Hurricane Local Analysis Prediction System (HRS/HLAPS) was applied to Hurricanes Katrina and Dennis (2005). The forecasting model used a warm-start run with 7% improved wind data and cloud initialization using the HLAPS. The simulated cyclones were more intense and realistic structures, although the performance varied slightly according to the lead time and cyclone characteristics. The results show that the tropical cyclone development (track and intensity) was significantly affected by initial forcing up to 6–12 h, as well as by the forcing of the limit condition after 6 h. The well-organized spiral bands of convective precipitations were also captured, particularly within the 6 h spin-up time due to vertical wind shear and water vapor trapped in the lower atmosphere. This study demonstrates that aircraft-observed winds and convective initialization can be useful for numerical modeling and operational forecasting. [ABSTRACT FROM AUTHOR]

After NEM treatment for 0, 3, 5, 6, 9, and 12 h, we counted the live cells at each time-point, and we selected 5 h for sampling the RNA-seq because the cell proliferation was significantly inhibited at 5 h in 10 µM NEM treated SW10 cells (Figure S1). Whole-Genome Expression Profiling of Dedifferentiated Schwann Cells We performed whole-genome expression profiling of DMSO-treated SW10 cells (control), as dedifferentiated Schwann cells and NEM-treated SW10 cells (H SB 2 sb S-pathway-inhibited Schwann cells), by RNA sequencing (RNA-seq) (Figure 1A). Next, because KEGG enrichment analysis identified ROS (Figure S3A), to test whether proteins involved in oxidative stress were regulated by NEM during ex vivo PND, we selected 19 oxidative stress proteins from the NEM/1DIV data overlapped with the QuickGO database [[16]] (Figure 4C). [Extracted from the article]

Nanoparticles have garnered significant interest in neurological research in recent years owing to their efficient penetration of the blood–brain barrier (BBB). However, significant concerns are associated with their harmful effects, including those related to the immune response mediated by microglia, the resident immune cells in the brain, which are exposed to nanoparticles. We analysed the cytotoxic effects of silica-coated magnetic nanoparticles containing rhodamine B isothiocyanate dye [MNPs@SiO2(RITC)] in a BV2 microglial cell line using systems toxicological analysis. We performed the invasion assay and the exocytosis assay and transcriptomics, proteomics, metabolomics, and integrated triple-omics analysis, generating a single network using a machine learning algorithm. The results highlight alteration in the mechanisms of the nanotoxic effects of nanoparticles using integrated omics analysis. [ABSTRACT FROM AUTHOR]

The ability to accurately predict the prognosis and intervention requirements for treating highly infectious diseases, such as COVID-19, can greatly support the effective management of patients, especially in resource-limited settings. The aim of the study is to develop and validate a multimodal artificial intelligence (AI) system using clinical findings, laboratory data and AI-interpreted features of chest X-rays (CXRs), and to predict the prognosis and the required interventions for patients diagnosed with COVID-19, using multi-center data. In total, 2282 real-time reverse transcriptase polymerase chain reaction-confirmed COVID-19 patients' initial clinical findings, laboratory data and CXRs were retrospectively collected from 13 medical centers in South Korea, between January 2020 and June 2021. The prognostic outcomes collected included intensive care unit (ICU) admission and in-hospital mortality. Intervention outcomes included the use of oxygen (O2) supplementation, mechanical ventilation and extracorporeal membrane oxygenation (ECMO). A deep learning algorithm detecting 10 common CXR abnormalities (DLAD-10) was used to infer the initial CXR taken. A random forest model with a quantile classifier was used to predict the prognostic and intervention outcomes, using multimodal data. The area under the receiver operating curve (AUROC) values for the single-modal model, using clinical findings, laboratory data and the outputs from DLAD-10, were 0.742 (95% confidence interval [CI], 0.696–0.788), 0.794 (0.745–0.843) and 0.770 (0.724–0.815), respectively. The AUROC of the combined model, using clinical findings, laboratory data and DLAD-10 outputs, was significantly higher at 0.854 (0.820–0.889) than that of all other models (p < 0.001, using DeLong's test). In the order of importance, age, dyspnea, consolidation and fever were significant clinical variables for prediction. The most predictive DLAD-10 output was consolidation. We have shown that a multimodal AI model can improve the performance of predicting both the prognosis and intervention in COVID-19 patients, and this could assist in effective treatment and subsequent resource management. Further, image feature extraction using an established AI engine with well-defined clinical outputs, and combining them with different modes of clinical data, could be a useful way of creating an understandable multimodal prediction model. [ABSTRACT FROM AUTHOR]

There is an increasing need for the development of low-cost and highly sensitive gas sensors for environmental, commercial, and industrial applications in various areas, such as hazardous gas monitoring, safety, and emission control in combustion processes. Considering this, resistive-based gas sensors using metal oxide semiconductors (MOSs) have gained special attention owing to their high sensing performance, high stability, and low cost of synthesis and fabrication. The relatively low final costs of these gas sensors allow their commercialization; consequently, they are widely used and available at low prices. This review focuses on the important MOSs with different morphologies, including quantum dots, nanowires, nanofibers, nanotubes, hierarchical nanostructures, and other structures for the fabrication of resistive gas sensors. [ABSTRACT FROM AUTHOR]

Quantum dots (QDs) are used progressively in sensing areas because of their special electrical properties due to their extremely small size. This paper discusses the gas sensing features of QD-based resistive sensors. Different types of pristine, doped, composite, and noble metal decorated QDs are discussed. In particular, the review focus primarily on the sensing mechanisms suggested for these gas sensors. QDs show a high sensing performance at generally low temperatures owing to their extremely small sizes, making them promising materials for the realization of reliable and high-output gas-sensing devices. [ABSTRACT FROM AUTHOR]

Although street trees take up carbon by vegetation growth, they emit carbon directly or indirectly into the atmosphere due to the need for regular management, such as pruning and, irrigation, etc. This study estimates the annual carbon budget from the growth and management of urban street trees in South Korea and suggests management strategies to minimize the degree of management and annual carbon emissions. Annual carbon emissions from management practices such as pruning, irrigation, pesticide and fungicide application, and fertilization were estimated based on maintenance data, interviews with managers, and actual measurements. Annual carbon emissions from the management of urban street trees were calculated to be 2.4 kg/tree/year. From this total, pruning was the largest contributor at 91%. The average net carbon uptake for each street tree was 5.5 kg/tree/year. The street trees included in this study played an important role in carbon uptake, which was equivalent to 90–550% of the annual carbon emissions from street tree management. Based on these results, suggested management strategies include pruning, irrigation, pesticide and fungicide application, and fertilization. These study results will be useful for designing practical low-carbon landscapes with minimal management and carbon emissions. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the feasibility and safety of temporary transcatheter balloon occlusion of bilateral internal iliac arteries (TBOIIA) during cesarean section in a hybrid operating room (OR) for placenta previa (PP) with a high risk of massive hemorrhage. This retrospective study analyzed the medical records of 62 patients experiencing PP with a high risk of massive hemorrhage (mean age, 36.2 years; age range 28–45 years) who delivered a baby via planned cesarean section with TBOIIA in a hybrid OR between May 2019 and July 2021. Operation time, estimated blood loss (EBL), amount of intra- and postoperative blood transfusion, perioperative hemoglobin level, hospital stay after operation, balloon time, fluoroscopy time, radiation dose, rate of uterine artery embolization (UAE) and hysterectomy, and complication-related TBOIIA were assessed. The mean operation time was 122 min, and EBL was 1290 mL. Nine out of sixty-two patients (14.5%) received a blood transfusion. The mean hemoglobin levels before surgery, immediately after surgery and within 1 week after surgery were 11.3 g/dL, 10.4 g/dL and 9.2 g/dL, respectively. In terms of radiation dose, the mean dose area product (DAP) and cumulative air kerma were 0.017 Gy/cm2 and 0.023 Gy, respectively. Ten out of sixty-two patients (16.1%) underwent UAE postoperatively in the hybrid OR. One out of sixty-two patients had been diagnosed with placenta percreta with bladder invasion based on preoperative ultrasound, and thus underwent cesarean hysterectomy following TBOIIA and UAE. While intra-arterial balloon catheter placement for managing PP with a high risk of hemorrhage remains controversial, a planned cesarean section with TBOIIA in a hybrid OR is effective in eliminating the potential risk of intra-arterial balloon catheter displacement, thus reducing intraoperative blood loss, ensuring safe placental removal and conserving the uterus. [ABSTRACT FROM AUTHOR]

Crop leaf disease management and control pose significant impact on enhancement in yield and quality to fulfill consumer needs. For smart agriculture, an intelligent leaf disease identification system is inevitable for efficient crop health monitoring. In this view, a novel approach is proposed for crop disease identification using feature fusion and PCA-LDA classification (FF-PCA-LDA). Handcrafted hybrid and deep features are extracted from RGB images. TL-ResNet50 is used to extract the deep features. Fused feature vector is obtained by combining handcrafted hybrid and deep features. After fusing the image features, PCA is employed to select most discriminant features for LDA model development. Potato crop leaf disease identification is used as a case study for the validation of the approach. The developed system is experimentally validated on a potato crop leaf benchmark dataset. It offers high accuracy of 98.20% on an unseen dataset which was not used during the model training process. Performance comparison of the proposed technique with other approaches shows its superiority. Owing to the better discrimination and learning ability, the proposed approach overcomes the leaf segmentation step. The developed approach may be used as an automated tool for crop monitoring, management control, and can be extended for other crop types. [ABSTRACT FROM AUTHOR]