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Multicomponent composite materials with the compositions Zr66Nb13Cu8Ni6.8Al6.2 and Ti66Nb13Cu8Ni6.8Al6.2 are produced by copper mold casting, and their microstructure and their room-temperature mechanical properties are investigated. The volume fraction of the dendritic bcc phase is higher for the Ti-based alloy and the formed interdendritic matrix phase(s) have a different structure.

Abstract: Cu-mould cast samples of the alloy Ti60Cu14Ni12Sn4Ta10 have a composite microstructure of micron scale β-Ti dendrites in a nanoscale binary eutectic matrix. Characterisation of the individual phases in the nanoscale eutectic was facilitated by nanobeam electron diffraction, energy dispersive X-ray analysis and Rietveld refinement of X-ray diffraction patterns. The results clearly showed that the eutectic is composed of a β-Ti solid solution, rich in Ta and Sn, and the intermetallic NiTi with ∼25at.% Cu substituted for Ni. Analysis of the solidification was achieved by comparison with an analogous ternary Ti–Cu–Ni alloy. In Cu-mould cast Ti60Cu14Ni12Sn4Ta10, after formation of the dendritic phase, the equilibrium phase CuTi2 was kinetically excluded due to sluggish diffusion conditions and the high cooling rate. A metastable equilibrium between β-Ti and NiTi was therefore created, resulting in the observed eutectic. Heat treatment of the Cu-mould cast samples resulted in recovery of the equilibrium CuTi2 phase. [Copyright &y& Elsevier]

A Zr74.9Ti10.5Ni14.6 nanostructure-dendrite composite was prepared by copper mold casting. X-ray diffraction, and scanning and transmission electron microscopies, reveal a microstructure comprising ultrafine-scale dendrites with hexagonal structure and a two-phase interdendritic matrix, consisting of nanocrystalline hexagonal and tetragonal phases. Room temperature compression tests show evident yielding and 6% plastic strain together with work hardening up to 1622 MPa. These results indicate that composite materials based on hexagonal phases might be an alternative to composites containing body-centered cubic phase reinforcements. [ABSTRACT FROM AUTHOR]

Alloys with bulk compositions ranging between Al71Co29 and Al77Co23 are prepared by arc melting the elements followed by annealing at temperatures between 1070 and 1150 °C (330 h) and rapid quenching. [ABSTRACT FROM AUTHOR]

Astrocytes play an important role in the regulation of the inflammatory response in the CNS, e.g., in demyelinating diseases. Since the chemokine CXCL1 is known to be secreted by astrocytes and to have a pro-inflammatory effect on immune cells in the CNS, we verified the effect of testosterone on its secretion in vitro (in the astrocytic cell line DI TNC1). Testosterone reduced the increase in CXCL1 production caused by the pro-inflammatory agent lysophosphatidylcholine and restored the basal production level of CXCL1. The androgen receptor (present and functional in the studied cell line) was strongly suggested to mediate this effect-its non-steroid ligand flutamide exerted an agonist-like effect, mimicking the activity of testosterone itself on CXCL1 secretion. This novel mechanism has important implications for the known immunomodulatory effect of testosterone and potentially other androgenic hormones. It provides a potential explanation on the molecular level and shows that astrocytes are important players in inflammatory homeostasis in the CNS and its hormonal regulation. Therefore, it suggests new directions for the development of the therapeutic intervention.

For a long time, astrocytes were considered a passive brain cell population. However, recently, many studies have shown that their role in the central nervous system (CNS) is more active. Previously, it was stated that there are two main functional phenotypes of astrocytes. However, nowadays, it is clear that there is rather a broad spectrum of these phenotypes. The major goal of this study was to evaluate the production of some inflammatory chemokines and neurotrophic factors by primary human astrocytes after pro- or anti-inflammatory stimulation. We observed that only astrocytes induced by inflammatory mediators TNFα/IL-1a/C1q produced CXCL10, CCL1, and CXCL13 chemokines. Unstimulated astrocytes and those cultured with anti-inflammatory cytokines (IL-4, IL-10, or TGF-β1) did not produce these chemokines. Interestingly, astrocytes cultured in proinflammatory conditions significantly decreased the release of neurotrophic factor PDGF-A, as compared to unstimulated astrocytes. However, in response to anti-inflammatory cytokine TGF-β1, astrocytes significantly increased PDGF-A production compared to the medium alone. The production of another studied neurotrophic factor BDNF was not influenced by pro- or anti-inflammatory stimulation. The secretory response was accompanied by changes in HLA-DR, CD83, and GFAP expression. Our study confirms that astrocytes differentially respond to pro- and anti-inflammatory stimuli, especially to inflammatory cytokines TNF-α, IL-1a, and C1q, suggesting their role in leukocyte recruitment.

This article focuses on a statistical analysis of the corona virus disease 2019 (COVID-19) data that appeared until November 31, 2020 in Poland. The studied database, expressed in terms of both population and air pollution (particulate) indicators, is provided mainly by the Airly company, the Central Statistical Office (GUS) and the Rogalski project. The particular measured factors, which underwent standardization, were assessed for mutual dependency by means of a Pearson correlation coefficient and analysed by a linear regression. Based on the presented models, our results indicate that air quality (air pollution level) is the most important factor in the context of enabling COVID-19 case load increase in Poland., Competing Interests: None., (© 2021 The Authors.)

During the 2020 global pandemic crisis, some health care teams pulled together while others fell apart. The teams who pulled together put aside their differences and became stronger, putting their patients first and then each other. Those teams grew stronger, but some teams completely fell apart. They spent their days nitpicking, complaining, and arguing-making decisions based on what was best for themselves, not patients or their coworkers. The common denominator in determining how well teams performed was the leader. Employees looked to their leaders to successfully lead them through crisis, whether it was on a small or global scale. Depending on leader behaviors, the leader strengthened or weakened the team; trust was built or broken. That is a heavy burden to carry knowing that employees were so dependent on them and how they showed up every day. What lessons can leaders learn from the coronavirus-2019 (COVID-19) pandemic that can help them strengthen and sustain a healthy, professional, and supportive workforce culture during a crisis and beyond?, Competing Interests: The authors declare no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Decreased range of motion is a common secondary complication of motor neuron disease (MND) that can contribute to functional decline and decreased participation in daily activities., Aim: The purpose of this study was to develop and assess the effectiveness of educational brochures and videos aimed at improving knowledge regarding the importance of a regular stretching program., Design: This was a quality improvement (QI) project., Setting: Participants were seen in an outpatient multidisciplinary neuromuscular clinic., Population: Individuals with motor neuron disease were invited to participate in this QI study., Methods: Individuals were asked to complete surveys asking questions regarding current stretching program, pain levels, and knowledge of benefits of stretching before and after receiving the stretching brochures or videos., Results: A total of 53 participants completed the pre-intervention survey, 28 in the brochure group and 25 in the video group. Of those, 86% and 88% completed the post-intervention survey in the brochure and video groups, respectively. The video group increased stretching frequency significantly more than the brochure group (2.04 and 0.62 days/week respectively, P=0.004). Significantly more participants in the video group reported usage of stretches from the educational materials on a regular basis (54% for brochure group and 86% for video group, P=0.024)., Conclusions: Educational brochures and videos are two different strategies to improve knowledge of benefits of stretching for individuals with MND. Both groups increased frequency of stretching. Videos may be better able to improve frequency of stretching when compared to brochures., Clinical Rehabilitation Impact: The brochures and videos developed for this study can be used by clinicians treating individuals with MND. By improving knowledge regarding the benefits of stretching, individuals with MND may choose to prioritize stretching as a part of their routine. This in turn may help to prevent or address potential joint or muscle length issues or assist patients to incorporate preventative measures into their treatment plans.

Background : Toward the goal of predicting individual long-term cancer survival to guide treatment decisions, this study evaluated the ability of a probabilistic neural network (PNN), an established model used for decision-making in research and clinical settings, to predict the 10-year overall survival in patients with cervical cancer who underwent primary surgical treatment. Patients and Method : The input dataset was derived from 102 patients with cervical cancer FIGO stage IA2-IIB treated by radical hysterectomy. We identified 4 demographic parameters, 13 tumor-related parameters, and 6 selected perioperative variables for each patient and performed computer simulations with DTREG software. The predictive ability of the model was determined on the basis of its error, sensitivity, and specificity, as well as area under the receiver operating characteristic curve. The results of the PNN predictive model were compared with those of logistic regression analysis and a single decision tree as reference models. Results : The PNN model had very high predictive ability, with a sensitivity of 0.949, a specificity of 0.679, and an error rate of 12.5%. The PNN's area under the receiver operating characteristic curve was high, 0.809, a value greater than those for both logistic regression analysis and the single decision tree. Conclusion : The PNN model effectively and reliably predicted 10-year overall survival in women with operable cervical cancer, and may therefore serve as a tool for decision-making process in cancer treatment., Competing Interests: Competing Interests: The authors have declared that no competing interest exists.

The efficiency of the probabilistic neural network (PNN) is very sensitive to the cardinality of a considered input data set. It results from the design of the network's pattern layer. In this layer, the neurons perform an activation on all input records. This makes the PNN architecture complex, especially for big data classification tasks. In this paper, a new algorithm for the structure reduction of the PNN is put forward. The solution relies on performing a fuzzy c-means data clustering and selecting PNN's pattern neurons on the basis of the obtained centroids. Then, to activate the pattern neurons, the algorithm chooses input vectors for which the highest values of the membership coefficients are determined. The proposed approach is applied to the classification tasks of repository data sets. PNN is trained by three different classification procedures: conjugate gradients, reinforcement learning and the plugin method. Two types of kernel estimators are used to activate the neurons of the network. A 10-fold cross validation errors for the original and the reduced PNNs are compared. Received results confirm the validity of the introduced algorithm., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

In this paper, we propose the use of local sensitivity analysis (LSA) for the structure simplification of the probabilistic neural network (PNN). Three algorithms are introduced. The first algorithm applies LSA to the PNN input layer reduction by selecting significant features of input patterns. The second algorithm utilizes LSA to remove redundant pattern neurons of the network. The third algorithm combines the proposed two and constitutes the solution of how they can work together. PNN with a product kernel estimator is used, where each multiplicand computes a one-dimensional Cauchy function. Therefore, the smoothing parameter is separately calculated for each dimension by means of the plug-in method. The classification qualities of the reduced and full structure PNN are compared. Furthermore, we evaluate the performance of PNN, for which global sensitivity analysis (GSA) and the common reduction methods are applied, both in the input layer and the pattern layer. The models are tested on the classification problems of eight repository data sets. A 10-fold cross validation procedure is used to determine the prediction ability of the networks. Based on the obtained results, it is shown that the LSA can be used as an alternative PNN reduction approach.

Background: Computational intelligence methods, including non-linear classification algorithms, can be used in medical research and practice as a decision making tool. This study aimed to evaluate the usefulness of artificial intelligence models for 5-year overall survival prediction in patients with cervical cancer treated by radical hysterectomy., Methods: The data set was collected from 102 patients with cervical cancer FIGO stage IA2-IIB, that underwent primary surgical treatment. Twenty-three demographic, tumor-related parameters and selected perioperative data of each patient were collected. The simulations involved six computational intelligence methods: the probabilistic neural network (PNN), multilayer perceptron network, gene expression programming classifier, support vector machines algorithm, radial basis function neural network and k-Means algorithm. The prediction ability of the models was determined based on the accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve. The results of the computational intelligence methods were compared with the results of linear regression analysis as a reference model., Results: The best results were obtained by the PNN model. This neural network provided very high prediction ability with an accuracy of 0.892 and sensitivity of 0.975. The area under the receiver operating characteristics curve of PNN was also high, 0.818. The outcomes obtained by other classifiers were markedly worse., Conclusions: The PNN model is an effective tool for predicting 5-year overall survival in cervical cancer patients treated with radical hysterectomy.

In this paper, we propose new methods for the choice and adaptation of the smoothing parameter of the probabilistic neural network (PNN). These methods are based on three reinforcement learning algorithms: Q(0)-learning, Q(λ)-learning, and stateless Q-learning. We regard three types of PNN classifiers: the model that uses single smoothing parameter for the whole network, the model that utilizes single smoothing parameter for each data attribute, and the model that possesses the matrix of smoothing parameters different for each data variable and data class. Reinforcement learning is applied as the method of finding such a value of the smoothing parameter, which ensures the maximization of the prediction ability. PNN models with smoothing parameters computed according to the proposed algorithms are tested on eight databases by calculating the test error with the use of the cross validation procedure. The results are compared with state-of-the-art methods for PNN training published in the literature up to date and, additionally, with PNN whose sigma is determined by means of the conjugate gradient approach. The results demonstrate that the proposed approaches can be used as alternative PNN training procedures.

Success with the Lean management system is rooted in a culture of stakeholder engagement and commitment. Unfortunately, many leaders view Lean as an "add-on" tool instead of one that requires a new way of thinking and approaching culture. This article addresses the "why, how, and what" to promote a Lean culture that works. We present a five-phased approach grounded in evidence-based practices of real-time culture change. We further help healthcare leaders understand the differences between traditional "sequenced" approaches to culture change and "real-time" methods--and why these real-time practices are more sustainable and ultimately more successful than traditional culture change methods.

Presented as a representative case of how to handle the disruptive behaviors of professionals in healthcare, this article describes the strategies of a systems approach with a five-phase model for culture change. The "large-scale, real-time" culture change process, based on our own evidence-based research on toxic behaviors and the research of others, has been demonstrated to be more effective than one-on-one feedback to change these behaviors. The real-time approach has been applied to other organizational situations--strategy formulation, change management, or service improvement--with more sustainable effects than simply training alone. This article will help your organization with four outcomes: understanding the rationale for a five-phase model for cultural change, describing the advantages of a real-time versus nonreal-time approach to change, identifying the how-to's for application within a systems approach, and articulating a clear evaluation process to sustain successful organizational culture change.