The authors demonstrate a form of two‐photon‐counting interferometry by measuring the coincidence counts between single‐photon‐counting detectors at an output port of a Mach–Zehnder Interferometer (MZI) following injection of broad‐band time‐frequency‐entangled photon pairs (EPP) generated from collinear spontaneous parametric down conversion into a single input port. Spectroscopy and refractometry are performed on a sample inserted in one internal path of the MZI by scanning the other path in length, which acquires phase and amplitude information about the sample's linear response. Phase modulation and lock‐in detection are introduced to increase detection signal‐to‐noise ratio and implement a “down‐sampling” technique for scanning the interferometer delay, which reduces the sampling requirements needed to reproduce fully the temporal interference pattern. The phase‐modulation technique also allows the contributions of various quantum‐state pathways leading to the final detection outcomes to be extracted individually. Feynman diagrams frequently used in the context of molecular spectroscopy are used to describe the interferences resulting from the coherence properties of time‐frequency EPPs passing through the MZI. These results are an important step toward the implementation of a proposed method for molecular spectroscopy—quantum‐light‐enhanced 2D spectroscopy.
Fluorescence-detected Fourier transform (FT) spectroscopy is a technique in which the relative paths of an optical interferometer are controlled to excite a material sample, and the ensuing fluorescence is detected as a function of the interferometer path delay and relative phase. A common approach to enhance the signal-to-noise ratio in these experiments is to apply a continuous phase sweep to the relative optical path, and to detect the resulting modulated fluorescence using a phase-sensitive lock-in amplifier. In many important situations, the fluorescence signal is too weak to be measured using a lock-in amplifier, so that photon counting techniques are preferred. Here we introduce an approach to low-signal fluorescence-detected FT spectroscopy, in which individual photon counts are assigned to a modulated interferometer phase (‘phase-tagged photon counting,’ or PTPC), and the resulting data are processed to construct optical spectra. We studied the fluorescence signals of a molecular sample excited resonantly by a pulsed coherent laser over a range of photon flux and visibility levels. We compare the performance of PTPC to standard lock-in detection methods and establish the range of signal parameters over which meaningful measurements can be carried out. We find that PTPC generally outperforms the lock-in detection method, with the dominant source of measurement uncertainty being associated with the statistics of the finite number of samples of the photon detection rate.
more » « less- Award ID(s):
- 1839216
- NSF-PAR ID:
- 10182948
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Optics Express
- Volume:
- 28
- Issue:
- 17
- ISSN:
- 1094-4087; OPEXFF
- Page Range / eLocation ID:
- Article No. 25194
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
Optical projection tomography (OPT) is a powerful imaging modality for attaining high resolution absorption and fluorescence imaging in tissue samples and embryos with a diameter of roughly 1 mm. Moving past this 1 mm limit, scattered light becomes the dominant fraction detected, adding significant “blur” to OPT. Time-domain OPT has been used to select out early-arriving photons that have taken a more direct route through the tissue to reduce detection of scattered photons in these larger samples, which are the cause of image domain blur1. In addition, it was recently demonstrated by our group that detection of scattered photons could be further depressed by running in a “deadtime” regime where laser repetition rates are selected such that the deadtime incurred by early-arriving photons acts as a shutter to later-arriving scattered photons2. By running in this deadtime regime, far greater early photon count rates are achievable than with standard early photon OPT. In this work, another advantage of this enhanced early photon collection approach is demonstrated: specifically, a significant improvement in signal-to-noise ratio. In single photon counting detectors, the main source of noise is “afterpulsing,” which is essentially leftover charge from a detected photon that spuriously results in a second photon count. When the arrival of the photons are time-stamped by the time correlated single photon counting (TCSPC) module , the rate constant governing afterpusling is slow compared to the time-scale of the light pulse detected so it is observed as a background signal with very little time-correlation. This signal is present in all time-gates and so adds noise to the detection of early photons. However, since the afterpusling signal is proportional to the total rate of photon detection, our enhanced early photon approach is uniquely able to have increased early photon counts with no appreciable increase in the afterpulsing since overall count-rate does not change. This is because as the rate of early photon detection goes up, the rate of late-photon detection reduces commensurately, yielding no net change in the overall rate of photons detected. This hypothesis was tested on a 4 mm diameter tissue-mimicking phantom (μa = 0.02 mm-1, μs’ = 1 mm-1) by ranging the power of a 10 MHz pulse 780-nm laser with pulse spread of < 100 fs (Calmar, USA) and an avalanche photodiode (MPD, Picoquant, Germany) and TCSPC module (HydraHarp, Picoquant, Germany) for light detection. Details of the results are in Fig. 1a, but of note is that we observed more than a 60-times improvement in SNR compared to conventional early photon detection that would have taken 1000-times longer to achieve the same early photon count. A demonstration of the type of resolution possible is in Fig 1b with an image of a 4-mm-thick human breast cancer biopsy where tumor spiculations of less than 100 μm diameter are observable. 1Fieramonti, L. et al. PloS one (2012). 2Sinha, L., et al. Optics letters (2016).more » « less
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Obeid, Iyad Selesnick (Ed.)Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEG channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9.more » « less
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Summary Localization of mRNA and small RNAs (sRNAs) is important for understanding their function. Fluorescent
in situ hybridization (FISH) has been used extensively in animal systems to study the localization and expression of sRNAs. However, current methods for fluorescentin situ detection of sRNA in plant tissues are less developed. Here we report a protocol (sRNA‐FISH) for efficient fluorescent detection of sRNAs in plants. This protocol is suitable for application in diverse plant species and tissue types. The use of locked nucleic acid probes and antibodies conjugated with different fluorophores allows the detection of two sRNAs in the same sample. Using this method, we have successfully detected the co‐localization of miR2275 and a 24‐nucleotide phased small interfering RNA in maize anther tapetal and archesporial cells. We describe how to overcome the common problem of the wide range of autofluorescence in embedded plant tissue using linear spectral unmixing on a laser scanning confocal microscope. For highly autofluorescent samples, we show that multi‐photon fluorescence excitation microscopy can be used to separate the target sRNA‐FISH signal from background autofluorescence. In contrast to colorimetricin situ hybridization, sRNA‐FISH signals can be imaged using super‐resolution microscopy to examine the subcellular localization of sRNAs. We detected maize miR2275 by super‐resolution structured illumination microscopy and direct stochastic optical reconstruction microscopy. In this study, we describe how we overcame the challenges of adapting FISH for imaging in plant tissue and provide a step‐by‐step sRNA‐FISH protocol for studying sRNAs at the cellular and even subcellular level. -
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We demonstrate fluorescence-detected two-dimensional electronic spectroscopy (F-2DES) with a broadband, continuum probe pulse pair in the pump-probe geometry. The approach combines a pump pulse pair generated by an acousto-optic pulse-shaper with precise control of the relative pump pulse phase and time delay with a broadband, continuum probe pulse pair created using the Translating Wedge-based Identical pulses eNcoding System (TWINS). The continuum probe expands the spectral range of the detection axis and lengthens the waiting times that can be accessed in comparison to implementations of F-2DES using a single pulse-shaper. We employ phase-cycling of the pump pulse pair and take advantage of the separation of signals in the frequency domain to isolate rephasing and non-rephasing signals and optimize the signal-to-noise ratio. As proof of principle, we demonstrate broadband F-2DES on a laser dye and bacteriochlorophyll
a .
