Fast Discrimination Research Articles

Optimization of fast neutron and X-ray pulse shape discrimination (PSD) using an EJ276G scintillator in high radiation mixed conditions

Organic crystalline, liquid, and plastic scintillators with low Z-number materials are commonly used in fast neutron detectors. Pulse shape discrimination (PSD) is used to distinguish neutrons from X-rays because these detectors are affected by X-rays due to Compton scattering during fast neutron measurement. PSD was performed with a 1 × 1 × 3 cm3 EJ276G plastic scintillator, SiPM, and DAQ (10-bit resolution, 400 MHz) using the charge comparison method, which compares the total charge (Q body) and the delayed charge (Q tail) from the peak. Most studies have been conducted under laboratory conditions, such as with 252Cf or 241Am-Be sources. This study will perform PSD in a 15 MeV electron accelerator with a total flux of 6.72 × 1014 #/sec and an X-ray-to-neutron ratio of up to 4071:1. The PSD was optimized before the measurement in high flux conditions because it was difficult to perform PSD due to the pile-up effect in these conditions. Optimization focused on the figure of merit (FoM), which is an index of PSD performance. To distinguish neutrons from X-rays under these conditions, lead shielding is necessary to reduce the X-ray-to-neutron ratio. We performed Monte Carlo N-Particle Transport Code (MCNP6) simulations to determine the optimal shielding thickness. Fast neutrons interact with lead due to its high density, which results in small neutron energy loss and direction changes by scattering. This effect can affect other pixels as noise. We plan to evaluate the neutron images under high X-ray flux conditions by comparing the images of the MCNP6 simulation results and the measurement results.

Fast neutron-gamma discrimination in organic scintillators via convolution neural network

Fast neutron-gamma discrimination in organic scintillators via convolution neural network

Amplification of Activated Near-Infrared Afterglow Luminescence by Introducing Twisted Molecular Geometry for Understanding Neutrophil-Involved Diseases.

Understanding the mechanism and progression of neutrophil-involved diseases (e.g., acute inflammation) is of great importance. However, current available analytical methods neither achieve the real-time monitoring nor provide dynamic information during the pathological processes. Herein, a peroxynitrite (ONOO-) and environmental pH dual-responsive afterglow luminescent nanoprobe is designed and synthesized. In the presence of ONOO- at physiological pH, the nanoprobes show activated near-infrared afterglow luminescence, whose intensity and lasting time can be highly enhanced by introducing the aggregation-induced emission (AIE) effect with a twisted molecular geometry into the system. In vivo studies using three diseased animal models demonstrate that the nanoprobes can sensitively reveal the development process of acute skin inflammation including infiltration of first arrived neutrophils and acidification initiating time, make a fast and accurate discrimination between allergy and inflammation, and rapidly screen the antitumor drugs capable of inducing immunogenic cell death. This work provides an alternative approach and advanced probes permitting precise disease monitoring in real time.

The chronometry of symmetry detection in the lateral occipital (LO) cortex

The lateral occipital cortex (LO) has been shown to code the presence of both vertical and horizontal visual symmetry in dot patterns. However, the specific time window at which LO is causally involved in symmetry encoding has not been investigated. This was assessed using a chronometric transcranial magnetic stimulation (TMS) approach. Participants were presented with a series of dot configurations and instructed to judge whether they were symmetric along the vertical axis or not while receiving a double pulse of TMS over either the right LO (rLO) or the vertex (baseline) at different time windows (ranging from 50 ms to 290 ms from stimulus onset). We found that TMS delivered over the rLO significantly decreased participants’ accuracy in discriminating symmetric from non-symmetric patterns when TMS was applied between 130 ms and 250 ms from stimulus onset, suggesting that LO is causally involved in symmetry perception within this time window. These findings confirm and extend prior neuroimaging and ERP evidence by demonstrating not only that LO is causally involved in symmetry encoding but also that its contribution occurs in a relatively large temporal window, at least in tasks requiring fast discrimination of mirror symmetry in briefly (75 ms) presented patterns as in our study.

A Camel Nose-Inspired Highly Durable Neuromorphic Humidity Sensor with Water Source Locating Capability.

Numerous emerging applications in modern society require humidity sensors that are not only sensitive and specific but also durable and intelligent. However, conventional humidity sensors do not have all of these simultaneously because they require very different or even contradictory design principles. Here, inspired by camel noses, we develop a porous zwitterionic capacitive humidity sensor. Relying on the synergistic effect of a porous structure and good chemical and thermal stabilities of hygroscopic zwitterions, this sensor simultaneously exhibits high sensitivity, discriminability, excellent durability, and, in particular, the highest respond speed among reported capacitive humidity sensors, with demonstrated applications in the fast discrimination between fresh, stale, and dry leaves, high-resolution touchless human-machine interactive input devices, and the real-time monitoring humidity level of a hot industrial exhaust. More importantly, this sensor exhibits typical synapse behaviors such as paired-pulse facilitation due to the strong binding interactions between water and zwitterions. This leads to learning and forgetting features with a tunable memory, thus giving the sensor artificial intelligence and enabling the location of water sources. This work offers a general design principle expected to be applied to develop other high-performance biochemical sensors and the next-generation intelligent sensors with much broader applications.

Discriminating the Interaction Anisotropy in Polymorphs Using Powder Brillouin Light Scattering

Drug product performance is polymorph specific, and it is imperative that solid phase stability be monitored throughout the manufacturing process to ensure final product quality and performance. PXRD remains the gold standard for polymorph identification, but due to a growing interest in continuous manufacturing, a need has emerged for alternative process analytical technologies (PATs) that can provide fast, reliable, and non-destructive polymorph discrimination amenable to in situ process monitoring. Herein we demonstrate an original application of powder Brillouin light scattering (p-BLS) for the discrimination of polymorphic molecular solids. We hypothesize that the anisotropic sound velocities directly reflect the strength and orientation of the intermolecular forces in molecular solids. Redistributing these forces upon polymorphic conversion should thus clearly be reflected in the sound frequency distributions obtained by p-BLS. To test this hypothesis, three model compounds - resorcinol, sulfamerazine and furosemide - were selected. Distinct, polymorph-specific, acoustic frequency distributions were observed, and these p-BLS spectra were interpreted using a hydrogen-bond analysis and energy frameworks calculated from CrystalExplorer. In conclusion, this study clearly demonstrates that the sound frequencies measured in p-BLS are sensitive to the interaction forces in molecular solids, and p-BLS is a novel optical technique capable of reliably discriminating polymorphs. Extending this study further, we fully expect that many pharmaceutically relevant processes – e.g., hydrate formation, co-crystallization, or amorphous instability – could potentially be monitored using p-BLS.

A Water-Stable Tb-MOF As a Rapid, Accurate, and Highly Sensitive Ratiometric Luminescent Sensor for the Discriminative Sensing of Antibiotics and D2O in H2O.

The design and development of self-calibrating ratiometric luminescent sensors for the fast, accurate, and sensitive discrimination and determination of pollutants in wastewater is highly desirable for public and environmental health. Herein, a 3D porous Tb(III)-based metal-organic framework (MOF), {[Tb(HL)(H2O)2]·x(solv)}n (1), was facilely synthesized using a urea-functionalized tetracarboxylate ligand, 5,5'-(((1,4-phenylenebis(azanediyl))bis(carbonyl))bis(azanediyl))diisophthalic acid (H4L). The activated framework showed a good water stability in both aqueous solutions at a wide pH range of 2-14 and simulated antibiotic wastewaters. Interestingly, this Tb-MOF exhibited dual luminescence owing to the partial energy transfer from the antenna H4L to Tb3+. More importantly, activated 1 (1a) that was dispersed in water showed a fast, accurate, and highly sensitive discrimination ability toward antibiotics with a good recyclability, discriminating three different classes of antibiotics from each other via the quenching or enhancement of the luminescence and tuning the emission intensity ratio between the H4L ligand and the Tb3+ center for the first time. Simultaneously, 1a is a ratiometric luminescent sensor for the rapid, accurate, and quantitative discrimination of D2O from H2O. Furthermore, this complex was successfully used for the effective determination of antibiotics and D2O in real water samples. This work indicates that 1a represents the first ever MOF material for the discriminative sensing of antibiotics and D2O in H2O and promotes the practical application of Ln-MOF-based ratiometric luminescent sensors in monitoring water quality and avoiding any major leak situation.

Fast and Accurate Discrimination of Brachiaria brizantha (A.Rich.) Stapf Seeds by Molecular Spectroscopy and Machine Learning

Fast and Accurate Discrimination of <i>Brachiaria brizantha</i> (A.Rich.) Stapf Seeds by Molecular Spectroscopy and Machine Learning

Minimizing the Discrimination Time for Quantum States of an Artificial Atom

Fast discrimination between quantum states of superconducting artificial atoms is an important ingredient for quantum information processing. In circuit quantum electrodynamics, increasing the signal field amplitude in the readout resonator, dispersively coupled to the artificial atom, improves the signal-to-noise ratio and increases the measurement strength. Here we employ this effect over two orders of magnitude in readout power, made possible by the unique combination of a dimer Josephson junction array amplifier with a large dynamic range, and the fact that the readout of our granular aluminum fluxonium artificial atom remained quantum-non-demolition (QND) at relatively large photon numbers in the readout resonator, up to $\overline{n} = 110$. Using Bayesian inference, this allows us to detect quantum jumps faster than the readout resonator response time $2/\kappa$, where $\kappa$ is the bandwidth of the readout resonator.

A fast and effective way for authentication of Dendrobium species: 2DCOS combined with ResNet based on feature bands extracted by spectrum standard deviation

Dendrobium Sw., as a traditional herb and function food with over 1500 years of history, shows a significant effect in improving immunity and fatigue resistance. However, due of course the large number of species and the quality fluctuating in different species, a fast and effective discrimination method is in need. Recently, spectroscopic techniques combined with chemometrics have become an effective method for low-cost and fast analysis in food and herb. Nevertheless, chemometrics method which based on one-dimensional spectral dataset still encounter the difficulty that can not effectively extract useful information from the spectra. Different from one-dimensional spectra, the two-dimensional correlation spectroscopy (2DCOS) can reveal more detail information of the spectral dataset. Moreover, the appearance of convolutional neural network makes the application of deep learning in image recognition faster and more accurate. In this study, a novel method 2DCOS combined with residual convolutional neural network (ResNet) was used to discriminate the 20 species of Dendrobium. Five feature bands were selected based on spectrum standard deviation (SDD) method in NIR and MIR spectra. Moreover, the models based on full band, total five feature bands, and their fusion-bands had been compared. The results showed that two feature bands 1800–450 cm−1 and 2400–1900 cm−1 displayed 100% accuracy in both training set and test set. And also, the accurate discrimination of 10% external validation showed that these models have good generalization ability. In conclusion, 2DCOS combined with ResNet could be an effective and accurate method for classify different Dendrobium species.

Dual-Phase Emission AIEgen with ICT Properties for VOC Chromic Sensing.

In a film-based fluorescence sensor, luminogens are of vital importance since they play the role of probes or indicators. Traditional organic luminogens like pyrene show high luminescence quantum yields in dilute solutions, but their applications are usually limited by the aggregation-caused quenching (ACQ) effect and bad photochemical stability. Thus, this paper reports a novel aggregation-induced emission luminogen (AIEgen) containing both pyrene and o-carborane (CB-PY), which possesses unique dual-phase emission both in solution and solid state and intramolecular charge transfer (ICT) properties, fulfilling the gap between ACQ and AIE compounds. Importantly, the fluorophore presents extraordinary stability that there was almost no attenuation in the emission intensity of CB-PY in the solid state after 4 months of exposure at ambient conditions. It is these merits that make CB-PY exhibit outstanding sensing performances for volatile organic compounds (VOCs), where the fluorescence test strip shows fast, reversible, and visual discrimination of four organic solvents with varied polarities. Moreover, 92#, 95#, and 98# gasolines could be discriminated with CB-PY, showing different colors under UV illumination.

Discrimination of neutrons and gamma-rays in plastic scintillator based on falling-edge percentage slope method

With the development of neutron detection technology, the demand for real-time and fast neutron gamma discrimination has become of increasing importance. In this study, neutron and gamma pulse signals measured by an EJ299-33A plastic scintillator detector were analyzed via Matlab off-line. This study compares the effects of seven fast neutron and gamma (n/γ) discrimination methods; (i) charge comparison method, (ii) time-domain oriented method, (iii) amplitude oriented method, (iv) pulse time–amplitude two types of width method, (v) angle difference method, (vi) falling-edge numerical integral method, and, lastly, (vii) falling-edge percentage slope method proposed in this study. We verified that filtering and amplitude normalization improve the discrimination quality but consume much time that accounts for 99.6% of the total discrimination time. The proposed method was compared with other six methods in two aspects: (i) the figure of merit (FoM), (ii) the discrimination time. The FoM value and time consumption of the falling-edge percentage slope method with amplitude normalization are 1.26 and 0.0621s, respectively, while an unnormalized FoM value is 1.02. Experimental results showed that compared with all aforementioned methods, the FOM value of our proposed method is the second high followed the charge comparison method, retaining a short discrimination time which is only half of that of the charge comparison method. This relatively short time consuming is on account of its low volume of data storage and calculation. Generally, the falling-edge percentage slope method has a good discrimination performance while consumes little time, which is capable to be implemented on real-time discrimination applications.

Novel Data Science Driven Chemical Agent Sensors: Towards Better Discrimination in Complex Environments

Chemical agents remain a persistent threat to both Warfighters and civilian personnel in both combat and non-combat environments. While many useful detection technologies exist to provide early warning, many of them suffer from similar drawbacks. Most common are unacceptable false positive response rates and inability to discriminate target analytes in complex or obscurant-laden environments. A typical approach to this problem is to develop material solutions for more specific and sensitive sensors. While useful, these solutions are often too expensive, not practical, or non-deployable. Our approach fuses both material and data science to extend the power of multiplexed sensor arrays towards more accurate and faster detection and discrimination.This presentation will discuss our approach towards vapor analyte detection using multiplex array systems combined with novel data science approaches. For vapor phase detection, sorptive and conductive graphene-based nanocomposite polymer films as well as select 2D conductive materials are used for semi-selective affinity towards chemical agent simulants in binary mixtures with common obscurants. Response data are pre-processed using a novel algorithm called the Variational Autoencoder Kalman Filter and the entire response dataset is used to train neural networks for analyte discrimination. To inform the neural networks to facilitate accurate and fast analyte discrimination, datasets far exceeding the capability of laboratory measurements are needed. Here too we describe our approach to generate simulated data to supplement and augment the classification algorithm machine learning process. We lastly demonstrate this approach to discriminate binary and tertiary mixtures of analytes with both similar and different chemical similarity both in and out of the precise of common environmental interferents.

UV spectroscopy and HPLC combined with chemometrics for rapid discrimination and quantification of Curcumae Rhizoma from three botanical origins

Curcumae Rhizoma (Ezhu in Chinese) is a multi-origin herbal medicine with excellent clinical efficacy. For fast discrimination and quantification analysis of Ezhu from three botanical origins (Curcuma kwangsiensis, Curcuma phaeocaulis, and Curcuma wenyujin), ultra-violet (UV) spectroscopy and high performance liquid chromatography (HPLC) combined with chemometric tools were employed in this study. Firstly, the analysis method for the simultaneous determination of eleven compounds in Ezhu was developed by HPLC, and the UV spectra of thirty-eight batches of Ezhu were acquired. Then, principal component analysis (PCA), an unsupervised pattern recognition method, was applied on the HPLC and UV spectral data. PCA did not show a clear separation between C. phaeocaulis and C. wenyujin samples with HPLC data. By contrast, the supervised techniques, decision tree (DT) and linear discriminant analysis (LDA), achieved the complete discrimination for the three species of Ezhu with 100 % correct classification rate (CCR), showing excellent performance. Based on UV spectral data, PCA presented good performance for discriminating the three species of Ezhu. LDA, support vector machine (SVM) and k-nearest neighbors (KNN) models provided 96.3 % CCR for the calibration set and 100 % CCR for the validation set. Moreover, the partial least squares (PLS) and back propagation-artificial neural network (BP-ANN) quantitative models established on UV spectral data were satisfactory in predicting the contents of zederone, curdione and 3,5-dihydroxy-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-heptane. The residual predictive deviation (RPD) for zederone, curdione and 3,5-dihydroxy-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-heptane of PLS models were 3.169, 1.502 and 1.735, and that of BP-ANN models were 3.467, 2.481 and 2.370, respectively. The present work proposed a rapid and reliable method for the discrimination of Ezhu from three botanical origins and the prediction of zederone, curdione and 3,5-dihydroxy-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-heptane contents in Ezhu, which will help a lot in the quality control of Ezhu and other multi-origin herbal medicines.

The role of cGMP signalling in auditory processing in health and disease.

cGMP is generated by the cGMP-forming guanylyl cyclases (GCs), the intracellular nitric oxide (NO)-sensitive (soluble) guanylyl cyclase (sGC) and transmembrane GC (e.g. GC-A and GC-B). In summarizing the particular role of cGMP signalling for hearing, we show that GC generally do not interfere significantly with basic hearing function but rather sustain a healthy state for proper temporal coding, fast discrimination and adjustments during injury. sGC is critical for the integrity of the first synapse in the ascending auditory pathway, the inner hair cell synapse. GC-A promotes hair cell stability under stressful conditions such as acoustic trauma or ageing. GC-B plays a role in the development of efferent feed-back and gain control. Regarding the crucial role hearing has for language development, speech discrimination and cognitive brain functions, differential pharmaceutical targeting of GCs offers therapeutic promise for the restoration of hearing. LINKED ARTICLES: This article is part of a themed issue on cGMP Signalling in Cell Growth and Survival. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v179.11/issuetoc.

HPLC fingerprint and UV–Vis spectroscopy coupled with chemometrics for Curcumae radix species discrimination and three bioactive compounds prediction

Curcumae Radix with four botanical origins (Wenyujin, Huangyujin, Guiyujin, Lvyujin) has a long medical history in China. The four species have not been differentiated well in clinical application, though there were differences in pharmacological effects among them. For the aim of fast discrimination and quantitative analysis of Yujin, HPLC (high performance liquid chromatography) fingerprint and UV–Vis (ultraviolet–visible) spectroscopy technologies coupled with chemometrics were employed. The HPLC fingerprints of 26 Guiyujin, 11 Huangyujin, 11 Lvyujin, 29 Wenyujin and full 77 samples were separately established, with good similarities among same species except Wenyujin and poor consistence in full samples. UV–Vis spectra showed obvious three absorption peaks in the spectra of Huangyujin samples and only one in that of the rest samples. LDA (linear discriminant analysis) and Bagged Trees models established based on HPLC fingerprint data attained higher than 90% discrimination accuracy. Based on the spectral data, complete separation for the four species of Yujin was failed to achieve by PCA (principal component analysis), and the KNN (k-nearest neighbors) and SVM (support vector machine) models achieved no<90% discrimination accuracies. Furthermore, the SVMR (support vector machine regression) and Bagged Trees Regression models used to quickly predict the contents of germacrone, curcumenol and curdione performed well, with relatively lower RMSE (root mean square error) and higher R2 (coefficient of determination) values. The present study proposed relatively rapid methods for the determination of bioactive compounds and discrimination of Yujin.

MALDI-TOF mass spectrometry–based identification of Eurotiales from different substrates and locations in Brazil

We used matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) fingerprinting to identify 95 strains of Aspergillus, Penicillium, and Talaromyces isolated from soil, marine sediment, and plants in Brazil. Samples for mass spectrometry were composed of conidia and conidiophores directly applied to the target plate and co-crystallized with the matrix solution. Cluster analyses of MS data and phylogenetic analyses based on the second largest subunit of RNA polymerase II gene sequence (RPB2) supported the identification of 42 species in Aspergillus (n = 17), Penicillium (n = 16), and Talaromyces (n = 9). In few cases, MALDI-TOF dendrograms split strains from one species into two neighboring clusters, but strains from different species were never grouped together. Three Penicillium species delimited by MALDI-TOF MS did not correspond to known species present in the large RPB2 dataset available for this group and may represent new taxa. These results expand the scope of applications of MALDI-TOF MS to the task of screening phylogenetically diverse assemblages of morphologically similar strains derived from field collections, allowing for fast discrimination of common and rare species, and detection of possible new taxa.

Dermal Penetration Analysis of Curcumin in an ex vivo Porcine Ear Model Using Epifluorescence Microscopy and Digital Image Processing

Introduction: Curcumin is a promising drug candidate, but its use for dermal application is limited due to its poor aqueous solubility. Thus, formulations that increase the solubility of curcumin are needed to fully exploit the therapeutic potential of curcumin. Various previous studies address this issue, but a comparison of the efficacy between these formulations remains difficult. The reason for this is a missing standard formulation as benchmark control and an easy-to-use skin penetration model that allows for a fast discrimination between different formulations. Objective: Thus, the aims of this study were the development of a curcumin standard formulation and a screening tool that allows for a fast discrimination between the dermal penetration efficacies of curcumin from different formulations. Methods: Ethanolic curcumin solutions were selected as simple and easy to produce standard formulations, and the ex vivo porcine ear model, coupled with epifluorescence microscopy and subsequent digital image analysis, was utilized to determine the dermal penetration efficacy of curcumin from the different formulations. Results: Results show that the utilized skin penetration model is a suitable and versatile tool that enables not only a fast determination of the dermal penetration efficacy of curcumin from different formulations but also a detailed and mechanistic information on the fate of chemical compounds after dermal penetration. Ethanolic solutions containing 0.25% curcumin were found to be the most suitable standard formulation. Conclusions: Results of the study provide a new, effective screening tool for the development of dermal formulations for improved dermal delivery of curcumin.

A Novel Synchronous DGs Islanding Detection Method based on Online Dynamic Features Extraction

Reliable and fast state discrimination are among the most important requirements in islanding detection methods (IDMs) to avoid islanded operation problems of distributed generation (DG) systems. This paper proposes a novel, IDM based on the dynamic response characteristics of synchronous DGs. The proposed method is based on four features of the dynamic behavior of synchronous DGs, namely, participation matrix; zero mode; oscillation frequency; and damping factor. This allows to distinguish between islanding conditions and grid-connected disturbances, even when demand and generation are closely matched. These features are extracted in practice from local measurements using Hilbert-Huang transform (HHT) technique. Moreover, a new method for online observation of the participation matrix based on online power system measurements is proposed. The impacts of synchronous DG controllers and motor loads are considered in the proposed method. Different scenarios such as load shedding, load restoration, capacitor switching, and short circuit faults have been simulated to assess the performance of the proposed method. Simulations are performed on a typical distribution system with real data for different scenarios to verify the high speed performance, reliability, robustness, and applicability of the proposed islanding detection algorithm. Comparison with alternative islanding detection methods prove the superiority of the proposed approach.

Boronic acid-containing carbon dots array for sensitive identification of glycoproteins and cancer cells

Discrimination of glycoproteins and cell types is a significant but difficult issue. Herein, we presented a novel fluorescence sensor array for the detection and identification of glycoproteins and cancer cells based on the specific affinity between boronic acid-containing carbon dots (BA-CDs) and cis-diol residues of polysaccharides. The differential binding affinity of three BA-CDs to various glycoproteins resulted in a different fluorescence turn-on signal pattern caused by aggregation-enhanced emission (AEE), along with negligible response from other proteins. Therefore, BA-CDs encompassing sensing elements and signal indicator into one can enable a fast and accurate discrimination of glycoproteins with simple and easy operation. Seven glycoproteins could be well discriminated at a very low concentration of 10 nmol/L. The discriminating capability of glycoproteins is not sacrificed in both human urine and serum. Notably, different glycoprotein compositions of cancer cells provide more recognizable features for identification of cancer cells, comparing to the total protein. Five cell types could be identified in 15 min at a low concentration of 1000 cells/mL. This method is fast, accurate, and easy operation, and has a potential application in cancer diagnosis.