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Sarcopenia is a syndrome characterised by progressive and systemic loss of skeletal muscle mass and strength. In order to prevent sarcopenia and lead a healthy life, it is necessary to maintain muscle mass and prevent loss of muscle mass. Insect faeces have long been consumed as tea in China, both as a medicine and as a functional food. In the present work, we investigated the efficacy of cherry caterpillar faeces tea (PT) for treating sarcopenia, particularly concerning muscle building and atrophy suppression using C2C12 cells. PT treatment (0.2 mg/mL) increased myotube widths by approximately 40%, and increased the expression levels of Myod, Myog, and MYHC. Additionally, PGC1α, TFAM, SDHA, BCAT, and BCKDH were upregulated in a PT concentration-dependent manner. For PGC1α, which is the transcription coactivator, the protein expression level also increased in a concentration-dependent manner. The findings demonstrated that PT could stimulate PGC1α and activate mitochondria via branched-chain amino acid metabolism and the electron transport chain in C2C12 myoblasts. Furthermore, PT suppressed LPS-induce expression of IL6 and TNFα, and reduced the protein expression levels of the ubiquitin ligases Atrogin-1 and MuRF, which are major cause of muscle atrophy. These results indicated that PT could be effective for muscle building and suppression of atrophy.

Detection of early signs of stuck pipe incidents is crucial because of the enormous costs of recovering from the incidents. Previous studies have leaned significantly toward a physics-based or data-science approach. However, both approaches have challenges, such as the uncertainty of the physics-based model and the lack of data in the data-science approach. This study proposes a hybrid approach using physical insights and data science and discusses the possibility of early detection of stuck pipes. The proposed method comprises two steps. In the first step, a data-driven model with physical insights is trained using the historical data of the in situ well to estimate some of the drilling variables. In the second step, the risk of stuck pipe occurrence (hereafter referred to as stuck risk) is calculated based on the historical and current measured data and the estimation of the trained model. This approach is expected to overcome the limitations of the previous methods as it allows the construction of a detection model tuned to the in situ well. In the case studies, models for estimating the top drive torque and standpipe pressure were constructed. The performance of the models is discussed using actual drilling data from drilling fields, including 21 stuck incidents during drilling operations. The proposed method was first examined using short-term output. The output confirmed that the stuck risk increased shortly before the stuck incident occurred in 15 cases. This increase in stuck risk was consistent with physical considerations. Subsequently, this study examined the long-term output over several months; this was rarely done in previous studies. Few false positives were observed in several cases even within this long-term output. Additionally, several model improvements were found to have the potential to further improve its performance. The novelty of our research lies in creating a broad framework for the early sign detection of stuck pipes by using both physical insights and data science methods. The proposed hybrid approach demonstrated the potential to reduce false alarms and improve interpretability compared to previous methods. The framework is highly extensible, and further performance improvements can be expected in the future.

Quantitative

Recently, quantitative NMR (qNMR), especially 1H-qNMR, has been widely used to determine the absolute quantitative value of organic molecules. We previously reported an optimal and reproducible sample preparation method for 1H-qNMR. In the present study, we focused on a 31P-qNMR absolute determination method. An organophosphorus compound, cyclophosphamide hydrate (CP), listed in the Japanese Pharmacopeia 17th edition was selected as the target compound, and the 31P-qNMR and 1H-qNMR results were compared under three conditions with potassium dihydrogen phosphate (KH2PO4) or O-phosphorylethanolamine (PEA) as the reference standard for 31P-qNMR and sodium 4,4-dimethyl-4-silapentanesulfonate-d6 (DSS-d6) as the standard for 1H-qNMR. Condition 1: separate sample containing CP and KH2PO4 for 31P-qNMR or CP and DSS-d6 for 1H-qNMR. Condition 2: mixed sample containing CP, DSS-d6, and KH2PO4. Condition 3: mixed sample containing CP, DSS-d6, and PEA. As conditions 1 and 3 provided good results, validation studies at multiple laboratories were further conducted. The purities of CP determined under condition 1 by 1H-qNMR at 11 laboratories and 31P-qNMR at 10 laboratories were 99.76 ± 0.43 and 99.75 ± 0.53%, respectively, and those determined under condition 3 at five laboratories were 99.66 ± 0.08 and 99.61 ± 0.53%, respectively. These data suggested that the CP purities determined by 31P-qNMR are in good agreement with those determined by the established 1H-qNMR method. Since the 31P-qNMR signals are less complicated than the 1H-qNMR signals, 31P-qNMR would be useful for the absolute quantification of compounds that do not have a simple and separate 1H-qNMR signal, such as a singlet or doublet, although further investigation with other compounds is needed.

Carbon fibers are highly anisotropic materials and it is necessary to understand their mechanical properties in various directions as well as their axial tensile property, for accurate prediction o...

Summary A real-time stuck pipe prediction using the deep-learning approach is studied in this paper. Early signs of stuck pipe, hereinafter called stuck, are assumed to show common patterns in the monitored data set, and designing a data clip that well captures these features is critical for efficient prediction. With the valuable input from drilling engineers, we propose a 3D-convolutional neural network (CNN) approach with depth-domain data clip. The clip illustrates depth-domain data in 2D-histogram images with unique abstraction of the time domain. Thirty field well data prepared in multivariate time series are used in this study—20 for training and 10 for validation. The validation data include six stuck incidents, and the 3D-CNN model has successfully detected early signs of stuck in three cases before the occurrence. The portion of the data clip contributing to anomaly detection is indicated by gradient-weighted class activation map (grad-CAM), providing physical explanation of the black box model. We consider such explanation inevitable for the drilling engineers to interpret the signs for rational decision-making.

Quantum anomalous Hall (QAH) effect appears in ferromagnetic topological insulators (FMTI) when a Dirac mass gap opens in the spectrum of the topological surface states (SS). Unaccountably, although the mean mass gap can exceed 28 meV (or ~320 K), the QAH effect is frequently only detectable at temperatures below 1 K. Using atomic-resolution Landau level spectroscopic imaging, we compare the electronic structure of the archetypal FMTI Cr_0.08(Bi_0.1Sb_0.9)_1.92Te_3 to that of its non-magnetic parent (Bi_0.1Sb_0.9)_2Te_3, to explore the cause. In (Bi_0.1Sb_0.9)_2Te_3, we find spatially random variations of the Dirac energy. Statistically equivalent Dirac energy variations are detected in Cr_0.08(Bi_0.1Sb_0.9)_1.92Te_3 with concurrent but uncorrelated Dirac mass gap disorder. These two classes of SS electronic disorder conspire to drastically suppress the minimum mass gap to below 100 {\mu}eV for nanoscale regions separated by

NMR spectroscopy has recently been utilized to determine the absolute amounts of organic molecules with metrological traceability since signal intensity is directly proportional to the number of each nucleus in a molecule. The NMR methodology that uses hydrogen nucleus (1H) to quantify chemicals is called quantitative 1H-NMR (1H qNMR). The quantitative method using 1H qNMR for determining the purity or content of chemicals has been adopted into some compendial guidelines and official standards. However, there are still few reports in the literature regarding validation of 1H qNMR methodology. Here, we coordinated an international collaborative study to validate a 1H qNMR based on the use of an internal calibration methodology. Thirteen laboratories participated in this study, and the purities of three samples were individually measured using 1H qNMR method. The three samples were all certified via conventional primary methods of measurement, such as butyl p-hydroxybenzoate Japanese Pharmacopeia (JP) reference standard certified by mass balance; benzoic acid certified reference material (CRM) certified by coulometric titration; fludioxonil CRM certified by a combination of freezing point depression method and 1H qNMR. For each sample, 1H qNMR experiments were optimized before quantitative analysis. The results showed that the measured values of each sample were equivalent to the corresponding reference labeled value. Furthermore, assessment of these 1H qNMR data using the normalized error, En-value, concluded that statistically 1H qNMR has the competence to obtain the same quantification performance and accuracy as the conventional primary methods of measurement.

Bone loss and bone-related disease are associated with the deregulation of osteoclast function, and therefore agents that affect osteoclastogenesis have attracted attention. The purpose of the present study was to discover modified kavalactone analogs as potential anti-osteoclastogenic agents. We assessed the effect of 26 analogs on osteoclast differentiation in vitro. The most potent compound, (E)-6-(2-fluorostyryl)-4-methoxy-2H-pyran-2-one (22), suppressed receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclastogenic differentiation of RAW264 cells with IC50 values of 4.3 µM. A partial structure-activity relationship study revealed the importance of fluorine and its position within the 5,6-dehydrokawain skeleton. The results of a pit formation assay suggested that compound 22 prevents osteoclastic bone resorption by inhibiting osteoclastogenesis. Moreover, compound 22 downregulated mRNA expression levels of RANKL-induced nuclear factor of activated T cells c1 (NFATc1) and osteoclastogenesis-related genes. These results suggest that (E)-6-(2-fluorostyryl)-4-methoxy-2H-pyran-2-one scaffold could lead to the identification of new anti-resorptive agents.

Breast cancer is the most frequently found cancer in women and the one most often subjected to genetic analysis. Nonetheless, it has been causing the largest number of women's cancer-related deaths. PAM50, the intrinsic subtype assay for breast cancer, is beneficial for diagnosis and stratified treatment but does not explain each subtype's mechanism. Nowadays, deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods. However, the previous studies did not directly use deep learning to examine which genes associate with the subtypes. Ours is the first study on a deep-learning approach to reveal the mechanisms embedded in the PAM50-classified subtypes. We developed an explainable deep learning model called a point-wise linear model, which uses a meta-learning approach to generate a custom-made logistic regression model for each sample. Logistic regression is familiar to physicians and medical informatics researchers, and we can use it to analyze which genes are important for subtype prediction. The custom-made logistic regression models generated by the point-wise linear model for each subtype used the specific genes selected in other subtypes compared to the conventional logistic regression model: the overlap ratio is less than twenty percent. And analyzing the point-wise linear model's inner state, we found that the point-wise linear model used genes relevant to the cell cycle-related pathways. The results of this study suggest the potential of our explainable deep learning to play a vital role in cancer treatment.

Essentials of the scientific discovery process have remained largely unchanged for centuries: systematic human observation of natural phenomena is used to form hypotheses that, when validated through experimentation, are generalized into established scientific theory. Today, however, we face major challenges because automated instrumentation and large-scale data acquisition are generating data sets of such volume and complexity as to defy human analysis. Radically different scientific approaches are needed, with machine learning (ML) showing great promise, not least for materials science research. Hence, given recent advances in ML analysis of synthetic data representing electronic quantum matter (EQM), the next challenge is for ML to engage equivalently with experimental data. For example, atomic-scale visualization of EQM yields arrays of complex electronic structure images, that frequently elude effective analyses. Here we report development and training of an array of artificial neural networks (ANN) designed to recognize different types of hypothesized order hidden in EQM image-arrays. These ANNs are used to analyze an experimentally-derived EQM image archive from carrier-doped cuprate Mott insulators. Throughout these noisy and complex data, the ANNs discover the existence of a lattice-commensurate, four-unit-cell periodic, translational-symmetry-breaking EQM state. Further, the ANNs find these phenomena to be unidirectional, revealing a coincident nematic EQM state. Strong-coupling theories of electronic liquid crystals are congruent with all these observations., 44 pages, 15 figures

A semi-implicit dispersive thin-wire finite-difference time-domain (FDTD) scheme is proposed for fast analysis of waveguide metamaterials. The proposed scheme is based on combining the Newmark-beta method with Makinen’s improved thin-wire FDTD model based on the contour-path integral of Maxwell’s equations around a wire corrected with scaling factors and Wait’s surface-impedance boundary condition for thin wires of finite conductivity. Its stability condition is determined by only the two meshes in the transverse plane to the wire axis. This feature allows using larger mesh steps than a wire radius without time step reducing. In order to verify the scheme, we demonstrate four device applications: plasmonic parallel-plate waveguide filled with only positive dielectrics, edge Fabry–Perot resonator, waveguide metatronic high-pass filter, and coaxial-to-waveguide matching with an epsilon-near-zero narrow channel. Its stability, accuracy, and computational efficiency are assessed in comparison with the standard FDTD explicit scheme, analytical solutions, and existing experimental data.

Differentiating the intrinsic subtypes of breast cancer is crucial for deciding the best treatment strategy. Deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods, but to date, deep learning has not been directly utilized to examine which genes are associated with which subtypes. To clarify the mechanisms embedded in the intrinsic subtypes, we developed an explainable deep learning model called a point-wise linear (PWL) model that generates a custom-made logistic regression for each patient. Logistic regression, which is familiar to both physicians and medical informatics researchers, allows us to analyze the importance of the feature variables, and the PWL model harnesses these practical abilities of logistic regression. In this study, we show that analyzing breast cancer subtypes is clinically beneficial for patients and one of the best ways to validate the capability of the PWL model. First, we trained the PWL model with RNA-seq data to predict PAM50 intrinsic subtypes and applied it to the 41/50 genes of PAM50 through the subtype prediction task. Second, we developed a deep enrichment analysis method to reveal the relationships between the PAM50 subtypes and the copy numbers of breast cancer. Our findings showed that the PWL model utilized genes relevant to the cell cycle-related pathways. These preliminary successes in breast cancer subtype analysis demonstrate the potential of our analysis strategy to clarify the mechanisms underlying breast cancer and improve overall clinical outcomes., 25 pages, 5 figures

Recently, quantitative NMR (qNMR), especially