Plant signaling molecules can be divided into plant messenger signaling molecules (such as calcium ions, hydrogen peroxide, Nitric oxide) and plant hormone signaling molecules (such as auxin (mainly indole-3-acetic acid or IAA), salicylic acid, abscisic acid, cytokinin, jasmonic acid or methyl jasmonate, gibberellins, brassinosteroids, strigolactone, and ethylene), which play crucial roles in regulating plant growth and development, and response to the environment. Due to the important roles of the plant signaling molecules in the plants, many methods were developed to detect them. The development of in-situ and real-time detection of plant signaling molecules and field-deployable sensors will be a key breakthrough for botanical research and agricultural technology. Electrochemical methods provide convenient methods for in-situ and real-time detection of plant signaling molecules in plants because of their easy operation, high sensitivity, and high selectivity. This article comprehensively reviews the research on electrochemical detection of plant signaling molecules reported in the past decade, which summarizes the various types electrodes of electrochemical sensors and the applications of multiple nanomaterials to enhance electrode detection selectivity and sensitivity. This review also provides examples to introduce the current research trends in electrochemical detection, and highlights the applicability and innovation of electrochemical sensors such as miniaturization, non-invasive, long-term stability, integration, automation, and intelligence in the future. In all, the electrochemical sensors can realize in-situ, real-time and intelligent acquisition of dynamic changes in plant signaling molecules in plants, which is of great significance for promoting basic research in botany and the development of intelligent agriculture.

UNASSIGNED: MUC16 is a commonly employed biomarker to identify and predict ovarian cancer (OC). Precise measurement of MUC16 levels is essential for the accurate diagnosis, prediction, and management of OC. This research seeks to introduce a new surface plasmon resonance (SPR) biosensor design that utilizes aptamer-based technology to enable the sensitive and real-time detection of MUC16.
UNASSIGNED: In this study, the sensor chip was immobilized with an anti-MUC16 aptamer (Ap) by utilizing 11-mercaptoundecanoic acid (MUA) as a linker to attach the amine-terminated Ap to the chip using EDC/NHS chemistry.
UNASSIGNED: The results indicated that the newly created aptasensor had a detection limit of 0.03 U/mL for MUC16 concentration, with a linear range of 0.09 to 0.27 U/mL. The findings demonstrate good precision and accuracy (<15%) for each MUC16 concentration, with recoveries ranging from 93% to 96%. Additionally, the aptasensor exhibited high selectivity, good repeatability, stability, and applicability in real human serum samples, indicating its potential as a valuable tool for the diagnosis and treatment of OC.
UNASSIGNED: According to the outcomes, the designed aptasensor exhibited acceptable specificity to detect the CA125 antigen and could be utilized for the serum detection of target antigen by SPR method.

Most real-time semantic segmentation networks use shallow architectures to achieve fast inference speeds. This approach, however, limits a network\'s receptive field. Concurrently, feature information extraction is restricted to a single scale, which reduces the network\'s ability to generalize and maintain robustness. Furthermore, loss of image spatial details negatively impacts segmentation accuracy. To address these limitations, this paper proposes a Multiscale Context Pyramid Pooling and Spatial Detail Enhancement Network (BMSeNet). First, to address the limitation of singular semantic feature scales, a Multiscale Context Pyramid Pooling Module (MSCPPM) is introduced. By leveraging various pooling operations, this module efficiently enlarges the receptive field and better aggregates multiscale contextual information. Moreover, a Spatial Detail Enhancement Module (SDEM) is designed, to effectively compensate for lost spatial detail information and significantly enhance the perception of spatial details. Finally, a Bilateral Attention Fusion Module (BAFM) is proposed. This module leverages pixel positional correlations to guide the network in assigning appropriate weights to the features extracted from the two branches, effectively merging the feature information of both branches. Extensive experiments were conducted on the Cityscapes and CamVid datasets. Experimental results show that the proposed BMSeNet achieves a good balance between inference speed and segmentation accuracy, outperforming some state-of-the-art real-time semantic segmentation methods.

The detection of ammonia levels in blood is critical for diagnosing and monitoring various medical conditions, including liver dysfunction and metabolic disorders. However, traditional diagnostic methods are slow and cumbersome, often involving multiple contact-based steps such as ammonia separation in alkali conditions followed by distillation or microdiffusion, leading to delays in diagnosis and treatment. Herein, we developed a colorimetric assay capable of rapid detection of ammonia in whole blood or plasma samples, utilizing 2,2,6,6-tetramethylpiperidine 1-oxyl (TEMPO)-oxidized cellulose nanocrystals (TCNC) coupled with gold nanoparticles (AuNPs). The basis of our assay relies on either (i) the interaction between the carboxylate group (-COO) of TEMPO and ammonium ions or (ii) the manipulation of AuNPs surface plasmon resonance (SPR) through the formation of Au(NH3)43+, which displaces a redox mediator, resazurin, resulting in observable multicolor displays at various concentrations of ammonia. The colorimetric assay exhibits a wide linear detection range for dissolved NH4+ (0.1-37 μM) with a low limit of detection (LOD) of 0.1 μM. Additionally, it effectively measures NH3(g) concentrations in the range of 0.5-144 μM. The fabricated electrochemical nose (E-nose) device demonstrates excellent analytical performance for plasma ammonia sensing (0.05-256 μM). Experimental results demonstrate a linear detection range suitable for clinical applications, with excellent correlation to standard laboratory methods, offering a practical solution for point-of-care (PoC) testing. We anticipate that this approach can be applied broadly to improve patient monitoring and treatment by providing immediate and accurate ammonia measurements in a clinical setting.

This paper presents an implementation of an architecture based on open-source solutions using ELK Stack - Elasticsearch, Logstash, and Kibana - for real-time data analysis and visualizations in the Medical Data Integration Center, University Hospital Cologne, Germany. The architecture addresses challenges in handling diverse data sources, ensuring standardized access, and facilitating seamless analysis in real-time, ultimately enhancing the precision, speed, and quality of monitoring processes within the medical informatics domain.

Objective.To treat neurological and psychiatric diseases with deep brain stimulation (DBS), a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient method to quickly and automatically search for optimal parameters. However, conventional Bayesian optimization does not account for patient safety and could trigger unwanted or dangerous side-effects.Approach.In this study we develop SAFE-OPT, a Bayesian optimization algorithm designed to learn subject-specific safety constraints to avoid potentially harmful stimulation settings during optimization. We prototype and validate SAFE-OPT using a rodent multielectrode stimulation paradigm which causes subject-specific performance deficits in a spatial memory task. We first use data from an initial cohort of subjects to build a simulation where we design the best SAFE-OPT configuration for safe and accurate searchingin silico. Main results.We then deploy both SAFE-OPT and conventional Bayesian optimization without safety constraints in new subjectsin vivo, showing that SAFE-OPT can find an optimally high stimulation amplitude that does not harm task performance with comparable sample efficiency to Bayesian optimization and without selecting amplitude values that exceed the subject\'s safety threshold.Significance.The incorporation of safety constraints will provide a key step for adopting Bayesian optimization in real-world applications of DBS.

Monitoring of the introduction of unapproved genetically modified (GM) events is required because the approval status of a GM event may differ from country to country. The on-site methods such as loop-mediated isothermal amplification (LAMP) offer a technological answer for the rapid GM detection beyond the laboratories. Real-time LAMP assays detecting one GM target were reported earlier. To increase the efficiency of the assay, a multiplex real-time LAMP simultaneously targeting Figwort Mosaic Virus promoter (P-FMV) that constructs region between the Cauliflower Mosaic Virus 35S promoter and cry1Ac gene (p35S-cry1Ac) and neomycin phosphotransferase II (nptII) marker gene was developed. The assay could detect as low as 0.1% for each GM target within 45 min. To the best of our knowledge, multiplexing in real-time LAMP using the Genie II system with applicability in GM detection has been reported herein for the first time. The developed method provides rapid, on-site, and real-time GM detection in seeds and food products.

Artificial intelligence has been employed to simulate and optimize the performance of membrane capacitive deionization (MCDI), an emerging ion separation process. However, a real-time control for optimal MCDI operation has not been investigated yet. In this study, we aimed to develop a reinforcement learning (RL)-based control model and investigate the model to find an energy-efficient MCDI operation strategy. To fulfill the objectives, we established three long-short term memory models to predict applied voltage, outflow pH, and outflow electrical conductivity. Also, four RL agents were trained to minimize outflow concentration and energy consumption simultaneously. Consequently, actor-critic (A2C) and proximal policy optimization (PPO2) achieved the ion separation goal (<0.8 mS/cm) as they determined the electrical current and pump speed to be low. Particularly, A2C kept the parameters consistent in charging MCDI, which caused lower energy consumption (0.0128 kWh/m3) than PPO2 (0.0363 kWh/m3). To understand the decision-making process of A2C, the Shapley additive explanation based on the decision tree model estimated the influence of input parameters on the control parameters. The results of this study demonstrate the feasibility of RL-based controls in MCDI operations. Thus, we expect that the RL-based control model can improve further and enhance the efficiency of water treatment technologies.

OBJECTIVE: Clinical needle insertion into tissue, commonly assisted by 2D ultrasound imaging for real-time navigation, faces the challenge of precise needle and probe alignment to reduce out-of-plane movement. Recent studies investigate 3D ultrasound imaging together with deep learning to overcome this problem, focusing on acquiring high-resolution images to create optimal conditions for needle tip detection. However, high-resolution also requires a lot of time for image acquisition and processing, which limits the real-time capability. Therefore, we aim to maximize the US volume rate with the trade-off of low image resolution. We propose a deep learning approach to directly extract the 3D needle tip position from sparsely sampled US volumes.
METHODS: We design an experimental setup with a robot inserting a needle into water and chicken liver tissue. In contrast to manual annotation, we assess the needle tip position from the known robot pose. During insertion, we acquire a large data set of low-resolution volumes using a 16  ×  16 element matrix transducer with a volume rate of 4 Hz. We compare the performance of our deep learning approach with conventional needle segmentation.
RESULTS: Our experiments in water and liver show that deep learning outperforms the conventional approach while achieving sub-millimeter accuracy. We achieve mean position errors of 0.54 mm in water and 1.54 mm in liver for deep learning.
CONCLUSIONS: Our study underlines the strengths of deep learning to predict the 3D needle positions from low-resolution ultrasound volumes. This is an important milestone for real-time needle navigation, simplifying the alignment of needle and ultrasound probe and enabling a 3D motion analysis.

While the molten salt-catalyzed chemical vapor deposition (CVD) technique is recognized for its effectiveness in producing large-area transition metal chalcogenides, understanding their growth mechanisms involving alkali metals remains a challenge. Here, we investigate the kinetics and mechanism of sodium-catalyzed molybdenum disulfide (MoS2) growth and etching through image analysis conducted using an integrated CVD microscope. Sodium droplets, agglomerated via the thermal decomposition of the sodium cholate dispersant, catalyze the precipitation of supersaturated MoS2 laminates and induce growth despite fragmentation during this process. Triangular MoS2 crystals display a distinct self-exhausting exponential behavior and slow growth of thermodynamically favorable crystallographic faces, exhibiting a sulfur-dominant pressure. The growth and etching processes are facilitated by the scooting of sodium droplets along grain edges, displaying comparable rates. Leveraging these kinetics makes it possible to engineer atypical MoS2 shapes. This combined microscope not only enhances the understanding of growth mechanisms but also contributes to the facile development of next-generation nanomaterials.