MXene-Enhanced Ionovoltaic Effect by Evaporation and Water Infiltration in Semiconductor Nanochannels.

In recent years, substantial attention has been directed toward energy-harvesting systems that exploit sunlight energy and water resources. Intensive research efforts are underway to develop energy generation methodologies through interactions with water using various materials. In the present investigation, we synthesized sodium vanadium oxide (SVO) nanorods with n-type semiconductor characteristics. These nanorods facilitate the initiation of capillary phenomena within nanochannels, thereby enhancing the interfacial area between nanomaterials and ions. The open-circuit voltage (VOC) was 0.8 V, and the short-circuit current (ISC) was 30 µA, which were continuously monitored at room temperature using a 0.1 M saltwater solution. Additionally, we achieved enhanced energy generation by efficiently converting light energy into thermal energy using MXene, a 2D material. This was accomplished through the photothermal effect, leveraging the inherent semiconductor characteristics. Under light exposure, the system exhibited improved performance attributed to heightened ion diffusion and increased conductivity. This phenomenon was a result of the concerted synergy between ions and electrons facilitated by a semiconductor nanofluidic channel. Ultimately, we demonstrated an application to showcase real-world viability. In this scenario, electricity was harvested through a smart buoy floating on the water, and, based on this, data from the surrounding environment was sensed and wirelessly transmitted.

PCTC-Net: A Crack Segmentation Network with Parallel Dual Encoder Network Fusing Pre-Conv-Based Transformers and Convolutional Neural Networks.

Cracks are common defects that occur on the surfaces of objects and structures. Crack detection is a critical maintenance task that traditionally requires manual labor. Large-scale manual inspections are expensive. Research has been conducted to replace expensive human labor with cheaper computing resources. Recently, crack segmentation based on convolutional neural networks (CNNs) and transformers has been actively investigated for local and global information. However, the transformer is data-intensive owing to its weak inductive bias. Existing labeled datasets for crack segmentation are relatively small. Additionally, a limited amount of fine-grained crack data is available. To address this data-intensive problem, we propose a parallel dual encoder network fusing Pre-Conv-based Transformers and convolutional neural networks (PCTC-Net). The Pre-Conv module automatically optimizes each color channel with a small spatial kernel before the input of the transformer. The proposed model, PCTC-Net, was tested with the DeepCrack, Crack500, and Crackseg9k datasets. The experimental results showed that our model achieved higher generalization performance, stability, and F1 scores than the SOTA model DTrC-Net.

Novel Estrogen Receptor Dimerization BRET-Based Biosensors for Screening Estrogenic Endocrine-Disrupting Chemicals.

The increasing prevalence of endocrine-disrupting chemicals (EDCs) in our environment is a growing concern, with numerous studies highlighting their adverse effects on the human endocrine system. Among the EDCs, estrogenic endocrine-disrupting chemicals (eEDCs) are exogenous compounds that perturb estrogenic hormone function by interfering with estrogen receptor (ER) homo (α/α, ß/ß) or hetero (α/ß) dimerization. To date, a comprehensive screening approach for eEDCs affecting all ER dimer forms in live cells is lacking. Here, we developed ER dimerization-detecting biosensors (ERDDBs), based on bioluminescence resonance energy transfer, for dimerization detection and rapid eEDC identification. To enhance the performance of these biosensors, we determined optimal donor and acceptor locations using computational analysis. Additionally, employing HaloTag as the acceptor and incorporating the P2A peptide as a linker yielded the highest sensitivity among the prototypes. We also established stable cell lines to screen potential ER dimerization inducers among estrogen analogs (EAs). The EAs were categorized through cross-comparison of ER dimer responses, utilizing EC values derived from a standard curve established with 17ß-estradiol. We successfully classified 26 of 72 EAs, identifying which ER dimerization types they induce. Overall, our study underscores the effectiveness of the optimized ERDDB for detecting ER dimerization and its applicability in screening and identifying eEDCs.

User identification system based on 2D CQT spectrogram of EMG with adaptive frequency resolution adjustment.

User identification systems based on electromyogram (EMG) signals, generated inside the body in different signal patterns and exhibiting individual characteristics based on muscle development and activity, are being actively researched. However, nonlinear and abnormal signals constrain conventional user identification using EMG signals in improving accuracy by using the 1-D feature from each time and frequency domain. Therefore, multidimensional features containing time-frequency information extracted from EMG signals have attracted much attention to improving identification accuracy. We propose a user identification system using constant Q transform (CQT) based 2D features whose time-frequency resolution is customized according to EMG signals. The proposed user identification system comprises data preprocessing, CQT-based 2D image conversion, convolutional feature extraction, and classification by convolutional neural network (CNN). The experimental results showed that the accuracy of the proposed user identification system using CQT-based 2D spectrograms was 97.5%, an improvement of 15.4% and 2.1% compared to the accuracy of 1D features and short-time Fourier transform (STFT) based user identification, respectively.

TAUCON and TAUCOM: A novel biosensor based on fluorescence resonance energy transfer for detecting tau hyperphosphorylation-associated cellular pathologies.

Tauopathies are neurodegenerative diseases characterized by abnormal conformational changes in tau protein. Early hyperphosphorylation-induced conformational changes are considered a hallmark of tauopathy, but real-time tracking methods are lacking. Here, we present two novel fluorescence resonance energy transfer (FRET)-based tau biosensors that detect such changes with high spatiotemporal resolution at the single-cell level. The TAUCON biosensor measures instantaneous conformational changes in hyperphosphorylated tau within 20 min, while the TAUCOM biosensor detects changes in the paper-clip structure of microtubule-associated tau. Our biosensors provide faster and more precise detection than conventional methods and can serve as valuable tools for investigating the initial causes, mechanisms, progression, and treatment of tauopathies.

Multi-modal Biometrics Based Implicit Driver Identification System Using Multi-TF Images of ECG and EMG.

As security is emphasized inside and outside the vehicle, research on driver identification technology using bio-signals is being actively studied. The bio-signals acquired by the behavioral characteristics of the driver include artifacts generated according to the driving environment, which could potentially degrade the accuracy of the identification system. Existing driver identification systems either remove the normalization process of bio-signals in the preprocessing stage or use artifacts included in a single bio-signals, resulting in low identification accuracy. To solve these problems in a real situation, we propose a driver identification system that converts ECG and EMG signals obtained from different driving conditions into 2D spectrograms through multi-TF image and uses multi-stream CNN. The proposed system consists of a preprocessing phase of ECG and EMG signals, a multi-TF image conversion process, and a driver identification stage using a multi-stream-based CNN. Under all driving conditions, the driver identification system reached an average accuracy of 96.8% and an F1 score of 0.973, which overperformed the existing driver identification systems by more than 1%.

EFFNet-CA: An Efficient Driver Distraction Detection Based on Multiscale Features Extractions and Channel Attention Mechanism.

Driver distraction is considered a main cause of road accidents, every year, thousands of people obtain serious injuries, and most of them lose their lives. In addition, a continuous increase can be found in road accidents due to driver's distractions, such as talking, drinking, and using electronic devices, among others. Similarly, several researchers have developed different traditional deep learning techniques for the efficient detection of driver activity. However, the current studies need further improvement due to the higher number of false predictions in real time. To cope with these issues, it is significant to develop an effective technique which detects driver's behavior in real time to prevent human lives and their property from being damaged. In this work, we develop a convolutional neural network (CNN)-based technique with the integration of a channel attention (CA) mechanism for efficient and effective detection of driver behavior. Moreover, we compared the proposed model with solo and integration flavors of various backbone models and CA such as VGG16, VGG16+CA, ResNet50, ResNet50+CA, Xception, Xception+CA, InceptionV3, InceptionV3+CA, and EfficientNetB0. Additionally, the proposed model obtained optimal performance in terms of evaluation metrics, for instance, accuracy, precision, recall, and F1-score using two well-known datasets such as AUC Distracted Driver (AUCD2) and State Farm Distracted Driver Detection (SFD3). The proposed model achieved 99.58% result in terms of accuracy using SFD3 while 98.97% accuracy on AUCD2 datasets.

Automatic Classification Service System for Citrus Pest Recognition Based on Deep Learning.

Plant diseases are a major cause of reduction in agricultural output, which leads to severe economic losses and unstable food supply. The citrus plant is an economically important fruit crop grown and produced worldwide. However, citrus plants are easily affected by various factors, such as climate change, pests, and diseases, resulting in reduced yield and quality. Advances in computer vision in recent years have been widely used for plant disease detection and classification, providing opportunities for early disease detection, and resulting in improvements in agriculture. Particularly, the early and accurate detection of citrus diseases, which are vulnerable to pests, is very important to prevent the spread of pests and reduce crop damage. Research on citrus pest disease is ongoing, but it is difficult to apply research results to cultivation owing to a lack of datasets for research and limited types of pests. In this study, we built a dataset by self-collecting a total of 20,000 citrus pest images, including fruits and leaves, from actual cultivation sites. The constructed dataset was trained, verified, and tested using a model that had undergone five transfer learning steps. All models used in the experiment had an average accuracy of 97% or more and an average f1 score of 96% or more. We built a web application server using the EfficientNet-b0 model, which exhibited the best performance among the five learning models. The built web application tested citrus pest disease using image samples collected from websites other than the self-collected image samples and prepared data, and both samples correctly classified the disease. The citrus pest automatic diagnosis web system using the model proposed in this study plays a useful auxiliary role in recognizing and classifying citrus diseases. This can, in turn, help improve the overall quality of citrus fruits.

Resource for FRET-Based Biosensor Optimization.

After the development of Cameleon, the first fluorescence resonance energy transfer (FRET)-based calcium indicator, a variety of FRET-based genetically encoded biosensors (GEBs) have visualized numerous target players to monitor their cell physiological dynamics spatiotemporally. Many attempts have been made to optimize GEBs, which require labor-intensive effort, novel approaches, and precedents to develop more sensitive and versatile biosensors. However, researchers face considerable trial and error in upgrading biosensors because examples and methods of improving FRET-based GEBs are not well documented. In this review, we organize various optimization strategies after assembling the existing cases in which the non-fluorescent components of biosensors are upgraded. In addition, promising areas to which optimized biosensors can be applied are briefly discussed. Therefore, this review could serve as a resource for researchers attempting FRET-based GEB optimization.

Förster Resonance Energy Transfer-Based Single-Cell Imaging Reveals Piezo1-Induced Ca2+ Flux Mediates Membrane Ruffling and Cell Survival.

A mechanosensitive ion channel, Piezo1 induces non-selective cation flux in response to various mechanical stresses. However, the biological interpretation and underlying mechanisms of cells resulting from Piezo1 activation remain elusive. This study elucidates Piezo1-mediated Ca2+ influx driven by channel activation and cellular behavior using novel Förster Resonance Energy Transfer (FRET)-based biosensors and single-cell imaging analysis. Results reveal that extracellular Ca2+ influx via Piezo1 requires intact caveolin, cholesterol, and cytoskeletal support. Increased cytoplasmic Ca2+ levels enhance PKA, ERK, Rac1, and ROCK activity, which have the potential to promote cancer cell survival and migration. Furthermore, we demonstrate that Piezo1-mediated Ca2+ influx upregulates membrane ruffling, a characteristic feature of cancer cell metastasis, using spatiotemporal image correlation spectroscopy. Thus, our findings provide new insights into the function of Piezo1, suggesting that Piezo1 plays a significant role in the behavior of cancer cells.

Rhynchosia volubilis Promotes Cell Survival via cAMP-PKA/ERK-CREB Pathway.

Rhynchosia volubilis, a small black bean, has been used as a traditional remedy to treat diseases and maintain health in East Asia, but its cellular effects and molecular mechanisms are not fully understood. The purpose of this study was to investigate the effect of ethanol extract from Rhynchosia volubilis (EERV) on cell survival and to elucidate the biochemical signaling pathways. Our results showed that EERV stimulated the cyclic AMP (cAMP) signal revealed by a fluorescent protein (FP)-based intensiometric sensor. Using a Förster resonance energy transfer (FRET)-based sensor, we further revealed that EERV could activate PKA and ERK signals, which are downstream effectors of cAMP. In addition, we reported that EERV could induce the phosphorylation of CREB, a key signal for cell survival. Thus, our results suggested that EERV protects against apoptosis by activating the cell survival pathway through the cAMP-PKA/ERK-CREB pathway.

Distinct calcium regulation of TRPM7 mechanosensitive channels at plasma membrane microdomains visualized by FRET-based single cell imaging.

Transient receptor potential subfamily M member 7 (TRPM7), a mechanosensitive Ca2+ channel, plays a crucial role in intracellular Ca2+ homeostasis. However, it is currently unclear how cell mechanical cues control TRPM7 activity and its associated Ca2+ influx at plasma membrane microdomains. Using two different types of Ca2+ biosensors (Lyn-D3cpv and Kras-D3cpv) based on fluorescence resonance energy transfer, we investigate how Ca2+ influx generated by the TRPM7-specific agonist naltriben is mediated at the detergent-resistant membrane (DRM) and non-DRM regions. This study reveals that TRPM7-induced Ca2+ influx mainly occurs at the DRM, and chemically induced mechanical perturbations in the cell mechanosensitive apparatus substantially reduce Ca2+ influx through TRPM7, preferably located at the DRM. Such perturbations include the disintegration of lipid rafts, microtubules, or actomyosin filaments; the alteration of actomyosin contractility; and the inhibition of focal adhesion and Src kinases. These results suggest that the mechanical membrane environment contributes to the TRPM7 function and activity. Thus, this study provides a fundamental understanding of how the mechanical aspects of the cell membrane regulate the function of mechanosensitive channels.

Analysis of Laser Injection Condition and Electrical Properties in Local BSF for Laser Fired Contact c-Si Solar Cell Applications.

A crystalline silicon (c-Si) local-back-contact (LBC) solar cell for which a laser-condition-optimized surface-recombination velocity (SRV), a contact resistance (Rc), and local back surface fields (LBSFs) were utilized is reported. The effect of the laser condition on the rear-side electrical properties of the laser-fired LBC solar cell was studied. The Nd:YAG-laser (1064-nm wavelength) power and frequency were varied to obtain LBSF values with a lower contact resistance. A 10-kHz laser power of 44 mW resulted in an Rc of 0.125 ohms with an LBSF thickness of 2.09 µm and a higher open-circuit voltage (VOC) of 642 mV.

Direct metallization local Al-back surface field for high efficiency screen printed crystalline silicon solar cells.

In this paper, we present a detailed study on the local back contact (LBC) formation of rear-surface-passivated silicon solar cells, where both the LBC opening and metallization are realized by one-step alloying of a dot of fine pattern screen-printed aluminum paste with the silicon substrate. Based on energy dispersive spectrometer (EDS) and scanning electron microscopy (SEM) characterizations, we suggest that the aluminum distribution and the silicon concentration determine the local-back-surface-field (Al-p+) layer thickness, resistivity of the Al-p+ and hence the quality of the Al-p+ formation. The highest penetration of silicon concentration of 78.17% in aluminum resulted in the formation of a 5 microm-deep Al-p+ layer, and the minimum LBC resistivity of 0.92 x 10-6 omega cm2. The degradation of the rear-surface passivation due to high temperature of the LBC formation process can be fully recovered by forming gas annealing (FGA) at temperature and hydrogen content of 450 degrees C and 15%, respectively. The application of the optimized LBC of rear-surface-passivated by a dot of fine pattern screen(-) printed aluminum paste resulted in efficiency of up to 19.98% for the p-type czochralski (CZ) silicon wafers with 10.24 cm2 cell size at 649 mV open circuit voltage. By FGA for rear-surface passivation recovery, efficiencies up to 20.35% with a V(OC) of 662 mV, FF of 82%, and J(SC) of 37.5 mA/cm2 were demonstrated.

Cloning, expression, and characterization of single-chain variable fragment antibody against mycotoxin deoxynivalenol in recombinant Escherichia coli.

Deoxynivalenol (DON), a mycotoxin produced by several Fusarium species, is a worldwide contaminant of food and feedstuffs. The DON-specific single-chain variable fragment (scFv) antibody was produced in recombinant Escherichia coli. The variable regions of the heavy chain (V(H)) and light chain (V(L)) cloned from the hybridoma 3G7 were connected with a flexible linker using an overlap extension polymerase chain reaction. Nucleotide sequence analysis revealed that the anti-DON V(H) was a member of the V(H) III gene family IA subgroup and the V(L) gene belonged to the Vlambda gene family II subgroup. Extensive efforts to express the functional scFv antibody in E. coli have been made by using gene fusion and chaperone coexpression. Coexpression of the molecular chaperones (DnaK-DnaJ-GrpE) allowed soluble expression of the scFv. The scFv antibody fused with hexahistidine residues at the C-terminus was purified by immobilized metal affinity chromatography (IMAC). Soluble scFv antibody produced in this manner was characterized for its antigen-binding characteristics. Its biological affinity as antibody was measured by surface plasmon resonance (SPR) analysis and proved to be significant but weaker than that of the whole anti-DON mAb.