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.     

The pharmacokinetics and protein-binding of fusidic acid in patients with severe renal failure requiring either haemodialysis or continuous ambulatory peritoneal dialysis

Antibiotic treatment options for Burkholderia cepacia infection are limited because of high intrinsic resistance. The problem is complicated by development of cross-resistance between antibiotics of different classes. We isolated antibiotic-resistant mutants by stepwise exposure to chloramphenicol (Chlor) and to trimethoprim/sulphamethoxazole (T/S) for four B. cepacia strains: ATCC13945, Per (clinical isolate), Cas and D4 (environmental isolates). Chlor(r) mutants did not produce chloramphenicol acetyl-transferase. Cross-resistance, defined as greater than four-fold increase in MIC by microtitre dilution method, was consistently seen in both types of mutants. For chloramphenicol-resistant (Chlor[r]) and trimethoprim/sulphamethoxazole-resistant (Tr/Sr) mutants of B. cepacia ATCC13945 and Cas, no MIC change was seen for piperacillin, ceftazidime, rifampicin, gentamicin, tobramycin, polymyxin B or azithromycin. B. cepacia-Per and -D4 mutants showed cross-resistance to ceftazidime and to piperacillin. Comparison of outer membrane protein (OMP) profiles of B. cepacia and their mutants by SDS-PAGE revealed Tr/Sr) mutants to be deficient in a major OMP (molecular weight 39-47 kDa). Tr/Sr mutants also expressed additional OMPs not found in wild type strains at 75-77 kDa for B. cepacia-ATCC13945 and -Cas, and 20-21 kDa in B. cepacia-D4 and -Per. No OMP changes occurred in Chlor(r) mutants. Lipopolysaccharide (LPS) profiles of each type of mutant showed new high and low molecular weight LPS bands. Cross-resistance seems to be mediated by alterations in porin and LPS for Tr/Sr mutants, but only by LPS in Chlor(r) mutants.     

Context-specific adaptation of vertical vergence to correlates of eye position

Vertical phoria (vertical vergence in the absence of binocular feedback) can be trained to vary with non-visual cues such as vertical conjugate eye position, horizontal conjugate eye position and horizontal vergence. These prior studies demonstrated a low-level association or coupling between vertical vergence and several oculomotor cues. As a test of the potential independence of multiple eye-position cues for vertical vergence, context-specific adaptation experiments were conducted in three orthogonal adapting planes (midsagittal, frontoparallel, and transverse). Four vertical disparities in each of these planes were associated with various combinations of two specific components of eye position. Vertical disparities in the plane were associated with horizontal vergence and vertical conjugate eye position; vertical disparities in the frontoparallel plane were associated with horizontal and vertical conjugate eye position; and vertical disparities in the transverse plane were associated with horizontal vergence and horizontal conjugate eye position. The results demonstrate that vertical vergence can be adapted to respond to specific combinations of two different sources of eye-position information. The results are modeled with an association matrix whose inputs are two classes of eye position and whose weighted output is vertical vergence.