Quantification Of Different Types Research Articles

A Novel Model for Instance Segmentation and Quantification of Bridge Surface Cracks-The YOLOv8-AFPN-MPD-IoU.

Surface cracks are alluded to as one of the early signs of potential damage to infrastructures. In the same vein, their detection is an imperative task to preserve the structural health and safety of bridges. Human-based visual inspection is acknowledged as the most prevalent means of assessing infrastructures' performance conditions. Nonetheless, it is unreliable, tedious, hazardous, and labor-intensive. This state of affairs calls for the development of a novel YOLOv8-AFPN-MPD-IoU model for instance segmentation and quantification of bridge surface cracks. Firstly, YOLOv8s-Seg is selected as the backbone network to carry out instance segmentation. In addition, an asymptotic feature pyramid network (AFPN) is incorporated to ameliorate feature fusion and overall performance. Thirdly, the minimum point distance (MPD) is introduced as a loss function as a way to better explore the geometric features of surface cracks. Finally, the middle aisle transformation is amalgamated with Euclidean distance to compute the length and width of segmented cracks. Analytical comparisons reveal that this developed deep learning network surpasses several contemporary models, including YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and Mask-RCNN. The YOLOv8s + AFPN + MPDIoU model attains a precision rate of 90.7%, a recall of 70.4%, an F1-score of 79.27%, mAP50 of 75.3%, and mAP75 of 74.80%. In contrast to alternative models, our proposed approach exhibits enhancements across performance metrics, with the F1-score, mAP50, and mAP75 increasing by a minimum of 0.46%, 1.3%, and 1.4%, respectively. The margin of error in the measurement model calculations is maintained at or below 5%. Therefore, the developed model can serve as a useful tool for the accurate characterization and quantification of different types of bridge surface cracks.

Relationships between rumination and different types of rapid eye movement sleep in patients with chronic insomnia disorder

Background and objectiveRumination, a common factor of chronic insomnia disorder (CID) caused by cognitive-emotional arousal, is associated with an increased amount of rapid eye movement (REM) sleep. However, the specific subtypes, such as phasic REM and tonic REM, that contribute to the increased REM sleep have not been reported. This study aimed to determine the association between rumination and different REM sleep subtypes in patients with CID. MethodsThis study enrolled 35 patients with CID and 27 age- and sex-matched healthy controls. The Immersion-Rumination Questionnaire evaluated participants' rumination, and the Insomnia Severity Index was used to assess insomnia severity. Finally, polysomnography was used to monitor objective sleep quality and quantification of different types of REM. ResultsThe CID patients had higher rumination scores than the healthy controls. They had a shorter REM sleep duration, less phasic REM, a lower percentage of phasic REM time, and a higher percentage of tonic REM time. Spectral analysis revealed that the patients affected by insomnia had higher β power during REM sleep, higher β and σ power during phasic REM sleep, and higher β, and γ power during tonic REM sleep. Partial correlation analysis showed that rumination in the CID patients correlated negatively with the duration of phasic REM sleep. Additionally, rumination correlated negatively with δ power in REM sleep and positively with β power in REM sleep, tonic REM sleep, phasic REM sleep, N3and N2 sleep in the patients with CID. ConclusionThe CID patients had stronger rumination, reduced total and phasic REM sleep, and the stronger rumination was, the shorter phasic REM was and the higher fast (β) wave power in REM sleep.

Multiplexed detection of viral proteases through dual response peptide-assisted nanopore sensing

Owing to their central roles in viral replication, main protease (Mpro) and papain-like protease (PLpro) are promising biomarkers for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, the simultaneous sensing of Mpro and PLpro activities is a valuable approach for SARS-CoV-2 detection. Herein, we developed a dual response peptide probe for simultaneously monitoring the protease activities of Mpro and PLpro using a single aerolysin nanopore. The target proteases can specifically digest the probe at the corresponding cleavage sites, releasing the output peptide fragments composed of different amino acids. These fragments generated clearly distinguishable blocking currents in nanopore recording that can be assigned to each analyte, allowing accurate differentiation among multiple proteases. The approach enabled the sensitive detection of Mpro and PLpro activity without the need for any amplification or labeling. Furthermore, with excellent discrimination ability, the method can be applied to evaluate the activity of the two model proteases against a background of exhaled breath condensate. Importantly, since peptides with specific sequences serve as catalytic substrates of multiple enzymes, the proposed strategy would be applicable for accurate and multiplex quantification of different types of enzymes, which is critical for non-invasive early disease diagnosis.

Gait disorders induced by photothrombotic cerebellar stroke in mice

Patients with cerebellar stroke display relatively mild ataxic gaits. These motor deficits often improve dramatically; however, the neural mechanisms of this improvement have yet to be elucidated. Previous studies in mouse models of gait ataxia, such as ho15J mice and cbln1-null mice, have shown that they have a dysfunction of parallel fiber-Purkinje cell synapses in the cerebellum. However, the effects of cerebellar stroke on the locomotor kinematics of wild-type mice are currently unknown. Here, we performed a kinematic analysis of gait ataxia caused by a photothrombotic stroke in the medial, vermal, and intermediate regions of the cerebellum of wild-type mice. We used the data and observations from this analysis to develop a model that will allow locomotive prognosis and indicate potential treatment regimens following a cerebellar stroke. Our analysis showed that mice performed poorly in a ladder rung test after a stroke. During walking on a treadmill, the mice with induced cerebellar stroke had an increased duty ratio of the hindlimb caused by shortened duration of the swing phase. Overall, our findings suggest that photothrombotic cerebellar infarction and kinematic gait analyses will provide a useful model for quantification of different types of acute management of cerebellar stroke in rodents.

Digital PCR: a tool in clostridial mutant selection and detection.

The ClosTron mutagenesis system has enabled researchers to efficiently edit the clostridial genome. Since site-specific insertion of the mobile ClosTron insert may cause errors, validation is key. In this paper we describe the use of digital PCR (dPCR) as an alternative tool in selecting clostridial mutant strains. Clostridium perfringens chitinase mutant strains were constructed in which the mobile ClosTron intron was inserted into one of the chitinase genes. On-target insertion of the mobile intron was validated through conventional PCR. In order to confirm the absence of off-target insertions, dPCR was used to determine the amount of the ClosTron intron as well as the amount of a reference gene, located in close proximity to the interrupted gene. Subsequently, mutant strains containing an equivalent amount of both genes were selected as these do not contain additional off-target mobile ClosTron inserts. The outcome of this selection procedure was confirmed through a validated PCR-based approach. In addition to its application in mutant selection, dPCR can be used in other aspects of clostridial research, such as the distinction and easy quantification of different types of strains (wildtype vs. mutant) in complex matrices, such as faecal samples, a process in which other techniques are hampered by bacterial overgrowth (plating) or inhibition by matrix contaminants (qPCR). This research demonstrates that dPCR is indeed a high-throughput method in the selection of clostridial insertion mutants as well as a robust and accurate tool in distinguishing between wildtype and mutant C. perfringens strains, even in a complex matrix such as faeces. KEY POINTS: • Digital PCR as an alternative in ClosTron mutant selection • Digital PCR is an accurate tool in bacterial quantification in a complex matrix • Digital PCR is an alternative tool with great potential to microbiological research.

Identification of fish spermatogenic cells through high-throughput immunofluorescence against testis with an antibody set.

Image-based identification and quantification of different types of spermatogenic cells is of great importance, not only for reproductive studies but also for genetic breeding. Here, we have developed antibodies against spermatogenesis-related proteins in zebrafish (Danio rerio), including Ddx4, Piwil1, Sycp3, and Pcna, and a high-throughput method for immunofluorescence analysis of zebrafish testicular sections. By immunofluorescence analysis of zebrafish testes, our results demonstrate that the expression of Ddx4 decreases progressively during spermatogenesis, Piwil1 is strongly expressed in type A spermatogonia and moderately expressed in type B spermatogonia, and Sycp3 has distinct expression patterns in different subtypes of spermatocytes. Additionally, we observed polar expression of Sycp3 and Pcna in primary spermatocytes at the leptotene stage. By a triple staining of Ddx4, Sycp3, and Pcna, different types/subtypes of spermatogenic cells were easily characterized. We further demonstrated the practicality of our antibodies in other fish species, including Chinese rare minnow (Gobiocypris rarus), common carp (Cyprinus carpio), blunt snout bream (Megalobrama amblycephala), rice field eel (Monopterus albus) and grass carp (Ctenopharyngodon idella). Finally, we proposed an integrated criterion for identifying different types/subtypes of spermatogenic cells in zebrafish and other fishes using this high-throughput immunofluorescence approach based on these antibodies. Therefore, our study provides a simple, practical, and efficient tool for the study of spermatogenesis in fish species.

Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification.

Chemical reaction neural network (CRNN), a recently developed tool for autonomous discovery of reaction models, has been successfully demonstrated on a variety of chemical engineering and biochemical systems. It leverages the extraordinary data-fitting capacity of modern deep neural networks (DNNs) while preserving high interpretability and robustness by embedding widely applicable physical laws such as the law of mass action and the Arrhenius law. In this paper, we further developed Bayesian CRNN to not only reconstruct but also quantify the uncertainty of chemical kinetic models from data. Two methods, the Markov chain Monte Carlo algorithm and variational inference, were used to perform the Bayesian CRNN, with the latter mainly adopted for its speed. We demonstrated the capability of Bayesian CRNN in the kinetic uncertainty quantification of different types of chemical systems and discussed the importance of embedding physical laws in data-driven modeling. Finally, we discussed the adaptation of Bayesian CRNN for incomplete measurements and model mixing for global uncertainty quantification.

Localization and quantification of different types of defects in composite structures with SMART sensor layers

Localization and quantification of different types of defects in composite structures with SMART sensor layers

Paroxysmal nocturnal haemoglobinuria, diagnosis and haematological findings, first report from Iran, model for developing countries.

Since paroxysmal nocturnal haemoglobinuria (PNH) was first described in 1881, the diagnosis and follow‐up patients diagnosed with the illness has remained an area of concern, with several different techniques of varying sensitivity having been described in the literature for both the diagnosis and monitoring treatment of the disease. PNH is a rare and life‐threatening disease that manifests symptoms of haemolytic anaemia. Hence, a quick and reliable technique for precise diagnosis would be crucial. PNH patients who have previously been diagnosed with aplastic anaemia or myelodysplastic syndrome carry small PNH clones and for more than a century traditional method with low sensitivity was used for such patients. In 2010, the International Clinical Cytometry Society described a highly sensitive method for detection and quantification of different types of PNH clones using multi‐colour flow cytometry. In this method, a three‐colour flow cytometer is essential to detect PNH affected cells amongst monocytes and granulocytes. This started a new era in the diagnosis of patients who carry small clones of PNH cells. Before this, flow cytometric analysis was used only for detection of PNH cells amongst erythrocytes. By using flow cytometry instruments with more light sources, the sensitivity of detection and quantification of PNH clones would be augmented. However, standardisation and crosstalk compensation would be the most concerning issue. For the first time in Iran, we set up and standardised multi‐colour flow cytometry technique to detect PNH cells in erythrocytes and leukocytes at Payvand medical laboratory.

Thermoacoustic modal instability and its suppression with locally resonant flexible membranes

Thermoacoustic oscillation and instability are a common occurrence in many industrial problems. Investigations on the interplay among different types of modes, their evolution process and coupling with add-on control devices are of vital importance to guide the development of proper control strategies. Using a linear heat release n-τ model inside a duct, these issues are investigated in this paper based on a fully coupled energy-based model. Studies allow the classification and quantification of different types of eigen-modes, as well as their stability and controllability features. Using flush-mounted flexible membranes over the duct wall, possible suppressions of unstable modes and the underlying control mechanisms are revealed with particular focus on cases involving small time delay and interaction indices. Numerical analyses show that thermoacoustic instability of the system can be controlled via creating strong vibro-acoustic coupling, via two different physical processes for acoustic and intrinsic modes. For the former, the acoustic modal pressure distribution should be positively altered in the vicinity of the heat source to create a favorable pressure state scenario, in agreement with Rayleigh criterion. For the latter, control is achieved indirectly through a proper alteration of the nearby acoustic modes, thus affecting their coupling with the targeted intrinsic mode. In both cases, a successful suppression of the thermoacoustic modal instability can be materialized through a proper adjustment of the physical parameters of the membranes and their installation locations.

Accelerating photofragmentation UV Spectroscopy–Mass spectrometry fingerprinting for quantification of isomeric peptides

Identification of isomeric biomolecules remains a challenging analytical problem. A recently developed spectroscopic method that combines UV photofragmentation and mass spectrometry for fingerprinting of cold ions (2D UV-MS), has already demonstrated its high performance in the library-based identification and quantification of different types of biomolecular isomers. The practical use of the method has been hindered by a slow rate of data acquisition, which makes the fingerprinting incompatible with high-throughput analysis and online liquid chromatography (LC) separation. Herein we demonstrate how the use of a few pre-selected wavelengths can accelerate the method by two orders of magnitude without a significant loss of accuracy. As a proof of principle, 2D UV-MS fingerprinting was coupled to online LC separation and tested for quantification of isomeric peptides containing either Asp or isoAsp residues. The relative concentrations of the peptides mixed in solution have been determined, on average, with better than 4% and 6% accuracy for resolving and non-resolving gradients of LC separation, respectively.

Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning

A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) imaging is a powerful diagnostic method, providing high spatial resolution images of solar cells and modules. EL images allow the identification and quantification of different types of failures, including those in high recombination regions, as well as series resistance-related problems. In this study, almost 46,000 EL cell images are extracted from photovoltaic modules with different defects. We present a method that extracts statistical parameters from the histogram of these images and utilizes them as a feature descriptor. Machine learning algorithms are then trained using this descriptor to classify the detected defects into three categories: (i) cracks (Mode B and C), (ii) micro-cracks (Mode A) and finger failures, and (iii) no failures. By comparing the developed methods with the commonly used one, this study demonstrates that the pre-processing of images into a feature vector of statistical parameters provides a higher classification accuracy than would be obtained by raw images alone. The proposed method can autonomously detect cracks and finger failures, enabling outdoor EL inspection using a drone-mounted system for quick assessments of photovoltaic fields.

Effects of Different n6/n3 PUFAs Dietary Ratio on Cardiac Diabetic Neuropathy.

We studied the influence of experimentally induced DM1, in combination with different dietary n6:n3 polyunsaturated fatty acid (PUFA) ratios on different types of nerve fibers in rat myocardium, in order to reveal whether protective/unfavorable effects of different PUFAs on myocardial function in diabetic patients could be a (partial) repercussion of their effect on the changes in cardiac innervation. The control group (c) and diabetic group (stz) were fed with an n6/n3 ratio of ≈7; the diet of the stz+n6 group had an n6/n3 ratio ≈60, while the diet for the stz+DHA group contained 2.5% of fish oil (containing 16% eicosapentaenoic acid—EPA and 19% docosahexaenoic acid—DHA), n6/n3 ratio of ≈1. DM1 was induced by i.p. injection of streptozotocin (55 mg/kg) and rats were euthanized 30 days after induction. Immunohistochemistry was used for the detection and quantification of different types of neuronal fibers in the cardiac septum. We found changes in cardiac innervations characteristics for the initial phase of experimental DM1, which manifested as an increase in total number and area density of all neuronal fibers, measured by Pgp9.5 immunoreactivity. By detailed analysis, we found that this increase consisted mostly of heavy myelinated NF200 immunoreactive fibers and TH immunoreactive sympathetic fibers, while the density of ChAT immunoreactive parasympathetic fibers decreased. In the deep (middle) part of the myocardium, where rare fibers (of all studied types) were found, significant differences were not found. Surprisingly, we found a more consistent protective effect of n6 PUFAs, in comparison to n3 PUFAs supplementation. These results may provide a better understanding of the potential impacts of different PUFA ratios in the diet of diabetic patients on cardiac innervation and genesis and outcome of diabetic autonomic cardiomyopathy.

On kinetic modeling of change in active sites upon hydrothermal aging of Cu-SSZ-13

Dynamic changes in the state of a commercial Cu-SSZ-13 catalyst as a function of hydrothermal aging are explained through a unified and quantitative theoretical model. NH3 adsorption and desorption rate constants are identified on individual active sites, utilizing NH3-temperature programmed desorption (TPD) experiments on a commercial Cu-SSZ-13 and a model H-SSZ-13 catalyst. NH3 adsorption on Brønsted acid sites is described by a Type-II BET isotherm model to account for NH3 hydrogen bonded to a tetrahedral NH4+ ion. NH3 adsorption on different types of Copper sites is modeled with identical energetics, utilizing a Temkin isotherm model to account for minor site heterogeneity and lateral interactions between adsorbates. In the model, NH3 storage at low temperatures (<200 °C) is captured by a Physisorbed site to account for NH3 bound to extra-framework Al species and additional low temperature adsorption on Copper sites and Brønsted acid sites. The adsorption enthalpies and entropic penalties on individual sites in the kinetic model are consistent with the binding energies and entropies reported from first principles density functional theory (DFT) calculations by Paolucci et al., 2016, and the site-specific storage dynamics follow reported spectroscopic characterizations (Giordanino et al., 2014). Changes in NH3-temperature programmed desorption (TPD) peaks are then used as a probe to identify the transformation of individual active sites as a function of hydrothermal aging time and temperature, assuming fixed site-specific turnover rates (mean field approximation). An Arrhenius correlation is developed for the loss of Brønsted acid sites upon hydrothermal aging, yielding a similar activation energy for the aging process as reported by Luo et al., 2018. The quantification of different types of Copper sites is hypothesized, and the limitations of the mean field approximation at extreme aging temperatures are discussed. The systematic quantification of active sites as a function of hydrothermal age provides a foundation for improved understanding and modeling of the SCR reaction mechanism, and serves as a guide to better catalyst design for stricter durability requirements and lower NOx emissions.

A Real-Time Decision Platform for the Management of Structures and Infrastructures

Natural disasters and the poor management of civil engineering structures and infrastructures require timely action and new tools such as specially designed structural health monitoring platforms. This paper proposes an innovative platform based on a network of wirelessly connected, low-power, and renewable-energy-fed sensor units. The platform is a multipurpose tool for diagnostics, maintenance, and supervision, capable of simultaneously carrying out damage detection, localization, identification, and “multiclass” and “multi-material” level quantification of different types of failures. In addition, it works as a decision support tool for emergency management and post-disaster assessment, here tailored for an Italian theme park. The platform uses innovative algorithms based on the concept of the vibro-acoustic signature of the asset monitored. The vibro-acoustic signatures of the monitored assets are gathered by the microphones and accelerometers of the platform’s sensor units. Then, almost simultaneously, they are analyzed using specifically designed wavelet-based and convolutional-neural-network-based algorithms, which are able to extract crucial information about the structural and environmental conditions of both the asset and the areas of the thematic park. In addition, the platform shows escape routes during an emergency, indicating meeting points and helping people to proceed safely along a recognizable escape route to a safe place, as demonstrated by the simulations.

A screening method based on Visible-NIR spectroscopy for the identification and quantification of different adulterants in high-quality honey

According to European Union regulations, honey is a pure product and adding to or removing from it any kind of substance is illegal. Nevertheless, its adulteration by adding inexpensive and artificial adulterants is a common practice. This paper deals with the use of visible and near-infrared spectroscopy (Vis-NIRS) combined with chemometric tools as a screening technique for the identification and quantification of different types of adulterants (inverted sugar, rice syrup, brown cane sugar and fructose syrup) added to high-quality honey (Granada Protected Designation of Origin, Spain) at different levels (5%–50%). A complete discrimination between non-adulterated and adulterated samples was achieved. A general regression model to quantify the adulteration levels was developed as well as specific models for each adulterant. The coefficients of determination were higher than 0.96 for all the models. These results demonstrate the capacity of Vis-NIRS combined with chemometric tools for honey quality control.

Calibration curve and support vector regression methods applied for quantification of cement raw meal using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) is a qualitative and quantitative analytical technique with great potential in the cement industrial analysis. Calibration curve (CC) and support vector regression (SVR) methods coupled with LIBS technology were applied for the quantification of three types of cement raw meal samples to compare their analytical concentration range and the ability to reduce matrix effects, respectively. To reduce the effects of fluctuations of the pulse-to-pulse, the unstable ablation and improve the reproducibility, all of the analysis line intensities were normalized on a per-detector basis. The prediction results of the elements of interest in the three types of samples, Ca, Si, Fe, Al, Mg, Na, K and Ti, were compared with the results of the wet chemical analysis. The average relative error (ARE), relative standard deviation (RSD) and root mean squared error of prediction (RMSEP) were employed to investigate and evaluate the prediction accuracy and stability of the two prediction methods. The maximum average ARE of the CC and SVR methods is 34.62% instead of 6.13%, RSD is 40.89% instead of 7.60% and RMSEP is 1.34% instead of 0.43%. The results show that SVR method can accurately analyze samples within a wider concentration range and reduce the matrix effects, and LIBS coupled with it for a rapid, stable and accurate quantification of different types of cement raw meal samples is promising.

Analysis of the reproductive stage and ovarian histomorphometry of Dermanura cinerea (Chiroptera: Phyllostomidae) in an Atlantic Forest fragment of Pernambuco, northeastern Brazil

ABSTRACT: This study aimed to analyze the reproductive stage, histology and morphometry of the ovary of Dermanura cinerea in an Atlantic Forest fragments in the Biological Reserve of Saltinho, Pernambuco, Brazil. Adult females were captured monthly by mist net, during two consecutive nights from June/2014 to November/2015. The meteorological data were provided by the National Institute of Meteorology and grouped together with the reproductive data, in six periods: period I (June to August/2014), period II (September to November/2014), period III (December/2014 to February/2015), period IV (March to May/2015), period V (June to August/2015) and period VI (September to November/2015). The females were classified according to their reproductive stages in: Inactive, pregnant, lactating and postlactating. Among the 25 females caught, 10 inactive females were selected for the histomorphometric analysis of the ovaries and then they were euthanized. After that, the specimens had their ovaries removed, fixed, processed and included in Paraplast. The organs were cut and stained by Hematoxylin - Eosin and the histological slides produced were submitted to the appropriate optical microscope analysis. Each type of ovarian follicle was estimated according to the histological characteristics evidenced for other mammals. In the morphometric analysis, it was considered: The area of the ovary (measured in square millimeter), Quantification of different types of ovarian follicles (unilaminar and multilaminar primary follicles, antral and matures) and corpus luteum, as well as the area of each by those different follicles (measured in square micrometer) The results indicated that 16 females were inactive, 5 were lactating, 4 were pregnant and no post-lactating. The histological analysis revealed that the ovaries of D. cinerea exhibit characteristics, structures and cellular types similar to other mammals. The morphometric data of the study area were statistically evaluated using the Student’s t-test. Therefore, ovarian histomorphometric analysis are very important to infer with greater certainty about the reproduction of a species. Thus, it was observed that females of D. cinerea exhibit two reproductive peaks, that is, they can give birth either at the end of dry periods or from the beginning to the middle of the rainy season. The main feature observed in the ovary of this species, found in the Atlantic Forest area of the state of Pernambuco, is the preparation of the ovarian cortex to ovulate in months of high rainfall throughout the year. Therefore, this species tries to adjust its reproductive period with the variations of the environmental conditions, so that the reproduction might occur during the period more favorable for the maintenance of their offspring.

Reply to the comment by M. Genge and M. Van Ginneken on paper entitled “Unmelted cosmic metal particles from the Indian Ocean”

Reply to the comment by M. Genge and M. Van Ginneken on paper entitled “Unmelted cosmic metal particles from the Indian Ocean”

Dual Quantification of MicroRNAs and Telomerase in Living Cells.

The development of a unique and universal strategy for the simultaneous quantification of different types of biomolecules (i.e., nucleic acids and proteins) in living cells is extremely challenging. Herein, a two-signal platform, based upon surface-enhanced Raman scattering and upconversion, for the ultrasensitive and quantitative in situ detection of microRNA (miR)-21 and telomerase in living cells is reported. In the presence of miR-21 and telomerase, the hybridization of miR-21 with a molecular beacon leads to the separation of 3,3'-diethylthiocarbamyl cyanine iodide-modified Au NR dimers, resulting in a decrease in Raman signal. Also, the target telomerase triggers elongation of the telomerase primer strands, followed by substitutional hybridization and release of upconversion nanoparticles, leading to an increase in luminescence. A linear relationship between the Raman intensities and logarithmic concentration of intracellular miR-21 between 0.021 and 22.36 amol/ngRNA is observed, and the limit of detection (LOD) was determined to be 0.011 amol/ngRNA. The luminescence data show a linear response between 0.6 × 10-12 and 31 × 10-12 IU for logarithmic concentration of intracellular telomerase with a LOD of 3.2 × 10-13 IU. These results are in good agreement with Raman and confocal imaging. Importantly, the ultrasensitive detection of miR-21 was possible due to strong plasmonic "hot spots". This innovative two-signal approach can be utilized for the quantitative and precise detection of many types of signaling molecules in living cells and to understand the chemistry within cellular systems and its application in the diagnosis of disease.