Two-step sinter-crystallization of K2O–CaO–P2O5–SiO2 (45S5-K) bioactive glass

Bioactive glasses and glass-ceramics (GCs) effectively regenerate bone tissue, however most GCs show improved mechanical properties. In this work, we developed and tested a rarely studied bioactive glass composition (24.4K₂O-26.9CaO-46.1SiO₂-2.6P₂O₅ mol%, identified as 45S5-K) with different particle sizes and heating rates to obtain a sintered GC that combines good fracture strength, low elastic modulus, and bioactivity. We analyzed the influence of the sintering processing conditions in the elastic modulus, Vickers microhardness, density, and crystal phase formation in the GC. The best GC shows improved properties compared with its parent glass. This glass achieves a good densification degree with a two-step viscous flow sintering approach and the resulting GC shows as high bioactivity as that of the standard 45S5 Bioglass®. Furthermore, the GC elastic modulus (56 GPa) is relatively low, minimizing stress shielding. Therefore, we unveiled the glass sintering behavior with concurrent crystallization of this complex bioactive glass composition and developed a potential GC for bone regeneration.     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

Academic faculty practices: issues for viability in competitive managed care markets

This study compares the perspectives of eighteen managed care executives and twenty-four faculty practice executives on critical policy issues related to the managed care marketplace. Market sites studied in 1994 included four major metropolitan areas: Minneapolis-St. Paul, Los Angeles, Philadelphia, and Atlanta. These markets were selected as being representative of communities with descending degrees of managed care involvement, but with significant market activity. Study participants from both managed care systems and faculty practices examined five policy issues: (1) the importance of including academic medical centers in current and future health care plans for marketing purposes; (2) the provision of clinical services that are unique to the academic medical center, that is, unavailable elsewhere in the community; (3) the degree of financial supplement that employers might pay for including an academic medical center; (4) future restructuring of organizations to sustain the educational mission of academic faculty within a viable delivery system; (5) satisfaction of managed care providers with graduates of academic medical centers, as measured by the clinical skills of graduate physicians. The study findings showed little support among managed care plans for paying supplements to include faculty practices in a health care network. Most study participants from managed care systems and academic faculty practices identified limited competencies that are unique to academic centers. Moreover, managed care organizations were only willing to undertake limited restructuring at best to include faculty practices within their networks. General concern about the preparation of resident physicians (especially those in primary care disciplines) for practice within contemporary managed care organizations existed among managed care informants. The results of the study indicate that as traditional funding sources for medical education are reduced, schools require greater integration with managed care plans to enable academic medical centers and their faculties to continue promoting clinical enterprise.     

The CD40-CD154 system in anti-infective host defense

Research in the past few years has documented significant advances in our understanding of the CD40-CD40 ligand (CD154) system in diverse immune functions. This system influences many T cell mediated inflammatory immune responses and effector functions, unmasking a previously unexpected role for CD40-CD154 in cell mediated immunity. Manipulation of CD154 in animal models of infection by the use of CD154-deficient mice or anti-CD154 antibodies has shown the importance of this system in the initiation of the inflammatory response, in the activation of antigen-presenting cells and in resistance to infections.     

Tuning of MST neurons to spiral motions

Cells in the dorsal division of the medial superior temporal area (MSTd) have large receptive fields and respond to expansion/contraction, rotation, and translation motions. These same motions are generated as we move through the environment, leading investigators to suggest that area MSTd analyzes the optical flow. One influential idea suggests that navigation is achieved by decomposing the optical flow into the separate and discrete channels mentioned above, that is, expansion/contraction, rotation, and translation. We directly tested whether MSTd neurons perform such a decomposition by examining whether there are cells that are preferentially tuned to intermediate spiral motions, which combine both expansion/contraction and rotation components. The finding that many cells in MSTd are preferentially selective for spiral motions indicates that this simple three-channel decomposition hypothesis for MSTd does not appear to be correct. Instead, there is a continuum of patterns to which MSTd cells are selective. In addition, we find that MSTd cells maintain their selectivity when stimuli are moved to different locations in their large receptive fields. This position invariance indicates that MSTd cells selective for expansion cannot give precise information about the retinal location of the focus of expansion. Thus, individual MSTd neurons cannot code, in a precise fashion, the direction of heading by using the location of the focus of expansion. The only way this navigational information could be accurately derived from MSTd is through the use of a coarse, population encoding. Positional invariance and selectivity for a wide array of stimuli suggest that MSTd neurons encode patterns of motion per se, regardless of whether these motions are generated by moving objects or by motion induced by observer locomotion.