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•We found that there are non-hemodynamic components in intrinsic optical imaging data.•We found that the non-hemodynamic components result in insufficient substruction of hemodynamic noise from wide-field fluorescent imaging data.•We developed a linear regression method that can calculate the non-hemodynamic components and lead to more precise hemodynamic subtraction.•We demonstrate that the new method works on data from various experiments. Accurate interpretation of in vivo wide-field fluorescent imaging (WFFI) data requires precise separation of raw fluorescence signals into neural and hemodynamic components. The classical Beer-Lambert law-based approach, which uses concurrent 530-nm illumination to estimate relative changes in cerebral blood volume (CBV), fails to account for the scattering and reflection of 530-nm photons from non-neuronal components leading to biased estimates of CBV changes and subsequent misrepresentation of neural activity. This study introduces a novel linear regression approach designed to overcome this limitation. This correction provides a more reliable representation of CBV changes and neural activity in fluorescence data. Our method is validated across multiple datasets, demonstrating its superiority over the classical approach.

Representational similarity analysis of activation patterns has become an increasingly important tool for studying brain representations. The dissimilarity between two patterns is commonly quantified by the correlation distance or the accuracy of a linear classifier. However, there are many different ways to measure pattern dissimilarity and little is known about their relative reliability. Here, we compare the reliability of three classes of dissimilarity measure: classification accuracy, Euclidean/Mahalanobis distance, and Pearson correlation distance. Using simulations and four real functional magnetic resonance imaging (fMRI) datasets, we demonstrate that continuous dissimilarity measures are substantially more reliable than the classification accuracy. The difference in reliability can be explained by two characteristics of classifiers: discretization and susceptibility of the discriminant function to shifts of the pattern ensemble between imaging runs. Reliability can be further improved through multivariate noise normalization for all measures. Finally, unlike conventional distance measures, crossvalidated distances provide unbiased estimates of pattern dissimilarity on a ratio scale, thus providing an interpretable zero point. Overall, our results indicate that the crossvalidated Mahalanobis distance is preferable to both the classification accuracy and the correlation distance for characterizing representational geometries. •We compare the reliability of dissimilarity measures and classifiers in fMRI.•We examine the effect of noise normalizations and crossvalidation on reliability.•Multivariate noise normalization makes the dissimilarity measures more reliable.•Crossvalidation makes the dissimilarity measures more reliable and unbiased.•Dissimilarities measure brain representations more reliably than classifiers.

Distance-dependent magnetic resonance tuning (MRET) technology enables the sensing and quantitative imaging of biological targets in vivo, with the advantage of deep tissue penetration and fewer interactions with the surroundings as compared with those of fluorescence-based Förster resonance energy transfer. However, applications of MRET technology in vivo are currently limited by the moderate contrast enhancement and stability of T1-based MRET probes. Here we report a new two-way magnetic resonance tuning (TMRET) nanoprobe with dually activatable T1 and T2 magnetic resonance signals that is coupled with dual-contrast enhanced subtraction imaging. This integrated platform achieves a substantially improved contrast enhancement with minimal background signal and can be used to quantitatively image molecular targets in tumours and to sensitively detect very small intracranial brain tumours in patient-derived xenograft models. The high tumour-to-normal tissue ratio offered by TMRET in combination with dual-contrast enhanced subtraction imaging provides new opportunities for molecular diagnostics and image-guided biomedical applications.A distance-dependent two-way magnetic resonance tuning platform combined with dual-contrast enhanced subtraction imaging enables quantitative sensing and imaging in deep tissues with minimal background noise.

Stimulated Raman scattering (SRS) microscopy allows for high-speed label-free chemical imaging of biomedical systems. The imaging sensitivity of SRS microscopy is limited to ~10 mM for endogenous biomolecules. Electronic pre-resonant SRS allows detection of sub-micromolar chromophores. However, label-free SRS detection of single biomolecules having extremely small Raman cross-sections (~10 −30  cm 2 sr −1 ) remains unreachable. Here, we demonstrate plasmon-enhanced stimulated Raman scattering (PESRS) microscopy with single-molecule detection sensitivity. Incorporating pico-Joule laser excitation, background subtraction, and a denoising algorithm, we obtain robust single-pixel SRS spectra exhibiting single-molecule events, verified by using two isotopologues of adenine and further confirmed by digital blinking and bleaching in the temporal domain. To demonstrate the capability of PESRS for biological applications, we utilize PESRS to map adenine released from bacteria due to starvation stress. PESRS microscopy holds the promise for ultrasensitive detection and rapid mapping of molecular events in chemical and biomedical systems. Stimulated Raman scattering (SRS) microscopy enables label-free chemical imaging at high speed, but has been limited by low sensitivity. Here, the authors demonstrate plasmon-enhanced SRS microscopy and achieve single molecule detection sensitivity.

Background The analyses of amplification and melting curves have been shown to provide valuable information on the quality of the individual reactions in quantitative PCR (qPCR) experiments and to result in more reliable and reproducible quantitative results. Implementation The main steps in the amplification curve analysis are (1) a unique baseline subtraction, not using the ground phase cycles, (2) PCR efficiency determination from the exponential phase of the individual reactions, (3) setting a common quantification threshold and (4) calculation of the efficiency-corrected target quantity with the common threshold, efficiency per assay and C q per reaction. The melting curve analysis encompasses smoothing of the observed fluorescence data, normalization to remove product-independent fluorescence loss, peak calling and assessment of the correct peak by comparing its melting temperature with the known melting temperature of the intended amplification product. Results The LinRegPCR web application provides visualization and analysis of a single qPCR run. The user interface displays the analysis results on the amplification curve analysis and melting curve analysis in tables and graphs in which deviant reactions are highlighted. The annotated results in the tables can be exported for calculation of gene-expression ratios, fold-change between experimental conditions and further statistical analysis. Web-based LinRegPCR addresses two types of users, wet-lab scientists analyzing the amplification and melting curves of their own qPCR experiments and bioinformaticians creating pipelines for analysis of series of qPCR experiments by splitting its functionality into a stand-alone back-end RDML (Real-time PCR Data Markup Language) Python library and several companion applications for data visualization, analysis and interactive access. The use of the RDML data standard enables machine independent storage and exchange of qPCR data and the RDML-Tools assist with the import of qPCR data from the files exported by the qPCR instrument. Conclusions The combined implementation of these analyses in the newly developed web-based LinRegPCR ( https://www.gear-genomics.com/rdml-tools/ ) is platform independent and much faster than the original Windows-based versions of the LinRegPCR program. Moreover, web-based LinRegPCR includes a novel statistical outlier detection and the combination of amplification and melting curve analyses allows direct validation of the amplification product and reporting of reactions that amplify artefacts.

Stereo-electroencephalography (SEEG) is an intracranial recording technique in which depth electrodes are inserted in the brain as part of presurgical assessments for invasive brain surgery. SEEG recordings can tap into neural signals across the entire brain and thereby sample both cortical and subcortical sites. However, even though signal referencing is important for proper assessment of SEEG signals, no previous study has comprehensively evaluated the optimal referencing method for SEEG. In our study, we recorded SEEG data from 15 human subjects during a motor task, referencing them against the average of two white matter contacts (monopolar reference). We then subjected these signals to 5 different re-referencing approaches: common average reference (CAR), gray-white matter reference (GWR), electrode shaft reference (ESR), bipolar reference, and Laplacian reference. The results from three different signal quality metrics suggest the use of the Laplacian re-reference for study of local population-level activity and low-frequency oscillatory activity. •Optimal signal referencing has not been established for SEEG recordings.•We recorded SEEG data from 15 human subjects during a motor task.•We compared 6 referencing approaches against 3 different signal quality metrics.•We evaluated referencing effects on broadband gamma and low-frequency oscillations.•The Laplacian reference is optimal for broadband gamma and oscillatory activity.

About one third of all deaths worldwide can be traced back to cardiovascular diseases. An interventional radiology procedure for their diagnosis is Digital Subtraction Angiography (DSA). An alternative to DSA is K-Edge subtraction (KES) imaging, which has been shown to be advantageous for moving organs and eliminating image artifacts caused by patient movement. As highly brilliant, monochromatic X-rays are required for this method, it has been limited to synchrotron facilities so far, restraining the feasibility in clinical routine. Compact synchrotron X-ray sources based on inverse Compton scattering, which have been evolving substantially over the past decade, provide X-rays with sufficient brilliance that meet spatial and financial requirements affordable in laboratory settings or for university hospitals. In this work, we demonstrate a first proof-of-principle K-edge subtraction imaging experiment using the Munich Compact Light Source (MuCLS), the first user-dedicated installation of a compact synchrotron X-ray source worldwide. It is shown experimentally that the technique of KES increases the visibility of small blood vessels overlaid by bone structures.

Ratiometric time-lapse FRET analysis requires a robust and accurate processing pipeline to eliminate bias in intensity measurements on fluorescent images before further quantitative analysis can be conducted. This level of robustness can only be achieved by supplementing automated tools with built-in flexibility for manual ad-hoc adjustments. FRET-IBRA is a modular and fully parallelized configuration file-based tool written in Python. It simplifies the FRET processing pipeline to achieve accurate, registered, and unified ratio image stacks. The flexibility of this tool to handle discontinuous image frame sequences with tailored configuration parameters further streamlines the processing of outliers and time-varying effects in the original microscopy images. FRET-IBRA offers cluster-based channel background subtraction, photobleaching correction, and ratio image construction in an all-in-one solution without the need for multiple applications, image format conversions, and/or plug-ins. The package accepts a variety of input formats and outputs TIFF image stacks along with performance measures to detect both the quality and failure of the background subtraction algorithm on a per frame basis. Furthermore, FRET-IBRA outputs images with superior signal-to-noise ratio and accuracy in comparison to existing background subtraction solutions, whilst maintaining a fast runtime. We have used the FRET-IBRA package extensively to quantify the spatial distribution of calcium ions during pollen tube growth under mechanical constraints. Benchmarks against existing tools clearly demonstrate the need for FRET-IBRA in extracting reliable insights from FRET microscopy images of dynamic physiological processes at high spatial and temporal resolution. The source code for Linux and Mac operating systems is released under the BSD license and, along with installation instructions, test images, example configuration files, and a step-by-step tutorial, is freely available at github.com/gmunglani/fret-ibra .

The additive noise in the condition monitoring system using fiber Bragg grating (FBG) sensors, including white Gaussian noise and multifrequency interference, has a significantly negative influence on the fault diagnosis of rotating machinery. Spectral subtraction (SS) is an effective method for handling white Gaussian noise. However, the SS method exhibits poor performance in eliminating multifrequency interference because estimating the noise spectrum accurately is difficult, and it significantly weakens the useful information components in measured signals. In this study, an improved spectral subtraction (ISS) method is proposed to enhance its denoising performance. In the ISS method, a reference noise signal measured by the same sensing system without working loads is considered the estimated noise, the same sliding window is used to divide the power spectrums of the measured and reference noise signals into multiple frequency bands, and the formula of spectral subtraction in the standard SS method is modified. A simulation analysis and an experiment are executed by using simulated signals and establishing a vibration test rig based on the FBG sensor, respectively. The statistical results demonstrate the effectiveness and feasibility of the ISS method in simultaneously eliminating white Gaussian noise and multifrequency interference while well maintaining the useful information components.

The non-stationary and non-continuous noises in the tunnel seismic data can cause huge noise spectrum estimation errors in time-frequency domain spectrum subtraction method, thus affecting the filtering effect. In order to solve the problem of denoising non-stationary signals, we propose a joint filtering method called Empirical Mode Decomposition-Time-Frequency Domain Spectral Subtraction (EMD-T-FSS), which combines time-frequency domain spectral subtraction and empirical mode decomposition. The EMD-T-FSS method proposed in this manuscript mainly includes three stages. First, the empirical mode decomposition method is used to decompose the seismic data into multiple intrinsic modal functions, which can effectively reduce the non-stationary characteristics of the signal. After that, time-frequency domain spectral subtraction is used for denoising each intrinsic modal function. At last, all intrinsic modal functions after denoising are weighted and added to obtain the denoising data. The denoising ability and flexibility of the proposed method are tested by numerical simulation data. The analysis of the denoising results shows that the EMD-T-FSS method is better suitable for data with different signal-to-noise ratios, and has obvious advantage when comparing with conventional spectral subtraction.