Exploration of potential novel drug targets and biomarkers for small cell lung cancer by plasma proteome screening

Background: Small cell lung cancer (SCLC) is characterized by extreme invasiveness and lethality. There have been very few developments in its diagnosis and treatment over the past decades. It is urgently needed to explore potential novel biomarkers and drug targets for SCLC.Methods: Two-sample Mendelian Randomization (MR) was performed to investigate causal associations between SCLC and plasma proteins using genome-wide association studies (GWAS) summary statistics of SCLC from Transdisciplinary Research Into Cancer of the Lung Consortium (nCase = 2,791 vs. nControl = 20,580), and was validated in another cohort (nCase = 2,664 vs. nControl = 21,444). 734 plasma proteins and their genetic instruments of cis-acting protein quantitative trait loci (pQTL) were used, whereas external plasma proteome data was retrieved from deCODE database. Bidirectional MR, Steiger filtering and phenotype scanning were applied to further verify the associations.Results: Seven significant (p < 6.81 × 10−5) plasma protein-SCLC pairs were identified by MR analysis, including ACP5 (OR = 0.76, 95% CI: 0.67–0.86), CPB2 (OR = 0.90, 95% CI: 0.86–0.95), GSTM3 (OR = 0.45, 95% CI: 0.33–0.63), SHMT1 (OR = 0.74, 95% CI: 0.64–0.86), CTSB (OR = 0.79, 95% CI: 0.71–0.88), NTNG1 (OR = 0.81, 95% CI: 0.74–0.90) and FAM171B (OR = 1.40, 95% CI: 1.21–1.62). The external validation confirmed that CPB2, GSTM3 and NTNG1 had protective effects against SCLC, while FAM171B increased SCLC risk. However, the reverse causality analysis revealed that SCLC caused significant changes in plasma levels of most of these proteins, including decreases of ACP5, CPB2, GSTM3 and NTNG1, and the increase of FAM171B.Conclusion: This integrative analysis firstly suggested the causal associations between SCLC and plasma proteins, and the identified several proteins may be promising novel drug targets or biomarkers for SCLC

An Improved Bare Bone Multi-Objective Particle Swarm Optimization Algorithm for Solar Thermal Power Plants

Solar energy has many advantages, such as being abundant, clean and environmentally friendly. Solar power generation has been widely deployed worldwide as an important form of renewable energy. The solar thermal power generation is one of a few popular forms to utilize solar energy, yet its modelling is a complicated problem. In this paper, an improved bare bone multi-objective particle swarm optimization algorithm (IBBMOPSO) is proposed based on the bare bone multi-objective particle swarm optimization algorithm (BBMOPSO). The algorithm is first tested on a set of benchmark problems, confirming its efficacy and the convergency speed. Then, it is applied to optimize two typical solar power generation systems including the solar Stirling power generation and the solar Brayton power generation; the results show that the proposed algorithm outperforms other algorithms for multi-objective optimization problems

A Modified 2D Multiresolution Hybrid Algorithm for Ultrasound Strain Imaging

Ultrasound elastography is an imaging modality to evaluate elastic properties of soft tissue. Recently, 1D quasi-static elastography method has been commercialized by some companies. However, its performance is still limited on high strain level. In order to improve the precision of estimation during high compression, some algorithms have been proposed to expand the 1D window to a 2D window for avoiding the side-slipping. But they are usually more computationally expensive. In this paper, we proposed a modified 2D multiresolution hybrid method for displacement estimation, which can offer an efficient strain imaging with stable and accurate results. A FEM phantom with a stiffer circular inclusion is simulated for testing the algorithm. The elastographic contrast-to-noise rate (CNRe) is calculated for quantitatively comparing the performance of the proposed algorithm with conventional 1D elastography using phase zero estimation and the 1D elastography using downsampled (d-s) baseband signals. Results show that the proposed method is robust and performs similarly as other algorithms in low strain but is superior when high level strain is applied. Particularly, the CNRe of our algorithm is 15 times higher than original method under 4% strain level. Furthermore, the execution time of our algorithm is five times faster than other algorithms

Experimental and Theoretical Studies on the Creep Behavior of Bayer Red Mud

Long-term stability and safety of the Bayer red mud (BRM) disposal field is very important for the local residents’ life, which necessitates the knowledge of its creep behavior. In order to investigate the creep behavior of BRM, a series of triaxial drained creep tests were conducted by using an improved triaxial creep apparatus. The results indicate that the creep behavior of BRM is significant with confining and deviatoric stresses being critical factors. The creep strain is in a nonlinear relationship with stress and time, and a larger deviator stress will lead to a larger creep strain. The main failure mechanism of BRM is plastic shear, accompanied by a significant compression and ductile dilatancy. Based on the test results, two well-established creep models, the Burgers creep model and Singh–Mitchell creep model, were used to comparatively analyze the creep behavior of the Bayer red mud under a certain stress level. Then, an improved Burgers creep damage constitutive model with the addition of a damage variable was proposed, whose parameters were also analyzed in detail. The comparison of the calculated values of the creep model and the experimental values shows that the proposed creep damage model can better describe the instant elastic deformation, attenuation creep, steady-state creep, and accelerated creep stages of the Bayer red mud

Single-Cell Profiling Comparisons of Tumor Microenvironment between Primary Advanced Lung Adenocarcinomas and Brain Metastases and Machine Learning Algorithms in Predicting Immunotherapeutic Responses

Brain metastasis (BM) occurs commonly in patients with lung adenocarcinomas. Limited evidence indicates safety and efficacy of immunotherapy for this metastatic tumor, though immune checkpoint blockade has become the front-line treatment for primary advanced non-small cell lung cancer. We aim to comprehensively compare tumor microenvironments (TME) between primary tumors (PT) and BM at single-cell resolution. Single-cell RNA transcriptomics from tumor samples of PT (N = 23) and BM (N = 16) and bulk sequencing data were analyzed to explore potential differences in immunotherapeutic efficacy between PT and BM of lung adenocarcinomas. Multiple machine learning algorithms were used to develop and validate models that predict responses to immunotherapy using the external cohorts. We found obviously less infiltration of immune cells in BM than PT, characterized specifically by deletion of anti-cancer CD8+ Trm cells and more dysfunctional CD8+ Tem cells in BM tumors. Meanwhile, macrophages and dendritic cells within BM demonstrated more pro-tumoral and anti-inflammatory effects, represented by distinct distribution and function of SPP1+ and C1Qs+ tumor-associated microphages, and inhibited antigen presentation capacity and HLA-I gene expression, respectively. Besides, we also found the lack of inflammatory-like CAFs and enrichment of pericytes within BM tumors, which may be critical factors in shaping inhibitory TME. Cell communication analysis further revealed mechanisms of the immunosuppressive effects associated with the activation of some unfavorable pathways, such as TGFβ signaling, highlighting the important roles of stromal cells in the anti-inflammatory microenvironment, especially specific pericytes. Furthermore, pericyte-related genes were identified to optimally predict immunotherapeutic responses by machine learning models with great predictive performance. Overall, various factors contribute to the immunosuppressive TME within BM tumors, represented by the lack of critical anti-cancer immune cells. Meanwhile, pericytes may help shape the TME and targeting the associated mechanisms may enhance immunotherapy efficacy for BM tumors in patients with lung adenocarcinomas

DataSheet_1_Combination of bicarbonate and low temperature stress induces the biosynthesis of both arachidonic and docosahexaenoic acids in alkaliphilic microalgae Dunaliella salina HTBS.docx

High bicarbonate levels and low temperature may have an impact on microalgae cultivation. However, changes in cellular composition in response to the combination of the above stresses are still poorly understood. In this study, the combined effects of bicarbonate and low temperature on biochemical changes in alkaliphilic microalgae Dunaliella salina HTBS were investigated. Comparing to the control condition of 25°C without bicarbonate, the cell density was increased from 0.69 to 1.18 in the treatment condition of 0.15 M bicarbonate and low temperature (16 °C) while the lipid\protein\carbohydrate contents were increased from 34.71% to 43.94%, 22.44% to 26.03%, 22.62% to 29.18%, respectively. Meanwhile, the PUFAs, arachidonic acid (AA) and docosahexaenoic acid (DHA) contents reached to 3.52% and 4.73% with the combination of low temperature and bicarbonate, respectively, whereas they were not detected when the cells were treated with single condition. Moreover, both the chlorophyll and carotenoid contents were also detected with increased profiles in the combined treatments. As a result, the maximum photochemical efficiency but not reduced non-photochemical quenching was strengthened, which enhanced the photosynthetic performance. Additionally, our results indicated that D. salina HTBS could acclimate to the combined stress by up-regulating the activity of SOD\CAT and reducing MDA content. These findings demonstrated that the addition of a certain bicarbonate under low temperature could effectively enhance the biomass production and accumulation of AA and DHA, which would benefit the development of the microalgae industry in value-added products.</p

Band Gap and Defect Engineering Enhanced Scintillation from Ce³⁺-Doped Nanoglass Containing Mixed-Type Fluoride Nanocrystals

Much can be learned from the research and development of scintillator crystals for improving the scintillation performance of glasses. Relying on the concept of “embedding crystalline order in glass”, we have demonstrated that the scintillation properties of Ce3+-doped nanoglass composites (nano-GCs) can be optimized via the synergistic effects of Gd3+-sublattice sensitization and band-gap engineering. The nano-GCs host a large volume fraction of KYxGd1–xF4 mixed-type fluoride nanocrystals (NCs) and still retain reasonably good transparency at Ce3+-emitting wavelengths. The light yield of 3455 ± 20 ph/MeV is found, which is the largest value ever reported in fluoride NC-embedded nano-GCs. A comprehensive study is given on the highly selective doping of Ce3+ in the NCs and its positive effect on the scintillation properties. The favorable influence of the Y3+/Gd3+ mixing on the suppression of defects is accounted for by density functional theory and borne out experimentally. As a proof-of-concept, X-ray imaging with a good spatial resolution (7.9 lp/mm) is demonstrated by employing Ce3+-doped nano-GCs. The superior radiation hardness, repeatability, and thermal stability of the designed scintillators bode well for their long-term practical applications

CEPC Technical Design Report -- Accelerator

International audienceThe Circular Electron Positron Collider (CEPC) is a large scientific project initiated and hosted by China, fostered through extensive collaboration with international partners. The complex comprises four accelerators: a 30 GeV Linac, a 1.1 GeV Damping Ring, a Booster capable of achieving energies up to 180 GeV, and a Collider operating at varying energy modes (Z, W, H, and ttbar). The Linac and Damping Ring are situated on the surface, while the Booster and Collider are housed in a 100 km circumference underground tunnel, strategically accommodating future expansion with provisions for a Super Proton Proton Collider (SPPC). The CEPC primarily serves as a Higgs factory. In its baseline design with synchrotron radiation (SR) power of 30 MW per beam, it can achieve a luminosity of 5e34 /cm^2/s^1, resulting in an integrated luminosity of 13 /ab for two interaction points over a decade, producing 2.6 million Higgs bosons. Increasing the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons, facilitating precise measurements of Higgs coupling at sub-percent levels, exceeding the precision expected from the HL-LHC by an order of magnitude. This Technical Design Report (TDR) follows the Preliminary Conceptual Design Report (Pre-CDR, 2015) and the Conceptual Design Report (CDR, 2018), comprehensively detailing the machine's layout and performance, physical design and analysis, technical systems design, R&D and prototyping efforts, and associated civil engineering aspects. Additionally, it includes a cost estimate and a preliminary construction timeline, establishing a framework for forthcoming engineering design phase and site selection procedures. Construction is anticipated to begin around 2027-2028, pending government approval, with an estimated duration of 8 years. The commencement of experiments could potentially initiate in the mid-2030s