Novel FRET-Based Biosensors for Real-Time Monitoring of Estrogen Receptor Dimerization and Translocation Dynamics in Living Cells.

Estrogen receptors (ERs), comprising ER α and ER β, are crucial for regulating cell growth and differentiation via homo- and hetero-dimer formation. However, accurately detecting ER dimerization with precise spatiotemporal resolution remains a significant challenge. In this study, fluorescence resonance energy transfer-based biosensors to monitor ER dynamics in real-time, are developed and optimized.

ECM stiffness regulates calcium influx into mitochondria via tubulin and VDAC1 activity.

Calcium ions (Ca) play pivotal roles in regulating numerous cellular functions, including metabolism and growth, in normal and cancerous cells. Consequently, Ca signaling is a vital determinant of cell fate and influences both cell survival and death. These intracellular signals are susceptible to modulation by various factors, including changes in the extracellular environment, which leads to mechanical alterations.

Fluorescence-based techniques for investigating estrogen receptor dynamics.

Understanding estrogen receptor (ER) signaling pathways is crucial for uncovering the mechanisms behind estrogen-related diseases, such as breast cancer, and addressing the effects of environmental estrogenic disruptors. Traditionally, ER signaling involves genomic events, including ligand binding, receptor dimerization, and transcriptional modulation within cellular nuclei. However, recent research have revealed ERs also participate in non-genomic signaling pathways, adding complexity to their functions.

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.

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.

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, β/β) or hetero (α/β) dimerization. To date, a comprehensive screening approach for eEDCs affecting all ER dimer forms in live cells is lacking.

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.

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.

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.

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.

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.

Förster Resonance Energy Transfer-Based Single-Cell Imaging Reveals Piezo1-Induced Ca 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 Ca influx driven by channel activation and cellular behavior using novel Förster Resonance Energy Transfer (FRET)-based biosensors and single-cell imaging analysis.

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

, 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 (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.

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 Ca channel, plays a crucial role in intracellular Ca homeostasis. However, it is currently unclear how cell mechanical cues control TRPM7 activity and its associated Ca influx at plasma membrane microdomains. Using two different types of Ca biosensors (Lyn-D3cpv and Kras-D3cpv) based on fluorescence resonance energy transfer, we investigate how Ca influx generated by the TRPM7-specific agonist naltriben is mediated at the detergent-resistant membrane (DRM) and non-DRM regions.

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.

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.

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.