Dirac-Majorana neutrino type conversion induced by an oscillating scalar dark matter

Some properties of a neutrino may differ significantly depending on whether it is Dirac or Majorana type. The type is determined by the relative size of Dirac and Majorana masses, which may vary if they arise from an oscillating scalar dark matter. We show that the change can be significant enough to convert the neutrino type between Dirac and Majorana while satisfying constraints on the dark matter. It predicts periodic modulations in the event rates in various neutrino phenomena. As the energy density and, thus, the oscillation amplitude of the dark matter evolves in the cosmic time scale, the relative size of Dirac and Majorana masses changes accordingly. It provides an interesting link between the present-time neutrino physics to the early universe cosmology including the leptogenesis.Comment: Added more discussions and reference

An Active and Soft Hydrogel Actuator to Stimulate Live Cell Clusters by Self-folding

The hydrogels are widely used in various applications, and their successful uses depend on controlling the mechanical properties. In this study, we present an advanced strategy to develop hydrogel actuator designed to stimulate live cell clusters by self-folding. The hydrogel actuator consisting of two layers with different expansion ratios were fabricated to have various curvatures in self-folding. The expansion ratio of the hydrogel tuned with the molecular weight and concentration of gel-forming polymers, and temperature-sensitive molecules in a controlled manner. As a result, the hydrogel actuator could stimulate live cell clusters by compression and tension repeatedly, in response to temperature. The cell clusters were compressed in the 0.7-fold decreases of the radius of curvature with 1.0 mm in room temperature, as compared to that of 1.4 mm in 37 degrees C. Interestingly, the vascular endothelial growth factor (VEGF) and insulin-like growth factor-binding protein-2 (IGFBP-2) in MCF-7 tumor cells exposed by mechanical stimulation was expressed more than in those without stimulation. Overall, this new strategy to prepare the active and soft hydrogel actuator would be actively used in tissue engineering, drug delivery, and micro-scale actuators

The effect of hidden female smoking on the relationship between smoking and cardiovascular disease

Background: Smoking is a known risk factor for cardiovascular morbidity and mortality, but several Korean studies have shown differing results on the association of current smoking status and the risk of cardiovascular disease (CVD). The aim of the present study was to investigate the association between smoking status and CVD (myocardial infarction and stroke) using national representative populationbased samples. The aim was also to investigate the effects of hidden smokers on the association between CVD and smoking.Methods: Data were acquired from 28,620 participants (12,875 men and 15,745 women), age 19 years or older, who participated in the Korea National Health and Nutrition Examination Survey (KNHANES) conducted from 2008 to 2016.Results: The multivariable logistic regression analysis showed that ex-smoking status was correlated with CVD when self-reported (odds ratio [OR]: 1.62; 95% confidence interval [CI]: 1.20–2.19) and for survey-cotinine verified-smoking status (OR: 1.57; 95% CI: 1.20–2.19). Interestingly, the present study showed current smoking was not significantly associated with CVD. For the effect of sex on smoking and CVD, self-reported and survey-cotinine-verified ex-smoking status were correlated with CVD in males (OR: 1.45; 95% CI: 1.04–2.04 and OR: 1.43; 95% CI: 1.02–2.02) and in females (OR: 2.74; 95% CI: 1.59–4.71 and OR: 2.92; 95% CI: 1.64–5.18). The ratios of cotinine-verified to self-reported smoking rates were 1.95 for women and 1.08 for men.Conclusions: In the current study, while ex-smoking status was significantly associated with CVD, current smoking status was not. Female ex-smoking status had a higher adjusted odds ratio for CVD than males compared to non-smoking status. An effect of hidden female smoking was also found on the association between smoking status and CVD in Korean adults

GPX8 regulates clear cell renal cell carcinoma tumorigenesis through promoting lipogenesis by NNMT

Background Clear cell renal cell carcinoma (ccRCC), with its hallmark phenotype of high cytosolic lipid content, is considered a metabolic cancer. Despite the implication of this lipid-rich phenotype in ccRCC tumorigenesis, the roles and regulators of de novo lipid synthesis (DNL) in ccRCC remain largely unexplained. Methods Our bioinformatic screening focused on ccRCC-lipid phenotypes identified glutathione peroxidase 8 (GPX8), as a clinically relevant upstream regulator of DNL. GPX8 genetic silencing was performed with CRISPR-Cas9 or shRNA in ccRCC cell lines to dissect its roles. Untargeted metabolomics, RNA-seq analyses, and other biochemical assays (e.g., lipid droplets staining, fatty acid uptake, cell proliferation, xenograft, etc.) were carried out to investigate the GPX8s involvement in lipid metabolism and tumorigenesis in ccRCC. The lipid metabolic function of GPX8 and its downstream were also measured by isotope-tracing-based DNL flux measurement. Results GPX8 knockout or downregulation substantially reduced lipid droplet levels (independent of lipid uptake), fatty acid de novo synthesis, triglyceride esterification in vitro, and tumor growth in vivo. The downstream regulator was identified as nicotinamide N-methyltransferase (NNMT): its knockdown phenocopied, and its expression rescued, GPX8 silencing both in vitro and in vivo. Mechanically, GPX8 regulated NNMT via IL6-STAT3 signaling, and blocking this axis suppressed ccRCC survival by activating AMPK. Notably, neither the GPX8-NNMT axis nor the DNL flux was affected by the von Hippel Lindau (VHL) status, the conventional regulator of ccRCC high lipid content. Conclusions Taken together, our findings unravel the roles of the VHL-independent GPX8-NNMT axis in ccRCC lipid metabolism as related to the phenotypes and growth of ccRCC, which may be targeted for therapeutic purposes. Graphical abstractThe research was supported by the Basic Science Research Program (grant NRF-2018R1A3B1052328 to S.P.) funded by the Ministry of Science, Information and Communication Technology, by Future Planning through the National Research Foundation, and by the Basic Science Research Program through the National Research Foundation (NRF-2020R1I1A1A01073124 to J-M.K.) funded by the Ministry of Education of Korea

Observation of tW production in the single-lepton channel in pp collisions at root s=13 TeV

A measurement of the cross section of the associated production of a single top quark and a W boson in final states with a muon or electron and jets in proton-proton collisions at root s = 13 TeV is presented. The data correspond to an integrated luminosity of 36 fb(-1) collected with the CMS detector at the CERN LHC in 2016. A boosted decision tree is used to separate the tW signal from the dominant t (t) over bar background, whilst the subleading W+jets and multijet backgrounds are constrained using data-based estimates. This result is the first observation of the tW process in final states containing a muon or electron and jets, with a significance exceeding 5 standard deviations. The cross section is determined to be 89 +/- 4 (stat) +/- 12 (syst) pb, consistent with the standard model.Peer reviewe

Rh(III)-catalyzed 7-azaindole synthesis via C-H activation/annulative coupling of aminopyridines with alkynes

An efficient Rh(III)-catalyzed 7-azaindole synthesis was developed via C-H activation/annulative coupling of aminopyridines with alkynes. The reaction was highly regioselective and tolerated various functional groups, permitting the construction of various 7-azaindoles. © The Royal Society of Chemistry 20151171sciescopu

A copper-mediated cross-coupling approach for the synthesis of 3-heteroaryl quinolone and related analogues

An efficient and practical method for the direct cross-coupling between quinolones and a range of azoles was developed via copper-mediated C-H functionalization. This synthetic strategy provides a convenient access to a variety of C3-heteroaryl quinolones, quinolinone, nalidixic acid, uracil, pyridone and chromone derivatives, which are prominent structural motifs in many biologically active compounds.1991sciescopu

Glu-Ensemble: An ensemble deep learning framework for blood glucose forecasting in type 2 diabetes patients

Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels, posing significant health risks such as cardiovascular disease, and nerve, kidney, and eye damage. Effective management of blood glucose is essential for individuals with diabetes to mitigate these risks. This study introduces the Glu-Ensemble, a deep learning framework designed for precise blood glucose forecasting in patients with type 2 diabetes. Unlike other predictive models, Glu-Ensemble addresses challenges related to small sample sizes, data quality issues, reliance on strict statistical assumptions, and the complexity of models. It enhances prediction accuracy and model generalizability by utilizing larger datasets and reduces bias inherent in many predictive models. The framework's unified approach, as opposed to patient-specific models, eliminates the need for initial calibration time, facilitating immediate blood glucose predictions for new patients. The obtained results indicate that Glu-Ensemble surpasses traditional methods in accuracy, as measured by root mean square error, mean absolute error, and error grid analysis. The Glu-Ensemble framework emerges as a promising tool for blood glucose level prediction in type 2 diabetes patients, warranting further investigation in clinical settings for its practical application

A Machine Learning Trading System for the Stock Market based on N-Period Min-Max Labeling using XGBoost

Many researchers attempt to accurately predict stock price trends using technologies such as machine learning and deep learning to achieve high returns in the stock market. However, it is difficult to predict the exact trend since stock prices are nonlinear and often appear random. To improve accuracy, the focus of modelers usually lies in improving the performance of the prediction model. However, examining the data used in training the model is imperative. Most studies of stock price trend prediction use an up-down labeling that labels data at all time points. The drawback of this labeling method is that it is sensitive to small price changes, causing inefficient model training. Therefore, this study proposes an N-Period Min-Max (NPMM) labeling that labels data only at definite time points to help overcome small price change sensitivity. The proposed model also develops a trading system using XGBoost to automate trading and verify the proposed labeling method. The proposed trading system is evaluated through an empirical analysis of 92 companies listed on the NASDAQ. Moreover, the trading performance of the proposed labeling method is compared against other prominent labeling methods. In this study, NPMM labeling was found to be an efficient labeling method for stock price trend prediction, in addition to generating trading outperformance compared to other labeling methods