Digital PCR (dPCR) has become indispensable in nucleic acid (NA) detection across various fields, including viral diagnostics and mutant detection. However, misclassification of partitions in dPCR can significantly impact accuracy. Despite existing methods to minimize misclassification bias, accurate classification remains elusive, especially for nonamplified target partitions. To address these challenges, this study introduces an innovative microdroplet-based competitive PCR platform for nucleic acid quantification in microfluidic devices independent of Poisson statistics. In this approach, the target concentration (T) is determined from the concentration of competitor DNA (C) at the equivalence point (E.P.), where C/T is 1. Competitive PCR ensures that the ratio of target to competitor DNA remains constant during amplification, reflected in the resultant fluorescence intensity, allowing the quantification of target DNA concentration at the equivalence point. The unique amplification technique eliminates Poisson distribution, addressing misclassification challenges. Additionally, our approach reduces the need for post-PCR procedures and shortens analytical time. We envision this platform as versatile, reproducible, and easily adaptable for driving significant progress in molecular biology and diagnostics.

Accompanied by a historical perspective of the field of cytometry, this introductory chapter provides a broad view of what flow cytometry can do; hence, the glass is half full.

Event-driven hybrid pixel detectors with nanosecond time resolution have opened up novel pathways in modern ultrafast electron microscopy, for example in hyperspectral electron-energy loss spectroscopy or free-electron quantum optics. However, the impinging electrons typically excite more than one pixel of the device, and an efficient algorithm is therefore needed to convert the measured pixel hits to real single-electron events. Here we present a robust clustering algorithm that is fast enough to find clusters in a continuous stream of raw data in real time. Each tuple of position and arrival time from the detector is continuously compared to a buffer of previous hits until the probability of a merger with an old event becomes irrelevant. In this way, the computation time becomes independent of the density of electron arrival and the algorithm does not break the operation chain. We showcase the performance of the algorithm with a \'timepix\' camera in two regimes of electron microscopy, in continuous beam emission and laser-triggered femtosecond mode.

BACKGROUND: High tube current generates a high flux of x-rays to photon counting detectors (PCDs) that can potentially result in the piling up of pulses formed by concurrent photons, which can cause count loss and energy resolution degradation.
OBJECTIVE: To evaluate the performance of clinical photon-counting CT (PCCT) systems in high flux, potentially influenced by pulse pileup effects, in terms of task-generic image quality metrics.
METHODS: A clinical phantom was scanned on a commercial PCCT scanner (NAEOTOM Alpha, Siemens) at 120 kV under fourteen different tube current levels (40-1000 mA) with a rotation time of 0.25 s and a pitch of 1. The dose levels corresponded to CTDIvol (32 cm phantom) of 0.79-19.8 mGy. CT sinograms were reconstructed using QIR-off mode (noniterative reconstruction algorithm), Br44 kernel, and a voxel size of 0.4102 × 0.4102 × 3 mm 3 $0.4102 \\times 0.4102 \\times 3{\\mathrm{\\ mm}}^3$ . imQuest, an open-source MATLAB-based software package was used to calculate noise power spectrum (NPS), task transfer function (TTF), contrast-to-noise ratio (CNR), and CT number according to AAPM Task Group 233 metrology.
RESULTS: The 50% cut-off frequency of TTF (f50 ) remained mostly constant across all higher tube currents for all inserts, namely polyethylene, bone, air, and acrylic. Using the lowest two data points (40 and 80 mA), the expected relationship between noise magnitude and tube current was determined to be noise ∝ $ \\propto \\ $ mA-0.47 . The measured noise magnitude were up to 11.1% higher than the expected value at the highest tube current. The average frequency of NPS (fav ) decreased from 0.32 to 0.29 mm-1 as tube current increased from 40 to 1000 mA. No considerable effects were observed in CT number measurement of any insert; however, CT numbers for air and bone changed almost monotonically as tube current increased. Absolute CNR increased monotonically for all inserts; however, the difference between measured and expected CNRs were approximately -6% to 12% across all tube currents.
CONCLUSIONS: Increasing tube currents did not affect the spatial resolution, but slightly affected the CT number and noise measurements of the clinical PCCT system. However, the effects were only considerable at clinically irrelevant tube currents used on a small 20-cm phantom. In general clinical practices, automatic exposure control techniques are used to decrease the variation of flux on the detector, which alleviates the chances of detector saturation due to high count rates. The observed effects could be due to pulse pileup, signal-dependent filtration of the system, or nonlinearities in the reconstruction algorithm. In conclusion, either the deadtime of the detector used in the photon-counting CT system is shorter such that count losses due to pulse pileup are negligible, or pulse pileup has inconsiderable effects on the image quality of clinical photon-counting CT systems in routine clinical practice due to possible corrections applied on the system.

GeSn on Si has attracted much research interest due to its tunable direct bandgap for mid-infrared applications. Recently, short-range order (SRO) in GeSn alloys has been theoretically predicted, which profoundly impacts the band structure. However, characterizing SRO in GeSn is challenging. Guided by physics-informed Poisson statistical analyses of k-nearest neighbors (KNN) in atom probe tomography (APT), a new approach is demonstrated here for 3D nanoscale SRO mapping and semi-quantitative strain mapping in GeSn. For GeSn with ≈14 at. % Sn, the SRO parameters of Sn-Sn 1NN in 10 × 10 × 10 nm3 nanocubes can deviate from that of the random alloys by ±15 %. The relatively large fluctuation of the SRO parameters contributes to band-edge softening observed optically. Sn-Sn 1NN also tends to be more favored toward the surface, less favored under strain relaxation or tensile strain, while almost independent of local Sn composition. An algorithm based on least square fit of atomic positions further verifies this Poisson-KNN statistical method. Compared to existing macroscopic spectroscopy or electron microscopy techniques, this new APT statistical analysis uniquely offers 3D SRO mapping at nanoscale resolution in a relatively large volume with millions of atoms. It can also be extended to investigate SRO in other alloy systems.

Large-scale nanoarrays of single biomolecules enable high-throughput assays while unmasking the underlying heterogeneity within ensemble populations. Until recently, creating such grids which combine the advantages of microarrays and single-molecule experiments (SMEs) has been particularly challenging due to the mismatch between the size of these molecules and the resolution of top-down fabrication techniques. DNA origami placement (DOP) combines two powerful techniques to address this issue: (i) DNA origami, which provides a ∼100 nm self-assembled template for single-molecule organization with 5 nm resolution and (ii) top-down lithography, which patterns these DNA nanostructures, transforming them into functional nanodevices via large-scale integration with arbitrary substrates. Presently, this technique relies on state-of-the-art infrastructure and highly trained personnel, making it prohibitively expensive for researchers. Here, we introduce a cleanroom-free, $1 benchtop technique to create meso-to-macro-scale DNA origami nanoarrays using self-assembled colloidal nanoparticles, thereby circumventing the need for top-down fabrication. We report a maximum yield of 74%, 2-fold higher than the statistical limit of 37% imposed on non-specific molecular loading alternatives. Furthermore, we provide a proof-of-principle for the ability of this nanoarray platform to transform traditionally low-throughput, stochastic, single-molecule assays into high-throughput, deterministic ones, without compromising data quality. Our approach has the potential to democratize single-molecule nanoarrays and demonstrates their utility as a tool for biophysical assays and diagnostics.

The ultimate precision in both dPCR and qPCR experiments is limited by the Poisson statistics in the total number m of template molecules in the sample, giving relative standard deviation 1/m. This means that precision is limited by sample volume at low concentrations. Accordingly qPCR instruments, used in dPCR mode, can give better precision than dPCR instruments in this limit. For example, 13% standard deviation can be achieved with a 96-well plate for number concentrations ~20-5000 mL-1. For fixed m, qPCR loses to dPCR by a factor of ~2 in precision when calibration is needed.

The accurate determination of the molar concentration or the number concentration of particles in a defined volume is important but challenging. Since particle diversity and heterogeneity cannot be ignored in particle quantification, single particle counting has become quite important. However, most methods require standard samples (calibrators) which are usually difficult to obtain. The authors describe a method for single particle counting that is based on the combination of digital counting and formation of microdroplets in a microchip. By compartmentalizing particles into picoliter droplets, positive droplets encapsulating particles were counted and particle concentrations were calculated by Poisson statistics. The concentration of particles over a wide range (from 5.0 × 103 to 1.8 × 107 particles per mL) were accurately determined without the need for using a calibrator. A microdroplet chip including a T-junction channel achieved a 9-fold increase of signal-to-background ratio compared to the traditional flow-focusing chip. This makes the digital counting system a widely applicable tool for quantification of fluorescent particles. Various particles including differently sized fluorescent microspheres and bacteria with large heterogeneity in shape such as Escherichia coli DH5α-pDsRed were accurately quantified by this method. Graphical abstract Schematic representation of the digital single particle counting system for absolute quantification of particles. Particles compartmentalized in picoliter droplets were counted and the number concentration of particles was determined using digital analysis.

This work provides a short summary of techniques for formally-correct handling of statistical uncertainties in Poisson-statistics dominated data, with emphasis on X-ray powder diffraction patterns. Correct assignment of uncertainties for low counts is documented. Further, we describe a technique for adaptively rebinning such data sets to provide more uniform statistics across a pattern with a wide range of count rates, from a few (or no) counts in a background bin to on-peak regions with many counts. This permits better plotting of data and analysis of a smaller number of points in a fitting package, without significant degradation of the information content of the data set. Examples of the effect of this on a diffraction data set are given.

Accompanied by a historical perspective of the field of cytometry, this introductory chapter provides a broad view of what flow cytometry can do; hence, the glass is half full.