Staining and scanning of tissue samples for microscopic examination is fraught with undesirable color variations arising from differences in raw materials and manufacturing techniques of stain vendors, staining protocols of labs, and color responses of digital scanners. When comparing tissue samples, color normalization and stain separation of the tissue images can be helpful for both pathologists and software. Techniques that are used for natural images fail to utilize structural properties of stained tissue samples and produce undesirable color distortions. The stain concentration cannot be negative. Tissue samples are stained with only a few stains and most tissue regions are characterized by at most one effective stain. We model these physical phenomena that define the tissue structure by first decomposing images in an unsupervised manner into stain density maps that are sparse and non-negative. For a given image, we combine its stain density maps with stain color basis of a pathologist-preferred target image, thus altering only its color while preserving its structure described by the maps. Stain density correlation with ground truth and preference by pathologists were higher for images normalized using our method when compared to other alternatives. We also propose a computationally faster extension of this technique for large whole-slide images that selects an appropriate patch sample instead of using the entire image to compute the stain color basis.

Quantitative analysis of bioimaging data is often skewed by both shading in space and background variation in time. We introduce BaSiC, an image correction method based on low-rank and sparse decomposition which solves both issues. In comparison to existing shading correction tools, BaSiC achieves high-accuracy with significantly fewer input images, works for diverse imaging conditions and is robust against artefacts. Moreover, it can correct temporal drift in time-lapse microscopy data and thus improve continuous single-cell quantification. BaSiC requires no manual parameter setting and is available as a Fiji/ImageJ plugin.

Cryogenic electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. However, automated identification of macromolecules inside cellular tomograms is challenged by noise and reconstruction artifacts, as well as the presence of many molecular species in the crowded volumes. Here, we present DeepFinder, a computational procedure that uses artificial neural networks to simultaneously localize multiple classes of macromolecules. Once trained, the inference stage of DeepFinder is faster than template matching and performs better than other competitive deep learning methods at identifying macromolecules of various sizes in both synthetic and experimental datasets. On cellular cryo-ET data, DeepFinder localized membrane-bound and cytosolic ribosomes (∼3.2 MDa), Rubisco (∼560 kDa soluble complex), and photosystem II (∼550 kDa membrane complex) with an accuracy comparable to expert-supervised ground truth annotations. DeepFinder is therefore a promising algorithm for the semi-automated analysis of a wide range of molecular targets in cellular tomograms.

Amyloid-β (Aβ) is thought to play an essential pathogenic role in Alzheimer´s disease (AD). A key enzyme involved in the generation of Aβ is the β-secretase BACE, for which powerful inhibitors have been developed and are currently in use in human clinical trials. However, although BACE inhibition can reduce cerebral Aβ levels, whether it also can ameliorate neural circuit and memory impairments remains unclear. Using histochemistry, in vivo Ca 2+ imaging, and behavioral analyses in a mouse model of AD, we demonstrate that along with reducing prefibrillary Aβ surrounding plaques, the inhibition of BACE activity can rescue neuronal hyperactivity, impaired long-range circuit function, and memory defects. The functional neuronal impairments reappeared after infusion of soluble Aβ, mechanistically linking Aβ pathology to neuronal and cognitive dysfunction. These data highlight the potential benefits of BACE inhibition for the effective treatment of a wide range of AD-like pathophysiological and cognitive impairments.A lzheimer´s disease (AD) is the most common cause of dementia globally, with an increasing impact on aging societies (1). Therefore, the prevention and treatment of AD is a major unmet medical need. The amyloid hypothesis posits that the abnormal accumulation of amyloid-β (Aβ) peptides in the brain, and their aggregation, is an essential feature of AD (2, 3); however, results from clinical studies using several Aβ-targeting compounds have called into question the existence of a direct link between a reduction in Aβ and improvement of brain function, particularly in more advanced disease stages (4-6). In addition, recent evidence obtained in mouse models carrying genetic mutations that cause AD in humans revealed that immunotherapy with antibodies against Aβ worsened rather than reversed neuronal dysfunction (7). Despite reducing plaque burden, the anti-Aβ antibodies caused a massive increase in cortical hyperactivity and promoted abnormal synchrony of neurons in a subset of the treated mice. In this context, it is noteworthy that another recent mouse study found an increased risk of sudden death after anti-Aβ antibody treatment, which was attributed to enhanced excitatory neuronal activity culminating in fatal convulsive seizures (8).To clarify the causal relationship between Aβ and pathophysiology in vivo, we made use of a novel compound that reduces Aβ by inhibiting the β-secretase BACE, the rate-limiting enzyme for Aβ production (9). This approach allowed us to determine how the inhibition of Aβ production affects neural circuit and memory impairments in APP23xPS45 transgenic mice overexpressing mutant human amyloid precursor protein (APP) and presenilin 1 (PS1). The combination of histochemistry, in vivo Ca 2+ imaging, and behavioral analysis allowed us to directly link the treatment-related changes in brain Aβ levels to changes in neuronal and cognitive functions in individual mice. ResultsIn this study, we used 6-to 8-mo-old APP23xPS45 transgenic mice that exhibit severe cerebral Aβ pathology, neur...

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The effect of spontaneous beat-to-beat mean arterial blood pressure (ABP) fluctuations and breath-to-breath end-tidal carbon dioxide (PETCO2) and end-tidal oxygen (PETO2) fluctuations on beat-to-beat cerebral bloodflow velocity (CBFV) variations is studied using a multiple coherence function. Multiple coherence is a measure of the extent to which the output, CBFV, can be represented as a linear time invariant system of multiple input signals. Analysis of experimental measurements from 13 different healthy subjects reveal that, with additional inputs, PETCO2 and PETO2, the multiple coherence for frequencies <0.05 Hz is significantly higher than the corresponding values obtained for univariate coherence with a single input of ABP. The result illustrates that the low value of univariate coherence at small frequencies may be due to the effects of PETCO2 and PETO2 fluctuations on CBFV variability. Moreover, it is also found that the transfer function between ABP and CBFVtime series identified from previous univariate techniques at low frequencies can be modified by CO2 and O2 reactivity and no longer represents pressure autoregulation only. Multivariate system identification provides a technique of incorporating additional variability and recovering from this artifact. Finally, a physiologically based model and its linear transfer function are used as a simulation tool to investigate possible causes of low univariate coherence.

Thermal treatments for tissue ablation rely upon the heating of cells past a threshold beyond which the cells are considered destroyed, denatured, or killed. In this article, a novel three-state model for cell death is proposed where there exists a vulnerable state positioned between the alive and dead states used in a number of existing cell death models. Proposed rate coefficients include temperature dependence and the model is fitted to experimental data of heated co-cultures of hepatocytes and lung fibroblasts with very small RMS error. The experimental data utilized include further reductions in cell viabilities over 24 and 48 h post-heating and these data are used to extend the three-state model to account for slow cell death. For the two cell lines employed in the experimental data, the three parameters for fast cell death appear to be linearly increasing with % content of lung fibroblast, while the sparse nature of the data did not indicate any co-culture make-up dependence for the parameters for slow cell death. A critical post-heating cell viability threshold is proposed beyond which cells progress to death; and these results are of practical importance with potential for more accurate prediction of cell death.

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