High energy processes in clusters of galaxies and the origin of cosmic rays

We test the hypothesis of a universal cosmic ray intensity by calculating the secondary electron or positron production in the hadronic interactions of cosmic ray nuclei with intergalactic gas within clusters of galaxies. We find that the spectral characteristics of the radio synchrotron emission by these secondary electrons is not consistent with observations of the Coma cluster. Thus the hypothesis can be ruled out on cluster scales.published_or_final_versio

A Hybrid Time-Scaling Transformation for Time-Delay Optimal Control Problems

In this paper, we consider a class of nonlinear time-delay optimal control problems with canonical equality and inequality constraints. We propose a new computational approach, which combines the control parameterization technique with a hybrid time-scaling strategy, for solving this class of optimal control problems. The proposed approach involves approximating the control variables by piecewise constant functions, whose heights and switching times are decision variables to be optimized. Then, the resulting problem with varying switching times is transformed, via a new hybrid time-scaling strategy, into an equivalent problem with fixed switching times, which is much preferred for numerical computation. Our new time-scaling strategy is hybrid in the sense that it is related to two coupled time-delay systems—one defined on the original time scale, in which the switching times are variable, the other defined on the new time scale, in which the switching times are fixed. This is different from the conventional time-scaling transformation widely used in the literature, which is not applicable to systems with time-delays. To demonstrate the effectiveness of the proposed approach, we solve four numerical examples. The results show that the costs obtained by our new approach are lower, when compared with those obtained by existing optimal control methods

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

Lili Yuan,1,* Lin An,1,* Yandong Zhu,1 Chongling Duan,1 Weixiang Kong,1 Pei Jiang,2 Qing-Qing Yu1 1Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China; 2Translational Pharmaceutical Laboratory, Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qing-Qing Yu, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, 272000, People’s Republic of China, Email [email protected] Pei Jiang, Translational Pharmaceutical Laboratory, Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, 272000, People’s Republic of China, Email [email protected]: As a disease with high morbidity and high mortality, lung cancer has seriously harmed people’s health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.Keywords: machine learning, computed tomography, lung cancer, artificial intelligence, diagnosi

On-demand semiconductor single-photon source with near-unity indistinguishability

Single photon sources based on semiconductor quantum dots offer distinct advantages for quantum information, including a scalable solid-state platform, ultrabrightness, and interconnectivity with matter qubits. A key prerequisite for their use in optical quantum computing and solid-state networks is a high level of efficiency and indistinguishability. Pulsed resonance fluorescence (RF) has been anticipated as the optimum condition for the deterministic generation of high-quality photons with vanishing effects of dephasing. Here, we generate pulsed RF single photons on demand from a single, microcavity-embedded quantum dot under s-shell excitation with 3-ps laser pulses. The pi-pulse excited RF photons have less than 0.3% background contributions and a vanishing two-photon emission probability. Non-postselective Hong-Ou-Mandel interference between two successively emitted photons is observed with a visibility of 0.97(2), comparable to trapped atoms and ions. Two single photons are further used to implement a high-fidelity quantum controlled-NOT gate.Comment: 11 pages, 11 figure

Back-incident SiGe-Si multiple quantum-well resonant-cavity-enhanced photodetectors for 13-mu m operation

A back-incident Si-0.65 Ge-0.35/Si multiple quantum-well resonant-cavity-enhanced photodetector operating near 1.3 mum is demonstrated on a separation-by-implantation-oxygen substrate. The resonant cavity is composed of an electron-beam evaporated SiO2-Si distributed Bragg reflector as a top mirror and the interface between the buried SiO2 and the Si substrate as a bottom mirror. We have obtained the responsivity as high as 31 mA/WI at 1.305 mum and the full width at half maximum of 14 nm

RNAi-mediated silencing of CD147 inhibits tumor cell proliferation, invasion and increases chemosensitivity to cisplatin in SGC7901 cells in vitro

<p>Abstract</p> <p>Background</p> <p>CD147 is a widely distributed cell surface glycoprotein that belongs to the Ig superfamily. CD147 has been implicated in numerous physiological and pathological activities. Enriched on the surface of many tumor cells, CD147 promotes tumor growth, invasion, metastasis and angiogenesis and confers resistance to some chemotherapeutic drugs. In this study, we investigated the possible role of CD147 in the progression of gastric cancer.</p> <p>Methods</p> <p>Short hairpin RNA (shRNA) expressing vectors targeting CD147 were constructed and transfected into human gastric cancer cells SGC7901 and CD147 expression was monitored by quantitative realtime RT-PCR and Western blot. Cell proliferation, the activities of MMP-2 and MMP-9, the invasive potential and chemosensitivity to cisplatin of SGC7901 cells were determined by MTT, gelatin zymography, Transwell invasion assay and MTT, respectively.</p> <p>Results</p> <p>Down-regulation of CD147 by RNAi approach led to decreased cell proliferation, MMP-2 and MMP-9 activities and invasive potential of SGC7901 cells as well as increased chemosensitivity to cisplatin.</p> <p>Conclusion</p> <p>CD147 involves in proliferation, invasion and chemosensitivity of human gastric cancer cell line SGC7901, indicating that CD147 may be a promising therapeutic target for gastric cancer.</p

Superconductivity in HfTe5 across weak to strong topological insulator transition induced via pressures

Recently, theoretical studies show that layered HfTe5 is at the boundary of weak & strong topological insulator (TI) and might crossover to a Dirac semimetal state by changing lattice parameters. The topological properties of 3D stacked HfTe5 are expected hence to be sensitive to pressures tuning. Here, we report pressure induced phase evolution in both electronic & crystal structures for HfTe5 with a culmination of pressure induced superconductivity. Our experiments indicated that the temperature for anomaly resistance peak (Tp) due to Lifshitz transition decreases first before climbs up to a maximum with pressure while the Tp minimum corresponds to the transition from a weak TI to strong TI. The HfTe5 crystal becomes superconductive above ~5.5 GPa where the Tp reaches maximum. The highest superconducting transition temperature (Tc) around 5 K was achieved at 20 GPa. Crystal structure studies indicate that HfTe5 transforms from a Cmcm phase across a monoclinic C2/m phase then to a P-1 phase with increasing pressure. Based on transport, structure studies a comprehensive phase diagram of HfTe5 is constructed as function of pressure. The work provides valuable experimental insights into the evolution on how to proceed from a weak TI precursor across a strong TI to superconductors

Bioactive gelatin-siloxane hybrids as tissue engineering scaffold

Ca2+-containing porous gelatin-siloxane hybrids were prepared using sol-gel process, post-gelation soaking, and freeze-drying. The porosity and pore size of the hybrids could be well controlled by the freezing temperature and the pH value of the soaking solution. The pore characteristics were related to the structure change during the soaking treatment. A bone-like apatite layer was able to form in the Ca2+-containing porous gelatin-siloxane hybrids upon soaking in a stimulated body fluid. The porous gelatin-siloxane hybrids could release gentamicin sulfate which is an antibiotic drug in bone chemotherapy. Thus, those hybrid materials are proposed to find application as novel bioactive and biodegradable scaffolds in bone tissue engineering