A non-carboxylating pentose bisphosphate pathway in halophilic archaea

Bacteria and Eucarya utilize the non-oxidative pentose phosphate pathway to direct the ribose moieties of nucleosides to central carbon metabolism. Many archaea do not possess this pathway, and instead, Thermococcales utilize a pentose bisphosphate pathway involving ribose-1, 5-bisphosphate (R15P) isomerase and ribulose-1, 5-bisphosphate (RuBP) carboxylase/oxygenase (Rubisco). Intriguingly, multiple genomes from halophilic archaea seem only to harbor R15P isomerase, and do not harbor Rubisco. In this study, we identify a previously unrecognized nucleoside degradation pathway in halophilic archaea, composed of guanosine phosphorylase, ATP-dependent ribose-1-phosphate kinase, R15P isomerase, RuBP phosphatase, ribulose-1-phosphate aldolase, and glycolaldehyde reductase. The pathway converts the ribose moiety of guanosine to dihydroxyacetone phosphate and ethylene glycol. Although the metabolic route from guanosine to RuBP via R15P is similar to that of the pentose bisphosphate pathway in Thermococcales, the downstream route does not utilize Rubisco and is unique to halophilic archaea

Chord-aware automatic music transcription based on hierarchical Bayesian integration of acoustic and language models

This paper describes automatic music transcription with chord estimation for music audio signals. We focus on the fact that concurrent structures of musical notes such as chords form the basis of harmony and are considered for music composition. Since chords and musical notes are deeply linked with each other, we propose joint pitch and chord estimation based on a Bayesian hierarchical model that consists of an acoustic model representing the generative process of a spectrogram and a language model representing the generative process of a piano roll. The acoustic model is formulated as a variant of non-negative matrix factorization that has binary variables indicating a piano roll. The language model is formulated as a hidden Markov model that has chord labels as the latent variables and emits a piano roll. The sequential dependency of a piano roll can be represented in the language model. Both models are integrated through a piano roll in a hierarchical Bayesian manner. All the latent variables and parameters are estimated using Gibbs sampling. The experimental results showed the great potential of the proposed method for unified music transcription and grammar induction

A screening method for cervical myelopathy using machine learning to analyze a drawing behavior

Abstract Early detection of cervical myelopathy (CM) is important for a favorable outcome, as its prognosis is poor when left untreated. We developed a screening method for CM using machine learning-based analysis of the drawing behavior of 38 patients with CM and 66 healthy volunteers. Using a stylus pen, the participants traced three different shapes displayed on a tablet device. During the tasks, writing behaviors, such as the coordinates, velocity, and pressure of the stylus tip, along with the drawing time, were recorded. From these data, features related to the drawing pressure, and time to trace each shape and combination of shapes were used as training data for the support vector machine, a machine learning algorithm. To evaluate the accuracy, a receiver operating characteristic curve was generated, and the area under the curve (AUC) was calculated. Models with triangular waveforms tended to be the most accurate. The best triangular wave model identified patients with and without CM with 76% sensitivity and 76% specificity, yielding an AUC of 0.80. Our model was able to classify CM with high accuracy and could be applied to the development of disease screening systems useful outside the hospital setting

Health Literacy among Japanese College Students: Association with Healthy Lifestyle and Subjective Health Status

Healthcare. 2023, 11 (5), P.70

Determination of optimal regularization factor in Bayesian penalized likelihood reconstruction of brain PET images using [ F]FDG and [ C]PiB.

The Bayesian penalized likelihood (BPL) reconstruction algorithm, Q.Clear, can achieve a higher signal-to-noise ratio on images and more accurate quantitation than ordered subset-expectation maximization (OSEM). The reconstruction parameter (β) in BPL requires optimization according to the radiopharmaceutical tracer. The present study aimed to define the optimal β value in BPL required to diagnose Alzheimer disease from brain positron emission tomography (PET) images acquired using F-fluoro-2-deoxy-D-glucose ([ F]FDG) and C-labeled Pittsburg compound B ([ C]PiB)

Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists

The study aims to identify histological classifiers from histopathological images of oral squamous cell carcinoma using convolutional neural network (CNN) deep learning models and shows how the results can improve diagnosis. Histopathological samples of oral squamous cell carcinoma were prepared by oral pathologists. Images were divided into tiles on a virtual slide, and labels (squamous cell carcinoma, normal, and others) were applied. VGG16 and ResNet50 with the optimizers stochastic gradient descent with momentum and spectral angle mapper (SAM) were used, with and without a learning rate scheduler. The conditions for achieving good CNN performances were identified by examining performance metrics. We used ROCAUC to statistically evaluate diagnostic performance improvement of six oral pathologists using the results from the selected CNN model for assisted diagnosis. VGG16 with SAM showed the best performance, with accuracy = 0.8622 and AUC = 0.9602. The diagnostic performances of the oral pathologists statistically significantly improved when the diagnostic results of the deep learning model were used as supplementary diagnoses (p-value = 0.031). By considering the learning results of deep learning model classifiers, the diagnostic accuracy of pathologists can be improved. This study contributes to the application of highly reliable deep learning models for oral pathological diagnosis

Edge‐Site‐Free and Topological‐Defect‐Rich Carbon Cathode for High‐Performance Lithium‐Oxygen Batteries

Abstract The rational design of a stable and catalytic carbon cathode is crucial for the development of rechargeable lithium‐oxygen (LiO2) batteries. An edge‐site‐free and topological‐defect‐rich graphene‐based material is proposed as a pure carbon cathode that drastically improves LiO2 battery performance, even in the absence of extra catalysts and mediators. The proposed graphene‐based material is synthesized using the advanced template technique coupled with high‐temperature annealing at 1800 °C. The material possesses an edge‐site‐free framework and mesoporosity, which is crucial to achieve excellent electrochemical stability and an ultra‐large capacity (>6700 mAh g−1). Moreover, both experimental and theoretical structural characterization demonstrates the presence of a significant number of topological defects, which are non‐hexagonal carbon rings in the graphene framework. In situ isotopic electrochemical mass spectrometry and theoretical calculations reveal the unique catalysis of topological defects in the formation of amorphous Li2O2, which may be decomposed at low potential (∼ 3.6 V versus Li/Li+) and leads to improved cycle performance. Furthermore, a flexible electrode sheet that excludes organic binders exhibits an extremely long lifetime of up to 307 cycles (>1535 h), in the absence of solid or soluble catalysts. These findings may be used to design robust carbon cathodes for LiO2 batteries