Long-range SS-wave DD∗DD^* interaction in covariant chiral effective field theory

Motivated by the recent lattice QCD study of the $DD^*$ interaction at unphysical quark masses, we perform a theoretical study of the $DD^*$ interaction in covariant chiral effective field theory (ChEFT). In particular, we calculate the relevant leading-order two-pion exchange contributions. The results compare favorably with the lattice QCD results, supporting the conclusion that the intermediate-range $DD^*$ interaction is dominated by two-pion exchanges and the one-pion exchange contribution is absent. At a quantitative level, the covariant ChPT results agree better with the lattice QCD results than their non-relativistic counterparts, showing the relevance of relativistic corrections in the charm sector.Comment: 12 pages, 7 figure

Artificial Intelligence in the Radiomic Analysis of Glioblastomas: A Review, Taxonomy, and Perspective

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area

Artificial Intelligence in the Radiomic Analysis of Glioblastomas: A Review, Taxonomy, and Perspective

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area

Clinical and Prognostic Value of PET/CT Imaging with Combination of 68

Background. To evaluate the clinical and prognostic value of PET/CT with combination of 68Ga-DOTATATE and 18F-FDG in gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). Method. 83 patients of GEP-NENs who underwent 68Ga-DOTATATE and 18F-FDG PET/CT were enrolled between June 2013 and December 2016. Well-differentiated (WD) NETs are divided into group A (Ki-67 < 10%) and group B (Ki-67 ≥ 10%), and poorly differentiated (PD) NECs are defined as group C. The relationship between PET/CT results and clinicopathological characteristics was retrospectively investigated. Result. For groups A/B/C, the sensitivities of 68Ga-DOTATATE and 18F-FDG were 78.8%/83.3%/37.5% and 52.0%/72.2%/100.0%. A negative correlation between Ki-67 and SUVmax of 68Ga-DOTATATE (R = −0.415; P ≤ 0.001) was observed, while a positive correlation was noted between Ki-67 and SUVmax of 18F-FDG (R = 0.683; P ≤ 0.001). 62.5% (5/8) of patients showed significantly more lesions in the bone if 68Ga-DOTATATE was used, and 22.7% (5/22) of patients showed more lymph node metastases if 18F-FDG was used. Conclusions. The sensitivity of dual tracers was correlated with cell differentiation, and a correlation between Ki-67 and both SUVmax of PET-CTs could be observed. 68Ga-DOTATATE is suggested for WD-NET and 18F-FDG is probably suitable for patients with Ki-67 ≥ 10%

Dietary menthol-induced TRPM8 activation enhances WAT “browning” and ameliorates diet-induced obesity

Beige adipocytes are a new type of recruitable brownish adipocytes, with highly mitochondrial membrane uncoupling protein 1 expression and thermogenesis. Beige adipocytes were found among white adipocytes, especially in subcutaneous white adipose tissue (sWAT). Therefore, beige adipocytes may be involved in the regulation of energy metabolism and fat deposition. Transient receptor potential melastatin 8 (TRPM8), a Ca2+-permeable non-selective cation channel, plays vital roles in the regulation of various cellular functions. It has been reported that TRPM8 activation enhanced the thermogenic function of brown adiposytes. However, the involvement of TRPM8 in the thermogenic function of WAT remains unexplored. Our data revealed that TRPM8 was expressed in mouse white adipocytes at mRNA, protein and functional levels. The mRNA expression of Trpm8 was significantly increased in the differentiated white adipocytes than pre-adipocytes. Moreover, activation of TRPM8 by menthol enhanced the expression of thermogenic genes in cultured white aidpocytes. And menthol-induced increases of the thermogenic genes in white adipocytes was inhibited by either KT5720 (a protein kinase A inhibitor) or BAPTA-AM. In addition, high fat diet (HFD)-induced obesity in mice was significantly recovered by co-treatment with menthol. Dietary menthol enhanced WAT &quot;browning&quot; and improved glucose metabolism in HFD-induced obesity mice as well. Therefore, we concluded that TRPM8 might be involved in WAT &quot;browning&quot; by increasing the expression levels of genes related to thermogenesis and energy metabolism. And dietary menthol could be a novel approach for combating human obesity and related metabolic diseases

Cerebral Small Vessel Disease Burden Is Associated with Motor Performance of Lower and Upper Extremities in Community-Dwelling Populations

Objective: To investigate the correlation between cerebral small vessel disease (CSVD) burden and motor performance of lower and upper extremities in community-dwelling populations.Methods: We performed a cross-sectional analysis on 770 participants enrolled in the Shunyi study, which is a population-based cohort study. CSVD burden, including white matter hyperintensities (WMH), lacunes, cerebral microbleeds (CMBs), perivascular spaces (PVS), and brain atrophy were measured using 3T magnetic resonance imaging. All participants underwent quantitative motor assessment of lower and upper extremities, which included 3-m walking speed, 5-repeat chair-stand time, 10-repeat pronation–supination time, and 10-repeat finger-tapping time. Data on demographic characteristics, vascular risk factors, and cognitive functions were collected. General linear model analysis was performed to identify potential correlations between motor performance measures and imaging markers of CSVD after controlling for confounding factors.Results: For motor performance of the lower extremities, WMH was negatively associated with gait speed (standardized β = -0.092, p = 0.022) and positively associated with chair-stand time (standardized β = 0.153, p &lt; 0.0001, surviving FDR correction). For motor performance of the upper extremities, pronation–supination time was positively associated with WMH (standardized β = 0.155, p &lt; 0.0001, surviving FDR correction) and negatively with brain parenchymal fraction (BPF; standardized β = -0.125, p = 0.011, surviving FDR correction). Only BPF was found to be negatively associated with finger-tapping time (standardized β = -0.123, p = 0.012). However, lacunes, CMBs, or PVS were not found to be associated with motor performance of lower or upper extremities in multivariable analysis.Conclusion: Our findings suggest that cerebral microstructural changes related to CSVD may affect motor performance of both lower and upper extremities. WMH and brain atrophy are most strongly associated with motor function deterioration in community-dwelling populations

The LAMOST Survey of Background Quasars in the Vicinity of the Andromeda and Triangulum Galaxies -- II. Results from the Commissioning Observations and the Pilot Surveys

We present new quasars discovered in the vicinity of the Andromeda and Triangulum galaxies with the LAMOST during the 2010 and 2011 observational seasons. Quasar candidates are selected based on the available SDSS, KPNO 4 m telescope, XSTPS optical, and WISE near infrared photometric data. We present 509 new quasars discovered in a stripe of ~135 sq. deg from M31 to M33 along the Giant Stellar Stream in the 2011 pilot survey datasets, and also 17 new quasars discovered in an area of ~100 sq. deg that covers the central region and the southeastern halo of M31 in the 2010 commissioning datasets. These 526 new quasars have i magnitudes ranging from 15.5 to 20.0, redshifts from 0.1 to 3.2. They represent a significant increase of the number of identified quasars in the vicinity of M31 and M33. There are now 26, 62 and 139 known quasars in this region of the sky with i magnitudes brighter than 17.0, 17.5 and 18.0 respectively, of which 5, 20 and 75 are newly-discovered. These bright quasars provide an invaluable collection with which to probe the kinematics and chemistry of the ISM/IGM in the Local Group of galaxies. A total of 93 quasars are now known with locations within 2.5 deg of M31, of which 73 are newly discovered. Tens of quasars are now known to be located behind the Giant Stellar Stream, and hundreds behind the extended halo and its associated substructures of M31. The much enlarged sample of known quasars in the vicinity of M31 and M33 can potentially be utilized to construct a perfect astrometric reference frame to measure the minute PMs of M31 and M33, along with the PMs of substructures associated with the Local Group of galaxies. Those PMs are some of the most fundamental properties of the Local Group.Comment: 26 pages, 6 figures, AJ accepte

Long-term seasonal forecasting of a major migrant insect pest: the brown planthopper in the Lower Yangtze River Valley

Rice planthoppers and associated virus diseases have become the most important pests threatening food security in China and other Asian countries, incurring costs of hundreds of millions of US dollars annually in rice losses, and in expensive, environmentally harmful, and often futile control efforts. The most economically damaging species, the brown planthopper, Nilaparvata lugens (Hemiptera: Delphacidae), cannot overwinter in temperate East Asia, and infestations there are initiated by several waves of windborne spring or summer migrants originating from tropical areas in Indochina. The interaction of these waves of migrants and synoptic weather patterns, driven by the semi-permanent western Pacific subtropical high-pressure (WPSH) system, is of critical importance in forecasting the timing and intensity of immigration events and determining the seriousness of subsequent planthopper build-up in the rice crop. We analysed a 26-year data set from a standardised light trap network in Southern China, showing that planthopper aerial transport and concentration processes are associated with the characteristics (strength and position) of the WPSH in the year concerned. Then, using N. lugens abundance in source areas and indices of WPSH intensity or related sea surface temperature anomalies, we developed a model to predict plan-thopper numbers immigrating into the key rice-growing area of the Lower Yangtze Valley. We also demonstrate that these WPSH-related climatic indices combined with early-season planthopper catches can be used to forecast, several months in advance, the severity of that season’s N. lugens infestations (the correlation between model predictions and outcomes was 0.59), thus allowing time for effective control measures to be implemented

Corrigendum to: The TianQin project: current progress on science and technology

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article