Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue

Histological staining is a vital step used to diagnose various diseases and has been used for more than a century to provide contrast to tissue sections, rendering the tissue constituents visible for microscopic analysis by medical experts. However, this process is time-consuming, labor-intensive, expensive and destructive to the specimen. Recently, the ability to virtually-stain unlabeled tissue sections, entirely avoiding the histochemical staining step, has been demonstrated using tissue-stain specific deep neural networks. Here, we present a new deep learning-based framework which generates virtually-stained images using label-free tissue, where different stains are merged following a micro-structure map defined by the user. This approach uses a single deep neural network that receives two different sources of information at its input: (1) autofluorescence images of the label-free tissue sample, and (2) a digital staining matrix which represents the desired microscopic map of different stains to be virtually generated at the same tissue section. This digital staining matrix is also used to virtually blend existing stains, digitally synthesizing new histological stains. We trained and blindly tested this virtual-staining network using unlabeled kidney tissue sections to generate micro-structured combinations of Hematoxylin and Eosin (H&E), Jones silver stain, and Masson's Trichrome stain. Using a single network, this approach multiplexes virtual staining of label-free tissue with multiple types of stains and paves the way for synthesizing new digital histological stains that can be created on the same tissue cross-section, which is currently not feasible with standard histochemical staining methods.Comment: 19 pages, 5 figures, 2 table

Progressing Towards Understanding Water Use Efficiency in Southern, Ontario Canada: Quantifying Water Use Efficiency Metrics (WUE) and Investigating Soil and Plant Physiology Influences on WUE

Climate change, and corresponding temperature increases of 1.4 - 4.8 ºC by the end of the century, are expected to cause shifts in agricultural production. Additionally, shifts in precipitation patterns are expected to cause strains on water resources. Globally, croplands occupy 33-40% of terrestrial land area with only 17-18% of these being irrigated; thus, rainfed croplands will remain important to global food production. This highlights the need to maximize crop resource use while responding to environment shifts and maximizing agricultural production. Water use efficiency (WUE), which measures carbon assimilation per unit water use, has been identified as an important indicator for plant resource use, which has the potential to provide insight into responses of environmental changes. However, there is a paucity of information on differences between crop species WUE or the drivers behind these differences, which are important to consider in climate change scenarios. Furthermore, there are several calculations for WUE, but there is a lack of field-based studies investigating inconsistencies between these calculations. This thesis addresses these knowledge gaps with the following two objectives: 1) quantify plant water-carbon dynamics of two common forage crops in southern Ontario, maize (Zea mays L.) and alfalfa (Medicago sativa), and investigate the ecosystem drivers of these differences; and 2) quantify WUE of alfalfa and maize crops using different WUE calculation approaches, and investigate the inconsistencies between these methods. Alfalfa contained greater growing season ecosystem WUE (EWUE) than maize, with daily fluctuations in EWUE being controlled by differences in gross primary productivity (GPP) rather than evapotranspiration (ET). Since these sites were subjected to similar climate and atmospheric variables, and similar soil conditions, differences between the crops were attributed to crop physiology and farming practices which influenced crop growth. In general, alfalfa had higher growing season “flux-based” EWUE’s, while maize had higher “harvest-based” WUE’s (HWUE’s). Inconsistencies between methods were attributed to processing method, crop physiology, and management influences on crop growth. The importance of timescale was also shown where the typically less efficient C3 crop (alfalfa) had higher growing season EWUE despite having a lower median half-hourly EWUE The results of this thesis progressed our knowledge of WUE and how crop selection and farming practices influence it. Farming practices that affect crop growth influence these metrics, which can inform future crop selections and aid in adaptation to climate change. This also highlights the importance of considering different variables included in WUE calculations and the need for a more robust approach to crop resource use which accounts for both plant stomatal responses, non-plant ecosystem responses, and biomass production

Adult cortical plasticity depends on an early postnatal critical period

Development of the cerebral cortex is influenced by sensory experience during distinct phases of postnatal development known as critical periods. Disruption of experience during a critical period produces neurons that lack specificity for particular stimulus features, such as location in the somatosensory system. Synaptic plasticity is the agent by which sensory experience affects cortical development. Here, we describe, in mice, a developmental critical period that affects plasticity itself. Transient neonatal disruption of signaling via the C-terminal domain of "disrupted in schizophrenia 1" (DISC1)-a molecule implicated in psychiatric disorders-resulted in a lack of long-term potentiation (LTP) (persistent strengthening of synapses) and experience-dependent potentiation in adulthood. Long-term depression (LTD) (selective weakening of specific sets of synapses) and reversal of LTD were present, although impaired, in adolescence and absent in adulthood. These changes may form the basis for the cognitive deficits associated with mutations in DISC1 and the delayed onset of a range of psychiatric symptoms in late adolescence

Brown Carbon Production by Aqueous-Phase Interactions of Glyoxal and SO2

Oxalic acid and sulfate salts are major components of aerosol particles. Here, we explore the potential for their respective precursor species, glyoxal and SO2, to form atmospheric brown carbon via aqueous-phase reactions in a series of bulk aqueous and flow chamber aerosol experiments. In bulk aqueous solutions, UV- and visible-light-absorbing products are observed at pH 3–4 and 5–6, respectively, with small but detectable yields of hydroxyquinone and polyketone products formed, especially at pH 6. Hydroxymethanesulfonate (HMS), C2, and C3 sulfonates are major products detected by electrospray ionization mass spectrometry (ESI-MS) at pH 5. Past studies have assumed that the reaction of formaldehyde and sulfite was the only atmospheric source of HMS. In flow chamber experiments involving sulfite aerosol and gas-phase glyoxal with only 1 min residence times, significant aerosol growth is observed. Rapid brown carbon formation is seen with aqueous aerosol particles at \u3e80% relative humidity (RH). Brown carbon formation slows at 50–60% RH and when the aerosol particles are acidified with sulfuric acid but stops entirely only under dry conditions. This chemistry may therefore contribute to brown carbon production in cloud-processed pollution plumes as oxidizing volatile organic compounds (VOCs) interact with SO2 and water

Deep learning-based holographic polarization microscopy

Polarized light microscopy provides high contrast to birefringent specimen and is widely used as a diagnostic tool in pathology. However, polarization microscopy systems typically operate by analyzing images collected from two or more light paths in different states of polarization, which lead to relatively complex optical designs, high system costs or experienced technicians being required. Here, we present a deep learning-based holographic polarization microscope that is capable of obtaining quantitative birefringence retardance and orientation information of specimen from a phase recovered hologram, while only requiring the addition of one polarizer/analyzer pair to an existing holographic imaging system. Using a deep neural network, the reconstructed holographic images from a single state of polarization can be transformed into images equivalent to those captured using a single-shot computational polarized light microscope (SCPLM). Our analysis shows that a trained deep neural network can extract the birefringence information using both the sample specific morphological features as well as the holographic amplitude and phase distribution. To demonstrate the efficacy of this method, we tested it by imaging various birefringent samples including e.g., monosodium urate (MSU) and triamcinolone acetonide (TCA) crystals. Our method achieves similar results to SCPLM both qualitatively and quantitatively, and due to its simpler optical design and significantly larger field-of-view, this method has the potential to expand the access to polarization microscopy and its use for medical diagnosis in resource limited settings.Comment: 20 pages, 8 figure