The maximum size of a partial spread in H(5, q²) is q³+1

AbstractIn this paper, we show that the largest maximal partial spreads of the hermitian variety H(5,q2) consist of q3+1 generators. Previously, it was only known that q4 is an upper bound for the size of these partial spreads. We also show for q⩾7 that every maximal partial spread of H(5,q2) contains at least 2q+3 planes. Previously, only the lower bound q+1 was known

Characterization results on weighted minihypers and on linear codes meeting the G(r)iesmer bound

A

Partial ovoids and partial spreads in symplectic and orthogonal polar spaces

We present improved lower bounds on the sizes of small maximal partial ovoids and small maximal partial spreads in the classical symplectic and orthogonal polar spaces, and improved upper bounds on the sizes of large maximal partial ovoids and large maximal partial spreads in the classical symplectic and orthogonal polar spaces. An overview of the status regarding these results is given in tables. The similar results for the hermitian classical polar spaces are presented in [J. De Beule, A. Klein, K. Metsch, L. Storme, Partial ovoids and partial spreads in hermitian polar spaces, Des. Codes Cryptogr. (in press)]

Partial Ovoids and Partial Spreads of Classical Finite Polar Spaces

2000 Mathematics Subject Classification: 05B25, 51E20.We survey the main results on ovoids and spreads, large maximal partial ovoids and large maximal partial spreads, and on small maximal partial ovoids and small maximal partial spreads in classical finite polar spaces. We also discuss the main results on the spectrum problem on maximal partial ovoids and maximal partial spreads in classical finite polar spaces.The research of the fourth author was also supported by the Project Combined algorithmic and the oretical study of combinatorial structur es between the Fund for Scientific Research Flanders-Belgium (FWO-Flanders) and the Bulgarian Academy of Science

Mdct imaging before transcutanous aortic valve implantation: rationale and measurements

Since its introduction in 2002, transcatheter aortic valve implantation (TAVI) has assumed growing importance in the treatment of patients with severe aortic stenosis (AS), because it offers a much less invasive alternative for those in high risk for surgery. Good early results and advances in percutaneous valve technology have led to a substantial increase in procedural success rate and number of patients undergoing this less invasive treatment. Pre-procedural screening of several anatomic factors to assess the feasibility of this technique is important. Multidetector row computed tomography (MDCT) is the technique of choice in assessing these factors. This technical note aims to describe and illustrate the key elements that need to be evaluated before the procedure

Using machine learning to characterize heart failure across the scales

Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in time and space. Multiscale models of cardiac growth can provide a patient-specific window into the progression of heart failure and guide personalized treatment planning. Yet, the predictive potential of cardiac growth models remains poorly understood. Here, we quantify predictive power of a stretch-driven growth model using a chronic porcine heart failure model, subject-specific multiscale simulation, and machine learning techniques. We combine hierarchical modeling, Bayesian inference, and Gaussian process regression to quantify the uncertainty of our experimental measurements during an 8-week long study of volume overload in six pigs. We then propagate the experimental uncertainties from the organ scale through our computational growth model and quantify the agreement between experimentally measured and computationally predicted alterations on the cellular scale. Our study suggests that stretch is the major stimulus for myocyte lengthening and demonstrates that a stretch-driven growth model alone can explain 52.7% of the observed changes in myocyte morphology. We anticipate that our approach will allow us to design, calibrate, and validate a new generation of multiscale cardiac growth models to explore the interplay of various subcellular-, cellular-, and organ-level contributors to heart failure. Using machine learning in heart failure research has the potential to combine information from different sources, subjects, and scales to provide a more holistic picture of the failing heart and point toward new treatment strategies

Tasquinimod suppresses tumor cell growth and bone resorption by targeting immunosuppressive myeloid cells and inhibiting c-MYC expression in multiple myeloma

Background: Immunotherapy emerged as a promising treatment option for multiple myeloma (MM) patients. However, therapeutic efficacy can be hampered by the presence of an immunosuppressive bone marrow microenvironment including myeloid cells. S100A9 was previously identified as a key regulator of myeloid cell accumulation and suppressive activity. Tasquinimod, a small molecule inhibitor of S100A9, is currently in a phase Ib/IIa clinical trial in MM patients (NCT04405167). We aimed to gain more insights into its mechanisms of action both on the myeloma cells and the immune microenvironment. Methods: We analyzed the effects of tasquinimod on MM cell viability, cell proliferation and downstream signaling pathways in vitro using RNA sequencing, real-time PCR, western blot analysis and multiparameter flow cytometry. Myeloid cells and T cells were cocultured at different ratios to assess tasquinimod-mediated immunomodulatory effects. The in vivo impact on immune cells (myeloid cell subsets, macrophages, dendritic cells), tumor load, survival and bone disease were elucidated using immunocompetent 5TMM models. Results: Tasquinimod treatment significantly decreased myeloma cell proliferation and colony formation in vitro, associated with an inhibition of c-MYC and increased p27 expression. Tasquinimod-mediated targeting of the myeloid cell population resulted in increased T cell proliferation and functionality in vitro. Notably, short-term tasquinimod therapy of 5TMM mice significantly increased the total CD11b+ cells and shifted this population toward a more immunostimulatory state, which resulted in less myeloid-mediated immunosuppression and increased T cell activation ex vivo. Tasquinimod significantly reduced the tumor load and increased the trabecular bone volume, which resulted in prolonged overall survival of MM-bearing mice in vivo. Conclusion: Our study provides novel insights in the dual therapeutic effects of the immunomodulator tasquinimod and fosters its evaluation in combination therapy trials for MM patients