Influence of tensor interactions on masses and decay widths of dibaryons

The influence of gluon and Goldstone boson induced tensor interactions on the dibaryon masses and D-wave decay widths has been studied in the quark delocalization, color screening model. The effective S-D wave transition interactions induced by gluon and Goldstone boson exchanges decrease rapidly with increasing strangeness of the channel. The tensor contribution of K and $\eta$ mesons is negligible in this model. There is no six-quark state in the light flavor world studied so far that can become bound by means of these tensor interactions besides the deuteron. The partial D-wave decay widths of the $IJ^p={1/2}2^+$ N$\Omega$ state to spin 0 and 1 $\Lambda\Xi$ final states are 12.0 keV and 21.9 keV respectively. This is a very narrow dibaryon resonance that might be detectable in relativistic heavy ion reactions by existing RHIC detectors through the reconstruction of the vertex mass of the decay product $\Lambda\Xi$ and by the COMPAS detector at CERN or at JHF in Japan and the FAIR project in Germany in the future.Comment: 19 pages, 5 figure

Open‐field arena boundary is a primary object of exploration for Drosophila

Drosophila adults, when placed into a novel open‐field arena, initially exhibit an elevated level of activity followed by a reduced stable level of spontaneous activity and spend a majority of time near the arena edge, executing motions along the walls. In order to determine the environmental features that are responsible for the initial high activity and wall‐following behavior exhibited during exploration, we examined wild‐type and visually impaired mutants in arenas with different vertical surfaces. These experiments support the conclusion that the wall‐following behavior of Drosophila is best characterized by a preference for the arena boundary, and not thigmotaxis or centrophobicity. In circular arenas, Drosophila mostly move in trajectories with low turn angles. Since the boundary preference could derive from highly linear trajectories, we further developed a simulation program to model the effects of turn angle on the boundary preference. In an hourglass‐shaped arena with convex‐angled walls that forced a straight versus wall‐following choice, the simulation with constrained turn angles predicted general movement across a central gap, whereas Drosophila tend to follow the wall. Hence, low turn angled movement does not drive the boundary preference. Lastly, visually impaired Drosophila demonstrate a defect in attenuation of the elevated initial activity. Interestingly, the visually impaired w 1118 activity decay defect can be rescued by increasing the contrast of the arena's edge, suggesting that the activity decay relies on visual detection of the boundary. The arena boundary is, therefore, a primary object of exploration for Drosophila . In an open field arena, Drosophila spend the majority of time at the arena boundary even when additional vertical surfaces are present in the interior. The visually impaired white files have defects in the attenuation of exploratory activity. by increasing the contrast of the boundary, we can rescue this defect in white mutants, demonstrating that the boundary is a primary object of exploration in an open field arena.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90592/1/brb3.36.pd

Fucoidan from Fucus vesiculosus inhibits inflammatory response, both in vitro and in vivo

Fucoidan has been reported to present diverse bioactivities, but each extract has specific features from which a particular biological activity, such as immunomodulation, must be confirmed. In this study a commercially available pharmaceutical-grade fucoidan extracted from Fucus vesiculosus, FE, was characterized and its anti-inflammatory potential was investigated. Fucose was the main monosaccharide (90 mol%) present in the studied FE, followed by uronic acids, galactose, and xylose that were present at similar values (3.8–2.4 mol%). FE showed a molecular weight of 70 kDa and a sulfate content of around 10%. The expression of cytokines by mouse bone-marrow-derived macrophages (BMDMs) revealed that the addition of FE upregulated the expression of CD206 and IL-10 by about 28 and 22 fold, respectively, in respect to control. This was corroborated in a stimulated pro-inflammatory situation, with the higher expression (60 fold) of iNOS being almost completely reversed by the addition of FE. FE was also capable of reverse LPS-caused inflammation in an in vivo mouse model, including by reducing macrophage activation by LPS from 41% of positive CD11C to 9% upon fucoidan injection. Taken together, the potential of FE as an anti-inflammatory agent was validated, both in vitro and in vivo.This research received funding from project 0474_BLUEBIOLAB_1_E, financed by the European Regional Development Fund through INTERREG España-Portugal 2014–2020, and project ATLANTIDA (ref. NORTE-01-0145-FEDER-000040) supported by the Northern Portugal Regional Programme Norte 2020, under the Portugal 2020 Partnership Agreement. This work was also developed within the scope of the project CICECO-Aveiro Institute of Materials (UIDB/50011/2020, UIDP/50011/2020 and LA/P/0006/2020) and LAQV-REQUIMTE (UIDB/50006/2020), financed by national funds of the Portuguese Foundation for Science and Technology (FCT)/MCTES. A. S. F. thanks FCT for the individual grant (SFRH/BD/102471/2014). This work was also funded by national funds (OE), through FCT, I.P., within the scope of the framework contract seen in numbers 4, 5 and 6 of article 23 of the Decree Law 57/2016, August 29, changed by Law 57/2017, July 19. The authors are also thankful to the financial support from the Natural Science Foundation of China (grant No. 32000936)

Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma

Recent statistics on lung cancer, including the steady decline of advanced diseases and the dramatically increasing detection of early-stage diseases and indeterminate pulmonary nodules, mark the significance of a comprehensive understanding of early lung carcinogenesis. Lung adenocarcinoma (ADC) is the most common histologic subtype of lung cancer, and atypical adenomatous hyperplasia is the only recognized preneoplasia to ADC, which may progress to adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) and eventually to invasive ADC. Although molecular evolution during early lung carcinogenesis has been explored in recent years, the progress has been significantly hindered, largely due to insufficient materials from ADC precursors. Here, we employed state-of-the-art deep learning and artificial intelligence techniques to robustly segment and recognize cells on routinely used hematoxylin and eosin histopathology images and extracted 9 biology-relevant pathomic features to decode lung preneoplasia evolution. We analyzed 3 distinct cohorts (Japan, China, and United States) covering 98 patients, 162 slides, and 669 regions of interest, including 143 normal, 129 atypical adenomatous hyperplasia, 94 AIS, 98 MIA, and 205 ADC. Extracted pathomic features revealed progressive increase of atypical epithelial cells and progressive decrease of lymphocytic cells from normal to AAH, AIS, MIA, and ADC, consistent with the results from tissue-consuming and expensive molecular/immune profiling. Furthermore, pathomics analysis manifested progressively increasing cellular intratumor heterogeneity along with the evolution from normal lung to invasive ADC. These findings demonstrated the feasibility and substantial potential of pathomics in studying lung cancer carcinogenesis directly from the low-cost routine hematoxylin and eosin staining

Efficacy and Clinicogenomic Correlates of Response to Immune Checkpoint Inhibitors Alone or With Chemotherapy in Non-Small Cell Lung Cancer

The role of combination chemotherapy with immune checkpoint inhibitors (ICI) (ICI-chemo) over ICI monotherapy (ICI-mono) in non-small cell lung cancer (NSCLC) remains underexplored. In this retrospective study of 1133 NSCLC patients, treatment with ICI-mono vs ICI-chemo associate with higher rates of early progression, but similar long-term progression-free and overall survival. Sequential vs concurrent ICI and chemotherapy have similar long-term survival, suggesting no synergism from combination therapy. Integrative modeling identified PD-L1, disease burden (Stage IVb; liver metastases), and STK11 and JAK2 alterations as features associate with a higher likelihood of early progression on ICI-mono. CDKN2A alterations associate with worse long-term outcomes in ICI-chemo patients. These results are validated in independent external (n = 89) and internal (n = 393) cohorts. This real-world study suggests that ICI-chemo may protect against early progression but does not influence overall survival, and nominates features that identify those patients at risk for early progression who may maximally benefit from ICI-chemo

Synthetic PET From CT Improves Diagnosis and Prognosis for Lung Cancer: Proof of Concept

[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT

Predicting Benefit From Immune Checkpoint Inhibitors in Patients With Non-Small-Cell Lung Cancer by CT-Based Ensemble Deep Learning: A Retrospective Study

BACKGROUND: Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context. METHODS: In this retrospective modelling study, 976 patients with metastatic, EGFR/ALK negative NSCLC treated with immune checkpoint inhibitors at MD Anderson and Stanford were enrolled from Jan 1, 2014, to Feb 29, 2020. We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival after treatment with immune checkpoint inhibitors. We also evaluated the added predictive value of the Deep-CT model in the context of existing clinicopathological and radiological metrics. FINDINGS: Our Deep-CT model demonstrated robust stratification of patient survival of the MD Anderson testing set, which was validated in the external Stanford set. The performance of the Deep-CT model remained significant on subgroup analyses stratified by PD-L1, histology, age, sex, and race. In univariate analysis, Deep-CT outperformed the conventional risk factors, including histology, smoking status, and PD-L1 expression, and remained an independent predictor after multivariate adjustment. Integrating the Deep-CT model with conventional risk factors demonstrated significantly improved prediction performance, with overall survival C-index increases from 0·70 (clinical model) to 0·75 (composite model) during testing. On the other hand, the deep learning risk scores correlated with some radiomics features, but radiomics alone could not reach the performance level of deep learning, indicating that the deep learning model effectively captured additional imaging patterns beyond known radiomics features. INTERPRETATION: This proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer

SN 2017gmr: An Energetic Type II-P Supernova with Asymmetries

We present high-cadence UV, optical, and near-infrared data on the luminous Type II-P supernova SN2017gmr from hours after discovery through the first 180 days. SN2017gmr does not show signs of narrow, high-ionization emission lines in the early optical spectra, yet the optical light-curve evolution suggests that an extra energy source from circumstellar medium (CSM) interaction must be present for at least 2 days after explosion. Modeling of the early light curve indicates a ∼500 Re progenitor radius, consistent with a rather compact red supergiant, and latetime luminosities indicate that up to 0.130±0.026 Me of 56Ni are present, if the light curve is solely powered by radioactive decay, although the 56Ni mass may be lower if CSM interaction contributes to the post-plateau luminosity. Prominent multipeaked emission lines of Hα and [O I] emerge after day 154, as a result of either an asymmetric explosion or asymmetries in the CSM. The lack of narrow lines within the first 2 days of explosion in the likely presence of CSM interaction may be an example of close, dense, asymmetric CSM that is quickly enveloped by the spherical supernova ejecta

Stellar variability from Dome A, Antarctica

The Antarctic plateau is one of the best observing sites on the surface of the Earth thanks to its extremely cold, dry, stable and transparent atmosphere conditions. Various astronomical activities are underway there and the Chinese Center for Antarctic Astronomy (CCAA) is dedicated to developing Antarctic astronomy at the highest point, Dome A or the Chinese Kunlun station. So far a large number of images have been collected from a 14.5-cm quad-telescope called the Chinese Small Telescope ARray (CSTAR) and the first two of a trio of 50-cm Antarctic Survey Telescopes (AST3-1 and AST3-2)