Notes

Coherent manipulation of an Andreev rewrite qubit.
Pyridoxal-5'-phosphate (PLP), the active form of vitamin B6, is an important and versatile coenzyme involved in a variety of enzymatic reactions, accounting for about 4% of all classified activities. However, the detailed catalytic reaction pathways for PLP-dependent enzymes remain to be explored. Methionine-γ-lyase (MGL), a promising alternative anti-tumor agent to conventional chemotherapies whose catalytic mechanism is highly desired for guiding further development of re-engineered enzymes, was used as a representative PLP-dependent enzyme, and the catalytic mechanism for L-Met elimination by MGL was explored at the first-principles quantum mechanical/molecular mechanical (QM/MM) level with umbrella sampling. The QM/MM calculations revealed that the enzymatic reaction pathway consists of 4 stages for a total of 19 reaction steps with five intermediates captured in available crystal structures. Furthermore, the more comprehensive role of PLP was revealed. Besides the commonly known role of "electron sink", coenzyme PLP can also assist proton transfer and temporarily store the excess proton generated in some intermediate states by using its hydroxyl group and phosphate group. Thus, PLP is participated in most of the 19 steps. This study not only provided a theoretical basis for further development and re-engineering MGL as a potential anti-tumor agent, but also revealed the comprehensive role of PLP which could be used to explore the mechanisms of other PLP-dependent enzymes.Up to now, methods for measuring rates of reactions on catalysts required long measurement times involving signal averaging over many experiments. This imposed a requirement that the catalyst return to its original state at the end of each experiment-a complete reversibility requirement. For real catalysts, fulfilling the reversibility requirement is often impossible-catalysts under reaction conditions may change their chemical composition and structure as they become activated or while they are being poisoned through use. It is therefore desirable to develop high-speed methods where transient rates can be quickly measured while catalysts are changing. In this work, we present velocity-resolved kinetics using high-repetition-rate pulsed laser ionization and high-speed ion imaging detection. The reaction is initiated by a single molecular beam pulse incident at the surface, and the product formation rate is observed by a sequence of pulses produced by a high-repetition-rate laser. Ion imaging provides the desorbing product flux (reaction rate) as a function of reaction time for each laser pulse. We demonstrate the principle of this approach by rate measurements on two simple reactions CO desorption from and CO oxidation on the 332 facet of Pd. This approach overcomes the time-consuming scanning of the delay between CO and laser pulses needed in past experiments and delivers a data acquisition rate that is 10-1000 times higher. We are able to record kinetic traces of CO2 formation while a CO beam titrates oxygen atoms from an O-saturated surface. This approach also allows measurements of reaction rates under diffusion-controlled conditions.Rhodium nanoparticles (NPs) immobilized on imidazolium-based supported ionic liquid phases (Rh@SILP) act as effective catalysts for the hydrogenation of biomass-derived furfuralacetone. learn more The structure of ionic liquid-type (IL) molecular modifiers was systematically varied regarding spacer, side chain, and anion to assess the influence on the NP synthesis and their catalytic properties. Well-dispersed Rh NPs with diameters in the range of 0.6-2.0 nm were formed on all SILP materials, whereby the actual size was dependent significantly on the IL structure. The resulting variations in catalytic activity for hydrogenation of the C=O moiety in furfuralacetone allowed control of the product selectivity to obtain either the saturated alcohol or the ketone in high yield. Experiments conducted under batch and continuous flow conditions demonstrated that Rh NPs immobilized on SILPs with suitable IL structures are more active and much more stable than Rh@SiO2 catalyst synthesized on unmodified silica.
Prescreening of biopsies has the potential to improve pathologists' workflow. Tools that identify features and display results in a visually thoughtful manner can enhance efficiency, accuracy, and reproducibility. Machine learning for detection of glomeruli ensures comprehensive assessment and registration of four different stains allows for simultaneous navigation and viewing.

Medical renal core biopsies (4 stains each) were digitized using a Leica SCN400 at ×40 and loaded into the Corista Quantum research platform. Glomeruli were manually annotated by pathologists. The tissue on the 4 stains was registered using a combination of keypoint- and intensity-based algorithms, and a 4-panel simultaneous viewing display was created. Using a training cohort, machine learning convolutional neural net (CNN) models were created to identify glomeruli in all stains, and merged into composite fields of views (FOVs). The sensitivity and specificity of glomerulus detection, and FOV area for each detection were calculates task. The added benefit of biopsy registration with simultaneous display and navigation allows reviewers to move from one machine-generated FOV to the next in all 4 stains. Together these features could increase both efficiency and accuracy in the review process.A whole-slide imaging (WSI) system is a digital color imaging system used in digital pathology with the potential to substitute the conventional light microscope. A WSI system digitalizes a glass slide by converting the optical image to digital data with a scanner and then converting the digital data back to the optical image with a display. During the digital-to-optical or optical-to-digital conversion, a color space is required to define the mapping between the digital domain and the optical domain so that the numerical data of each color pixel can be interpreted meaningfully. Unfortunately, many current WSI products do not specify the designated color space clearly, which leaves the user using the universally default color space, sRGB. sRGB is a legacy color space that has a limited color gamut, which is known to be unable to reproduce all color shades present in histology slides. In this work, experiments were conducted to quantitatively investigate the limitation of the sRGB color space used in WSI systems. Eight hematoxylin and eosin (H and E)-stained tissue samples, including human bladder, brain, breast, colon, kidney, liver, lung, and uterus, were measured with a multispectral imaging system to obtain the true colors at the pixel level. The measured color truth of each pixel was converted into the standard CIELAB color space to test whether it was within the color gamut of the sRGB color space. Experiment results show that all the eight images have a portion of pixels outside the sRGB color gamut. In the worst-case scenario, the bladder sample, about 35% of the image exceeded the sRGB color gamut. The results suggest that the sRGB color space is inadequate for WSI scanners to encode H and E-stained whole-slide images, and an sRGB display may have insufficient color gamut for displaying H and E-stained histology images.
Clinicopathological scores are used to predict the likelihood of recurrence-free survival for patients with clear cell renal cell carcinoma (ccRCC) after surgery. These are fallible, particularly in the middle range. This inevitably means that a significant proportion of ccRCC patients who will not develop recurrent disease enroll into clinical trials. As an exemplar of using digital pathology, we sought to improve the predictive power of "recurrence free" designation in localized ccRCC patients, by precise measurement of ccRCC nuclear morphological features using computational image analysis, thereby replacing manual nuclear grade assessment.

TNM 8 UICC pathological stage pT1-pT3 ccRCC cases were recruited in Scotland and in Singapore. A Leibovich score (LS) was calculated. Definiens Tissue studio® (Definiens GmbH, Munich) image analysis platform was used to measure tumor nuclear morphological features in digitized hematoxylin and eosin (H&E) images.

Replacing human-defined nuclear grade with compusured by computational image analysis, together with tumor stage and size, node status and necrosis improved the accuracy of predicting recurrence-free in the localized ccRCC patients. This finding was validated in an ethnically different Singaporean cohort, despite the different H and E staining protocol and scanner used. This may be a useful patient selection tool for recruitment to multicenter studies, preventing some patients from receiving unnecessary additional treatment while reducing the number of patients required to achieve adequate power within neoadjuvant and adjuvant clinical studies.
The microscope high-power field (HPF) is the cornerstone for histopathology diagnostic evaluation such as the quantification of mitotic figures, lymphocytes, and tumor grading. With traditional light microscopy, HPFs are typically evaluated by quantifying histologic events in 10 fields of view at × 400 magnification. In the era of digital pathology, new variables are introduced that may affect HPF evaluation. The aim of this study was to determine the parameters that influence HPF in whole slide images (WSIs).

Glass slides scanned on various devices (Leica's Aperio GT450, AT2, and ScanScope XT; Philips UltraFast Scanner; Hamamatsu's Nanozoomer 2.0HT; and 3DHistech's P1000) were compared to acquired digital slides reviewed on each vendor's respective WSI viewer software (e.g., Aperio ImageScope, ImageScope DX, Philips IMS, 3DHistech CaseViewer, and Hamamatsu NDP.view) and an in-house developed vendor-agnostic viewer. WSIs were reviewed at "×40" equivalent HPF on different sized monitors with varying displaonventional light microscopy was not equivalent to "×40" digital HPF areas. Digital HPF quantification may vary due to differences in the tissue area displayed by monitor sizes, display resolutions, and WSI viewers but not by scanner or scanning resolution. These findings will need to be further clinically validated with potentially new digital metrics for evaluation.
Determining the site of origin for metastatic well-differentiated neuroendocrine tumors (WDNETs) is challenging, and immunohistochemical (IHC) profiles do not always lead to a definitive diagnosis. We sought to determine if a deep-learning convolutional neural network (CNN) could improve upon established IHC profiles in predicting the site of origin in a cohort of WDNETs from the common primary sites.

Hematoxylin and eosin (H&E)-stained tissue microarrays (TMAs) were created using 215 WDNETs arising from the known primary sites. A CNN trained and tested on 60% (
= 130) and 40% (
= 85) of these cases, respectively. One hundred and seventy-nine cases had TMA tissue remaining for the IHC analysis. These cases were stained with IHC markers pPAX8, CDX2, SATB2, and thyroid transcription factor-1 (markers of pancreas/duodenum, ileum/jejunum/duodenum, colorectum/appendix, and lung WDNET sites of origin, respectively). The CNN diagnosis was deemed correct if it designated a majority or plurality of the tumor area as the known site of origin.
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