Jurassic shuotheriids show earliest dental diversification of mammaliaforms.

Shuotheriids are Jurassic mammaliaforms that possess pseudotribosphenic teeth in which a pseudotalonid is anterior to the trigonid in the lower molar, contrasting with the tribosphenic pattern of therian mammals (placentals, marsupials and kin) in which the talonid is posterior to the trigonid1-4. The origin of the pseudotribosphenic teeth remains unclear, obscuring our perception of shuotheriid affinities and the early evolution of mammaliaforms1,5-9. Here we report a new Jurassic shuotheriid represented by two skeletal specimens. Their complete pseudotribosphenic dentitions allow reidentification of dental structures using serial homology and the tooth occlusal relationship. Contrary to the conventional view1,2,6,10,11, our findings show that dental structures of shuotheriids can be homologized to those of docodontans and partly support homologous statements for some dental structures between docodontans and other mammaliaforms6,12. The phylogenetic analysis based on new evidence removes shuotheriids from the tribosphenic ausktribosphenids (including monotremes) and clusters them with docodontans to form a new clade, Docodontiformes, that is characterized by pseudotribosphenic features. In the phylogeny, docodontiforms and 'holotherians' (Kuehneotherium, monotremes and therians)13 evolve independently from a Morganucodon-like ancestor with triconodont molars by labio-lingual widening their posterior teeth for more efficient food processing. The pseudotribosphenic pattern passed a cusp semitriangulation stage9, whereas the tribosphenic pattern and its precursor went through a stage of cusp triangulation. The two different processes resulted in complex tooth structures and occlusal patterns that elucidate the earliest diversification of mammaliaforms.

Fossils document evolutionary changes of jaw joint to mammalian middle ear.

The dual jaw joint of Morganucodon1,2 consists of the dentary-squamosal joint laterally and the articular-quadrate one medially. The articular-quadrate joint and its associated post-dentary bones constitute the precursor of the mammalian middle ear. Fossils documenting the transition from such a precursor to the mammalian middle ear are poor, resulting in inconsistent interpretations of this hallmark apparatus in the earliest stage of mammaliaform evolution1-5. Here we report mandibular middle ears from two Jurassic mammaliaforms: a new morganucodontan-like species and a pseudotribosphenic shuotheriid species6. The morganucodontan-like species shows many previously unknown post-dentary bone morphologies1,2 and exhibits features that suggest a loss of load-bearing function in its articular-quadrate joint. The middle ear of the shuotheriid approaches the mammalian condition in that it has features that are suitable for an exclusively auditory function, although the post-dentary bones are still attached to the dentary. With size reduction of the jaw-joint bones, the quadrate shifts medially at different degrees in relation to the articular in the two mammaliaforms. These changes provide evidence of a gradual loss of load-bearing function in the articular-quadrate jaw joint-a prerequisite for the detachment of the post-dentary bones from the dentary7-12 and the eventual breakdown of the Meckel's cartilage13-15 during the evolution of mammaliaforms.

Hyperspectral imaging and dynamic region of interest tracking approaches to quantify localized cAMP signals.

Cyclic adenosine monophosphate (cAMP) is a ubiquitous second messenger known to orchestrate a myriad of cellular functions over a wide range of timescales. In the last 20 years, a variety of single-cell sensors have been developed to measure second messenger signals including cAMP, Ca2+, and the balance of kinase and phosphatase activities. These sensors utilize changes in fluorescence emission of an individual fluorophore or Förster resonance energy transfer (FRET) to detect changes in second messenger concentration. cAMP and kinase activity reporter probes have provided powerful tools for the study of localized signals. Studies relying on these and related probes have the potential to further revolutionize our understanding of G protein-coupled receptor signaling systems. Unfortunately, investigators have not been able to take full advantage of the potential of these probes due to the limited signal-to-noise ratio of the probes and the limited ability of standard epifluorescence and confocal microscope systems to simultaneously measure the distributions of multiple signals (e.g. cAMP, Ca2+, and changes in kinase activities) in real time. In this review, we focus on recently implemented strategies to overcome these limitations: hyperspectral imaging and adaptive thresholding approaches to track dynamic regions of interest (ROI). This combination of approaches increases signal-to-noise ratio and contrast, and allows identification of localized signals throughout cells. These in turn lead to the identification and quantification of intracellular signals with higher effective resolution. Hyperspectral imaging and dynamic ROI tracking approaches offer investigators additional tools with which to visualize and quantify multiplexed intracellular signaling systems.

High-speed fluorescence excitation-scanning hyperspectral imaging microscopy using thin-film tunable filters.

Hyperspectral imaging (HSI) technologies have enabled a range of experimental techniques and studies in the fluorescence microscopy field. Unfortunately, a drawback of many HSI microscope platforms is increased acquisition time required to collect images across many spectral bands, as well as signal loss due to the need to filter or disperse emitted fluorescence into many discrete bands. We have previously demonstrated that an alternative approach of scanning the fluorescence excitation spectrum can greatly improve system efficiency by decreasing light losses associated with emission filtering. Our initial system was configured using an array of thin-film tunable filters (TFTFs, VersaChrome, Semrock) mounted in a tiltable filter wheel (VF-5, Sutter) that required ~150-200 ms to switch between wavelengths. Here, we present a new configuration for high-speed switching of TFTFs to allow rapid time-lapse HSI microscopy. A TFTF array was mounted in a custom holder that was attached to a piezoelectric rotation mount (ThorLabs), allowing high-speed rotation. Switching between adjacent filters was achieved using the internal optics of a DG-4 lightsource (Sutter Instrument), including a pair of off-axis parabolic mirrors and galvanometers. Output light was coupled to a liquid lightguide and into an inverted widefield fluorescence microscope (TI-2, Nikon Instruments). Initial tests indicate that the HSI system provides a 15-20 nm bandwidth tunable excitation band and ~10-20 ms wavelength switch time, allowing for high-speed HSI imaging of dynamic cellular events. This work was supported by NIH P01HL066299, R01HL169522, NIH TL1TR003106, and NSF MRI1725937.

Fluorescence excitation-scanning hyperspectral imaging with scalable 2D-3D deep learning framework for colorectal cancer detection.

Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with over 100,000 estimated cases in 2020 and over 50,000 deaths. The most common screening technique is minimally invasive colonoscopy using either reflected white light endoscopy or narrow-band imaging. However, current imaging modalities have only moderate sensitivity and specificity for lesion detection. We have developed a novel fluorescence excitation-scanning hyperspectral imaging (HSI) approach to sample image and spectroscopic data simultaneously on microscope and endoscope platforms for enhanced diagnostic potential. Unfortunately, fluorescence excitation-scanning HSI datasets pose major challenges for data processing, interpretability, and classification due to their high dimensionality. Here, we present an end-to-end scalable Artificial Intelligence (AI) framework built for classification of excitation-scanning HSI microscopy data that provides accurate image classification and interpretability of the AI decision-making process. The developed AI framework is able to perform real-time HSI classification with different speed/classification performance trade-offs by tailoring the dimensionality of the dataset, supporting different dimensions of deep learning models, and varying the architecture of deep learning models. We have also incorporated tools to visualize the exact location of the lesion detected by the AI decision-making process and to provide heatmap-based pixel-by-pixel interpretability. In addition, our deep learning framework provides wavelength-dependent impact as a heatmap, which allows visualization of the contributions of HSI wavelength bands during the AI decision-making process. This framework is well-suited for HSI microscope and endoscope platforms, where real-time analysis and visualization of classification results are required by clinicians.