Direct Detection Approach Research Articles

Practical approach to extending baselines of telescopes using continuous-variable quantum information

Abstract Interferometric telescopes are instrumental for the imaging of distant astronomical bodies, but optical loss heavily restricts how far telescopes in an array can be placed from one another, leading to a bottleneck in the resolution that can be achieved. An entanglement-assisted approach to this problem has been proposed by (Gottesman et al 2011 Phys. Rev. Lett. 109 070503), as a possible solution to the issue of optical loss if the entangled state can be distributed across long distances by employing a quantum repeater network. In this paper, we propose an alternative entanglement-assisted scheme that interferes a two-mode squeezed vacuum state with the astronomical state and then measures the resulting state by means of homodyne detection. We use a continuous-variable approach and compute the Fisher information with respect to the mutual coherence of the astronomical source. We show that when the Fisher information is observed cumulatively at the rate at which successful measurements can be performed, our proposed scheme does not outperform the traditional direct detection approach or the entanglement-assisted approach of GJC12.

The ‘Open’ Site of Lewis Acid Zeolites for Meerwein‐Ponndorf‐Verley‐Oppenauer Reactions in Biomass Catalysis

AbstractHeteroatom zeolites are efficient catalysts for activating oxygenates in Meerwein‐Ponndorf‐Verley‐Oppenauer (MPVO) reactions, due to the unique Lewis acid character of their tetrahedral heteroatom metal sites. Such Lewis acid zeolites, especially heteroatom Beta zeolites, are intensively investigated with increasing attention in the valorization of biomass‐derived oxygenates. Herein, we first briefly review the recent development and advantage of Lewis acid zeolites for MPVO reactions involved in biomass valorization, majorly based on the selected case studies of the most representative heteroatom Beta zeolites. Increasing recognization of the open framework sites as the intrinsic active sites for Lewis acid zeolites in MPVO reactions has been discussed, with static and dynamic viewpoints. Furthermore, the recent progress on the development of characterization technologies applied in recent the state‐of‐the‐art showcases of identification and quantification of open sites, has been assessed, with a special focus on indirect detection using probe molecules and direct detection approaches. Future opportunities and challenges regarding the development of complimentary in situ characterization technologies and in‐depth understanding of the true nature of active sites and correlative mechanisms are examined for the ultimate goal of providing stable MPVO catalysts in biomass valorization.

Accurate Identification for CW Direct Signal in Underwater Acoustic Ranging

The underwater channel is bilateral, heterogeneous, uncertain, and exhibits multipath transmission, sound line curvature, etc. These properties complicate the structure of the received pulse, causing great challenges in direct signal identification for ranging purposes and impacts on back-end data processing, even accurate acoustic positioning. Machine learning (ML) combined with underwater acoustics has emerged as a prominent area of research in recent years. From a statistical perspective, ML can be viewed as an optimization strategy. Nevertheless, the existing ML-based direct-signal discrimination approaches rely on independent assessment, utilizing a single sensor (beacon or buoy), which is still insufficient for adapting to the complex underwater environment. Thus, discrimination accuracy decreases. To address the above issues, an accurate CW direct signal detection approach is performed using the decision tree algorithm, which belongs to ML. Initially, the pulse parameter characteristics in the underwater multipath channel are investigated and the parameter models are built. Then, based on multi-sensor localization performance feedback, fusion characteristics for diverse pulse are created. Next, the pulse parameter characteristics are preprocessed to mitigate the impact of varying magnitudes and units of magnitude on data processing. Then, the decision tree is built to obtain the desired output results and realize accurate recognition of the ranging direct signals. Finally, the feasibility and reliability of this paper’s method are verified by computer simulation and field testing.

Single-Atom Site SERS Chip for Rapid, Ultrasensitive, and Reproducible Direct-Monitoring of RNA Binding.

Ribonucleic acids (RNA) play active roles within cells or viruses by catalyzing biological reactions, controlling gene expression, and communicating responses to cellular signals. Rapid monitoring RNA variation has become extremely important for appropriate clinical decisions and frontier biological research. However, the most widely used method for RNA detection, nucleic acid amplification, is restricted by a mandatory temperature cycling period of ≈1 h required to reach target detection criteria. Herein, a direct detection approach via single-atom site integrated surface-enhanced Raman scattering (SERS) monitoring nucleic acid pairing reaction, can be completed within 3 min and reaches high sensitivity and extreme reproducibility for COVID-19 and two other influenza viruses' detection. The mechanism is that a single-atom site on SERS chip, enabled by positioning a single-atom oxide coordinated with a specific complementary RNA probe on chip nanostructure hotspots, can effectively bind target RNA analytes to enrich them at designed sites so that the binding reaction can be detected through Raman signal variation. This ultrafast, sensitive, and reproducible single-atom site SERS chip approach paves the route for an alternative technique of immediate RNA detection. Moreover, single-atom site SERS is a novel surface enrichment strategy for SERS active sites for other analytes at ultralow concentrations.

Frequency Acquisition Approach in Polarization-Modulated Ranging Based on Lock-in Analysis

In this study, we propose a frequency measurement and estimation approach based on a lock-in analysis for precise frequency determination in polarization-modulated ranging signals. In this method, the modulation signal of an electro-optic modulator (EOM) is manipulated to introduce an intermediate frequency (IF) component into the detected signal. Subsequently, the detected signal is analyzed using lock-in analysis techniques to extract the necessary frequency component, and a new swept frequency waveform is generated, facilitating the frequency acquisition and distance calculation. We conducted theoretical derivations, simulations and experiments to validate the effectiveness of this method. The research findings suggest that our method can enhance the accuracy of frequency measurements by a factor of approximately ten when compared to a direct detection approach, leading to a corresponding improvement in ranging precision. Furthermore, even with larger sweep step sizes and smaller modulation frequencies, the proposed approach can achieve superior ranging results.

Investigation of light dark matter with 2beta decay experiments

Nuclei that are unstable with respect to double beta decay are investigated in this work for a novel dark matter (DM) direct detection approach. In particular, the diagram responsible for the neutrinoless double beta decay can be considered for the possible detection technique of a Majorana DM fermion inelastically scattering on a double beta unstable nucleus, stimulating its decay. The exothermic nature of the stimulated double beta decay would allow the direct detection also of a light DM fermion, a class of DM candidates that are difficult or impossible to investigate with the traditional elastic scattering techniques. The expected signal distribution for different DM masses and the upper limits on the nucleus scattering cross sections, are shown and compared with the existing data for the case of ^{136}Xe nucleus.

Automatic Detection of Pancreatic Lesions and Main Pancreatic Duct Dilatation on Portal Venous CT Scans Using Deep Learning.

This study proposes and evaluates a deep learning method to detect pancreatic neoplasms and to identify main pancreatic duct (MPD) dilatation on portal venous computed tomography scans. A total of 2890 portal venous computed tomography scans from 9 institutions were acquired, among which 2185 had a pancreatic neoplasm and 705 were healthy controls. Each scan was reviewed by one in a group of 9 radiologists. Physicians contoured the pancreas, pancreatic lesions if present, and the MPD if visible. They also assessed tumor type and MPD dilatation. Data were split into a training and independent testing set of 2134 and 756 cases, respectively.A method to detect pancreatic lesions and MPD dilatation was built in 3 steps. First, a segmentation network was trained in a 5-fold cross-validation manner. Second, outputs of this network were postprocessed to extract imaging features: a normalized lesion risk, the predicted lesion diameter, and the MPD diameter in the head, body, and tail of the pancreas. Third, 2 logistic regression models were calibrated to predict lesion presence and MPD dilatation, respectively. Performance was assessed on the independent test cohort using receiver operating characteristic analysis. The method was also evaluated on subgroups defined based on lesion types and characteristics. The area under the curve of the model detecting lesion presence in a patient was 0.98 (95% confidence interval [CI], 0.97-0.99). A sensitivity of 0.94 (469 of 493; 95% CI, 0.92-0.97) was reported. Similar values were obtained in patients with small (less than 2 cm) and isodense lesions with a sensitivity of 0.94 (115 of 123; 95% CI, 0.87-0.98) and 0.95 (53 of 56, 95% CI, 0.87-1.0), respectively. The model sensitivity was also comparable across lesion types with values of 0.94 (95% CI, 0.91-0.97), 1.0 (95% CI, 0.98-1.0), 0.96 (95% CI, 0.97-1.0) for pancreatic ductal adenocarcinoma, neuroendocrine tumor, and intraductal papillary neoplasm, respectively. Regarding MPD dilatation detection, the model had an area under the curve of 0.97 (95% CI, 0.96-0.98). The proposed approach showed high quantitative performance to identify patients with pancreatic neoplasms and to detect MPD dilatation on an independent test cohort. Performance was robust across subgroups of patients with different lesion characteristics and types. Results confirmed the interest to combine a direct lesion detection approach with secondary features such as the MPD diameter, thus indicating a promising avenue for the detection of pancreatic cancer at early stages.

Seroprevalence of Brucella Infection in Wild Boars (Sus scrofa) of Bavaria, Germany, 2019 to 2021 and Associated Genome Analysis of Five B. suis Biovar 2 Isolates

Brucella species are highly pathogenic zoonotic agents and are found in vertebrates all over the world. To date, Germany is officially declared free from brucellosis and continuous surveillance is currently limited to farm ruminants. However, porcine brucellosis, mostly caused by B. suis biovar 2, is still found in wild boars and hares. In the present study, a three-year monitoring program was conducted focusing on the wild boar population in the state of Bavaria. Serologic screening of 11,956 animals and a direct pathogen detection approach, including a subset of 681 tissue samples, was carried out. The serologic incidence was 17.9%, which is in approximate accordance with previously published results from various European countries. Applying comparative whole genome analysis, five isolated B. suis biovar 2 strains from Bavaria could be assigned to three known European genetic lineages. One isolate was closely related to another strain recovered in Germany in 2006. Concluding, porcine brucellosis is endemic in Bavaria and the wild boar population represents a reservoir for genetically distinct B. suis biovar 2 strains. However, the transmission risk of swine brucellosis to humans and farm animals is still regarded as minor due to low zoonotic potential, awareness, and biosafety measures.

Analysis and experiments on C band 200G coherent PON based on Alamouti polarization-insensitive receivers.

Passive optical network (PON) based on coherent detection has attracted a great deal of attention in recent years as a future solution for 100+ Gbps per wavelength. Particularly for 200G-PON, one of the most attractive options would be to switch to QAM transmission and coherent detection, due to its well know advantages compared to the Direct-Detection approaches used so far in PON. However, coherent technology, extensively used in core networks, has costs that are still perceived as too high for the access ecosystem. In order to perform cost reduction, some groups have studied the option of coherent polarization-independent (PI) detection, since it halves the number of optoelectronic components in the receiver front end. In this paper, we thus present a detailed simulative and experimental investigation of polarization-independent receivers to achieve 200 Gbps transmission in C band using the Alamouti polarization time block coding (PTBC). Our goal is to show what would be the system requirements in terms of optoelectronic bandwidths, laser phase noise and ultimate power budget limitations. We study two different modulation formats: quadrature phase-shift keying (QPSK) and 16 quadrature amplitude modulation (16QAM). We also compare heterodyne and homodyne/intradyne solutions through simulations. As a summarizing result, we experimentally show that 200G PON based on 50 Gbaud-16QAM single-polarization Alamouti coded signals would be possible with today state-of-the-art coherent technologies, demonstrating an Optical Distribution Network loss above 33 dB with 25 km fiber length, a very promising result that is compliant with the PON power budget E1 class.

Probing the Influence of Crosslinking Layer Incubation Time on the Performance of Non-Faradaic Impedimetric Biosensors

Non-faradaic impedimetric biosensors have received a wide interest due to their direct detection approach. The crosslinking layer in these biosensors has to be dense to avoid permeation of ions into the electrode to allow direct detection of diseases. In this work, we investigate, for the first time, the effect of incubation time of cysteamine crosslinking layer on non-faradaic impedimetric biosensors performances. Two sets of interdigitated gold electrodes were functionalized with cysteamine for 1 h and 24 h. Then, UL83-antibodies were immobilized on the surface via glutaraldehyde for the detection of human cytomegalovirus. The sensitivity of biosensors functionalized with cysteamine for 24 h were superior to those functionalized with cysteamine for 1 h (i.e. 0.046 nF/ln(ng ml−1) compared to 0.0224 nF/ln(ng ml−1)). Probing the cysteamine layer with faradaic-EIS and X-ray photoelectron spectroscopy suggests that the better biosensing performance of biosensors incubated in cysteamine for 24 h is related to the better coverage of cysteamine at the biosensor surface. Atomic force microscopy analysis also revealed an increase in surface roughness with increasing cysteamine incubation time which resulted in better biosensing performances due to the enlarged surface area.

Leptospiral Infections in Humans

Leptospirosis is a globally widespread spirochetal infection spread from animals to humans. Infections are common in settings of endemicity, primarily in tropical regions of the world. Leptospirosis is typically a self-limited febrile illness but may progress to potentially fatal multiorgan system failure. Patients often present with a nonspecific acute febrile illness that is clinically difficult to distinguish from other similarly presenting infections endemic to tropical regions, including dengue fever, influenza, and malaria. A high index of suspicion is essential to early identification of patients who may benefit from antimicrobial therapy. Diagnostic testing is key to both recognition of early infection and outbreak investigation, typically in the setting of water exposure after heavy rainfall and flooding. This review focuses on the epidemiology, clinical manifestations, and laboratory diagnosis of leptospirosis, including nucleic acid amplification tests, culture, direct detection, and serological approaches.

High-throughput label-free detection of Ochratoxin A in wine using supported liquid membrane extraction and Ag-capped silicon nanopillar SERS substrates

Mycotoxins are toxic secondary metabolites produced by fungi, found in a variety of food sources and cause diseases and death in both humans and animals. On-site and easy to use detection would be significant improvement considering the currently used methods. We present an approach, based on surface-enhanced Raman spectroscopy (SERS), where label-free detection of Ochratoxin A (OTA) was achieved in a multiwell SERS detection platform. When quantifying OTA in wine samples the SERS-based detection was used in combination with high throughput supported liquid membrane (SLM) extraction. The obtained detection limit in white wine was 115 ppb and we found that the SERS-based quantification is comparable with LC-MS. However, we also found that the matrix effect from red wine was interfering with both SERS and LC-MS detection. The SLM-SERS method provides a time and cost effective approach for OTA detection, which is a clear advantage over other extraction and direct detection approaches.

A sandwich-type bacteriophage-based amperometric biosensor for the detection of Shiga toxin-producing Escherichia coli serogroups in complex matrices.

Immuno-based biosensors are a popular tool designed for pathogen screening and detection. The current antibody-based biosensors employ direct, indirect, or sandwich detection approaches; however, instability, cross-reactivity, and high-cost render them unreliable and impractical. To circumvent these drawbacks, here we report a portable sandwich-type bacteriophage-based amperometric biosensor, which is highly-specific to various Shiga toxin-producing Escherichia coli (STEC) serogroups. Environmentally isolated and biotinylated bacteriophages were directly immobilized onto a streptavidin-coated screen-printed carbon electrode (SPCE), which recognized and captured viable target cells. Samples (50 μL) were transferred to these bacteriophage-functionalized SPCEs (12 min, room temp) before sequentially adding a bacteriophage–gold nanoparticle solution (20 μL), H2O2 (40 mM), and 1,1′-ferrocenedicarboxylic acid for amperometric tests (100 mV s−1) and analysis (ANOVA and LSD, P < 0.05). The optimum biotin concentration (10 mM) retained 94.47% bacteriophage viability. Non-target bacteria (Listeria monocytogenes and Salmonella Typhimurium) had delta currents below the threshold of a positive detection. With less than 1 h turn-around time, the amperometric biosensor had a detection limit of 10–102 CFU mL−1 for STEC O157, O26, and O179 strains and R2 values of 0.97, 0.99, and 0.87, respectively, and a similar detection limit was observed in complex matrices, 10–102 CFU g−1 or mL−1 with R2 values of 0.98, 0.95, and 0.76, respectively. The newly developed portable amperometric biosensor was able to rapidly detect viable target cells at low inoculum levels, thus providing an inexpensive and improved alternative to the current immuno- and laboratory-based STEC screening methods.

DeepLog: Identify Tight Gas Reservoir Using Multi-Log Signals by a Fully Convolutional Network

In most cases, reservoir properties at one certain depth in the layer can be explicated by logging signals at just this depth point. In fact, the properties of complex reservoirs are often implicated in logging signals from the whole adjacent region of this certain depth point. So far, there is no effective way to solve this problem completely. For the first time, this letter tried to build a fully convolutional neural network (FCNN) to detect hydrocarbon from logging signals for the tight gas reservoir of Ordos Basin. The FCNN was based on a well-designed VGG-net. The prediction comparison between the empirical approach (EMA) and FCNN was implemented on 48 layers. The accuracy of FCNN was about 87.5%, which was higher than that of the EMA (75.0%). FCNN provided more reliable gas testing recommendations, especially when thin layers led to complex reservoir conditions. Deep learning (DL) has been proven to be an automatic feature extraction and direct hydrocarbon detection approach from logging signals. We are looking forward to its improvement and development in geophysics.

Ultraspecific and Amplification-Free Quantification of Mutant DNA by Single-Molecule Kinetic Fingerprinting.

Conventional techniques for detecting rare DNA sequences require many cycles of PCR amplification for high sensitivity and specificity, potentially introducing significant biases and errors. While amplification-free methods exist, they rarely achieve the ability to detect single molecules, and their ability to discriminate between single-nucleotide variants is often dictated by the specificity limits of hybridization thermodynamics. Here we show that a direct detection approach using single-molecule kinetic fingerprinting can surpass the thermodynamic discrimination limit by 3 orders of magnitude, with a dynamic range of up to 5 orders of magnitude with optional super-resolution analysis. This approach detects mutations as subtle as the drug-resistance-conferring cancer mutation EGFR T790M (a single C → T substitution) with an estimated specificity of 99.99999%, surpassing even the leading PCR-based methods and enabling detection of 1 mutant molecule in a background of at least 1 million wild-type molecules. This level of specificity revealed rare, heat-induced cytosine deamination events that introduce false positives in PCR-based detection, but which can be overcome in our approach through milder thermal denaturation and enzymatic removal of damaged nucleobases.

Real-time Detection and Monitoring of Loop Mediated Amplification (LAMP) Reaction Using Self-quenching and De-quenching Fluorogenic Probes

Loop-mediated isothermal amplification (LAMP) is an isothermal nucleic acid amplification (iNAAT) technique known for its simplicity, sensitivity and speed. Its low-cost feature has resulted in its wide scale application, especially in low resource settings. The major disadvantage of LAMP is its heavy reliance on indirect detection methods like turbidity and non-specific dyes, which often leads to the detection of false positive results. In the present work, we have developed a direct detection approach, whereby a labelled loop probe quenched in its unbound state, fluoresces only when bound to its target (amplicon). Henceforth, referred to as Fluorescence of Loop Primer Upon Self Dequenching-LAMP (FLOS-LAMP), it allows for the sequence-specific detection of LAMP amplicons. The FLOS-LAMP concept was validated for rapid detection of the human pathogen, Varicella-zoster virus, from clinical samples. The FLOS-LAMP had a limit of detection of 500 copies of the target with a clinical sensitivity and specificity of 96.8% and 100%, respectively. The high level of specificity is a major advance and solves one of the main shortcomings of the LAMP technology, i.e. false positives. Self-quenching/de-quenching probes were further used with other LAMP primer sets and different fluorophores, thereby demonstrating its versatility and adaptability.

The exhaust emission online detection on the diesel engine

Represented here is an exhaust nitric oxide emission online detection method on a single-cylinder diesel engine, which is based on the tunable diode laser absorption spectroscopy. Considering the effects of different particle matter concentration in exhaust gas on detection accuracy, the direct detection approach was adopted. The wave number chosen in the present study was 1900. 07059 cm−1. The spectral parameters were calculated as a function of temperatures. And a thermo-couple was designed to detect the real-time temperature under different operating condition and modify the experimental results. All the experimental analysis demonstrated that the online detection system developed in the present study could realize the time resolution of 100 ms and effectively record the nitric oxide emission characteristics. With the high time resolution, the transient NO emission value under the transitional work condition was also obtained,which gives a new prospect to the future research on diesel engines.

Rapid Electrochemical Detection of Pseudomonas aeruginosa Signaling Molecules by Boron-Doped Diamond Electrode.

As the leading cause of morbidity and mortality of cystic fibrosis (CF) patients, early detection of Pseudomonas aeruginosa (PA) is critical in the clinical management of this pathogen. Herein, we describe rapid and sensitive electroanalytical methods using differential pulse voltammetry (DPV) at a boron-doped diamond (BDD) electrode for the detection of PA signaling biomolecules. Monitoring the production of key signaling molecules in bacterial cultures of P. aeruginosa PA14 over 8h is described, involving sample pretreatment by liquid-liquid and solid-phase extraction. In addition, direct electrochemical detection approach of PA signaling molecules is also reported in conjunction with hexadecyltrimethylammonium bromide (CTAB) to disrupt the bacterial membrane.

Analysis of chemical warfare agents by gas chromatography-mass spectrometry: methods for their direct detection and derivatization approaches for the analysis of their degradation products

Abstract Chemical warfare agents (CWAs) are unarguably one of the most feared toxic substances produced by mankind. Their inception in conventional warfare can be traced as far back as the Middle Ages but their full breakthrough as central players in bellic conflicts was not realized until World War I. Since then, more modern CWAs along with efficient methods for their manufacture have emerged and violently shaped the way modern warfare and diplomatic relations are conducted. Owing to their mass destruction ability, counter methods to mitigate their impact appeared almost immediately on par with their development. These efforts have focused on their efficient destruction, development of medical countermeasures and their detection by modern analytical chemistry methods. The following review seeks to provide the reader with a broad introduction on their direct detection by gas chromatography-mass spectrometry (GC-MS) and the various sample derivatization methods available for the analysis of their degradation products. The review concentrates on three of the main CWA classes and includes the nerve agents, the blistering agents and lastly, the incapacitating agents. Each section begins with a brief introduction of the CWA along with discussions of reports dealing with their detection in the intact form by GC-MS. Furthermore, as products arising from their degradation carry as much importance as the agents themselves in the field of forensic analysis, the available derivatization methods of these species are presented for each CWA highlighting some examples from our lab in the Forensic Science Center at the Lawrence Livermore National Laboratory.

Global detection approach for clustered microcalcifications in mammograms using a deep learning network

In computerized detection of clustered microcalcifications (MCs) from mammograms, the traditional approach is to apply a pattern detector to locate the presence of individual MCs, which are subsequently grouped into clusters. Such an approach is often susceptible to the occurrence of false positives (FPs) caused by local image patterns that resemble MCs. We investigate the feasibility of a direct detection approach to determining whether an image region contains clustered MCs or not. Toward this goal, we develop a deep convolutional neural network (CNN) as the classifier model to which the input consists of a large image window ([Formula: see text] in size). The multiple layers in the CNN classifier are trained to automatically extract image features relevant to MCs at different spatial scales. In the experiments, we demonstrated this approach on a dataset consisting of both screen-film mammograms and full-field digital mammograms. We evaluated the detection performance both on classifying image regions of clustered MCs using a receiver operating characteristic (ROC) analysis and on detecting clustered MCs from full mammograms by a free-response receiver operating characteristic analysis. For comparison, we also considered a recently developed MC detector with FP suppression. In classifying image regions of clustered MCs, the CNN classifier achieved 0.971 in the area under the ROC curve, compared to 0.944 for the MC detector. In detecting clustered MCs from full mammograms, at 90% sensitivity, the CNN classifier obtained an FP rate of 0.69clusters/image, compared to 1.17clusters/image by the MC detector. These results indicate that using global image features can be more effective in discriminating clustered MCs from FPs caused by various sources, such as linear structures, thereby providing a more accurate detection of clustered MCs on mammograms.