Study on the Effect of High-quality Nursing Combined with Breathing Exercises on Patients with Chronic Obstructive Pulmonary Disease

Objective: To study the effect of high-quality nursing combined with breathing exercises on patients with COPD. Methods: Using the random number table method of medical experiments, 60 patients with COPD received in our hospital from March 2020 to March 2021 were used as research samples. According to the differences in treatment measures, they were equally divided into control group and intervention group. Symptomatic support treatment and nursing routine, high-quality nursing combined with respiratory function exercise treatment and nursing were given respectively, and the application effects of the two groups were compared and analyzed. Results: The controllable rate of disease between the intervention group and the control group was 93.33% (28/30) and 66.67% (20/30) respectively, which was statistically significant (P<0.05). The comparison between the intervention group and the control group on the pulmonary function indexes of VT, TPTEF/Te, VEF/Te/, Ti/Te was statistically significant (P<0.05). The results of the intervention group on exercise pulse and 6-minute walking distance were significantly higher than those of the control group (P<0.05). Conclusions: The combination of high-quality nursing care and breathing exercises has outstanding disease controllable rate in patients with COPD, especially in improving the lung function of the patients and the level of treatment and care. It can be used as a feasible measure in the subsequent clinical treatment and nursing practice of patients. It is worthy of clinical promotion and implementation

A Bayesian Model of Cognitive Control

<p>"Cognitive control" describes endogenous guidance of behavior in situations where routine stimulus-response associations are suboptimal for achieving a desired goal. The computational and neural mechanisms underlying this capacity remain poorly understood. The present dissertation examines recent advances stemming from the application of a statistical, Bayesian learner perspective on control processes. An important limitation in current models consists of a lack of a plausible mechanism for the flexible adjustment of control over variable environments. I propose that flexible cognitive control can be achieved by a Bayesian model with a self-adapting, volatility-driven learning scheme, which modulates dynamically the relative dependence on recent (short-term) and remote (long-term) experiences in its prediction of future control demand. Using simulation data, human behavioral data and human brain imaging data, I demonstrate that this Bayesian model does not only account for several classic behavioral phenomena observed from the cognitive control literature, but also facilitates a principled, model-guided investigation of the neural substrates underlying the flexible adjustment of cognitive control. Based on the results, I conclude that the proposed Bayesian model provides a feasible solution for modeling the flexible adjustment of cognitive control.</p>Dissertatio

Introgression of bacterial blight (BB) resistance genes Xa7 and Xa21 into popular restorer line and their hybrids by molecular marker-assisted backcross (MABC) selection scheme

Yihui1577 is an elite restorer line widely used in hybrid rice production in China, however, both the restorer and their derived hybrids are susceptible to bacterial blight (BB) caused by Xathomonas oryzae pv. oryzae (Xoo). In order to overcome this problem, we had introgressed two resistant genes Xa7 and Xa21 into Yihui1577 by marker-assisted backcross (MABC) with foreground selection scheme to speed up the process. Six breeding lines with different BB resistance genes: HH1202 (Xa7), HH1203 (Xa7), HH1204 (Xa21), HH1205 (Xa21), HH1206 (Xa7+Xa21) and HH1207 (Xa7+Xa21) were selected and crossed with four CMS and one TGMS lines. Seven most virulent and prevalent Xoo strains (PXO61, PXO99, ZHE173, GD1358, FuJ, YN24 and HeN11) from the Philippines and different provinces of China were inoculated for evaluating the BB-resistance of the selected lines and their derived hybrids. The results reveal that the two lines and their derived hybrids with single resistance gene Xa7 were resistant against six of the seven Xoo strains, except for PXO99. The lines with single resistance gene Xa21 were only susceptible to the Xoo strain FuJ, but some of their derived hybrids were susceptible to the Xoo strains FuJ and GD1358. Interestingly, the pyramiding lines carrying the two resistance genes Xa7 and Xa21 and also their derived hybrids were resistant against all the seven Xoo strains. The data of agronomic and grain quality characteristics demonstrated that the selected lines were similar to that of the recurrent parent Yihui1577. Corrective measures taken by way of introgression of BB-resistance genes: Xa7 and Xa21 into the popular restorer line, Yihui1577 through MABC approach for enhancing the BB-resistance level was effective and timely.Keywords: Bacterial blight, resistance gene, Xa7 and Xa21, MABC, inoculation and reaction, agronomic traits, grain qualit

Nonparametric Mean Shift Functional Detection in the Functional Space for Task and Resting-state fMRI

International audienceIn functional Magnetic Resonance Imaging (fMRI) data analysis, normalization of time series is an important and sometimes necessary preprocessing step in many widely used methods. The space of normalized time series with n time points is the unit sphere S^{n-2}, named the functional space. Riemannian framework on the sphere, including the geodesic, the exponential map, and the logarithmic map, has been well studied in Riemannian geometry. In this paper, by introducing the Riemannian framework in the functional space, we propose a novel nonparametric robust method, namely Mean Shift Functional Detection (MSFD), to explore the functional space. The first merit of the MSFD is that it does not need many assumptions on data which are assumed in many existing method, e.g. linear addition (GLM, PCA, ICA), uncorrelation (PCA), independence (ICA), the number and the shape of clusters (FCM). Second, MSFD takes into account the spatial information and can be seen as a multivariate extension of the functional connectivity analysis method. It is robust and works well for activation detection in task study even with a biased activation reference. It is also able to find the functional networks in resting-state study without a user-selected "seed" region. Third, it can enhance the boundary between different functional networks. Experiments were conducted on synthetic and real data to compare the performance of the proposed method with GLM and ICA. The experimental results validated the accuracy and robustness of MSFD, not only for activation detection in task study but also for functional network exploration in resting-state study

Nonparametric Mean Shift Functional Detection in the Functional Space for Task and Resting-state fMRI

International audienceIn functional Magnetic Resonance Imaging (fMRI) data analysis, normalization of time series is an important and sometimes necessary preprocessing step in many widely used methods. The space of normalized time series with n time points is the unit sphere S^{n-2}, named the functional space. Riemannian framework on the sphere, including the geodesic, the exponential map, and the logarithmic map, has been well studied in Riemannian geometry. In this paper, by introducing the Riemannian framework in the functional space, we propose a novel nonparametric robust method, namely Mean Shift Functional Detection (MSFD), to explore the functional space. The first merit of the MSFD is that it does not need many assumptions on data which are assumed in many existing method, e.g. linear addition (GLM, PCA, ICA), uncorrelation (PCA), independence (ICA), the number and the shape of clusters (FCM). Second, MSFD takes into account the spatial information and can be seen as a multivariate extension of the functional connectivity analysis method. It is robust and works well for activation detection in task study even with a biased activation reference. It is also able to find the functional networks in resting-state study without a user-selected "seed" region. Third, it can enhance the boundary between different functional networks. Experiments were conducted on synthetic and real data to compare the performance of the proposed method with GLM and ICA. The experimental results validated the accuracy and robustness of MSFD, not only for activation detection in task study but also for functional network exploration in resting-state study

RXR negatively regulates ex vivo expansion of human cord blood hematopoietic stem and progenitor cells

Ex vivo expansion of human cord blood (CB) hematopoietic stem cells (HSCs) is one approach to overcome limited numbers of HSCs in single CB units. However, there is still no worldwide acceptable HSC ex vivo expansion system. A main reason is that we still have very limited knowldege regarding mechanisms underlying maintenance and expansion of CB HSCs. Here we report that retinoid X receptor (RXR) activity is of significance for CB HSC ex vivo expansion. RXR antagonist HX531 significantly promoted ex vivo expansion of CB HSCs and progenitor cells (HPCs). RXR agonist Bexarotene notably suppressed ex vivo expansion of CB HSCs. Activation of RXR by Bexarotene significantly blocked expansion of phenotypic HSCs and HPCs and expressed increased functional HPCs as assessed by colony formation induced by UM171 and SR1. In vivo transplantation experiments in immune-deficient mice demonstrated that HX531 expanded CB HSCs possess long-term reconstituting capacities, and Bexarotene treatment inhibited expansion of functional CB HSCs. RNA-seq analysis revealed that RXR regulates expression of FBP1 (a negative regulator of glucose metabolism) and many genes involved in differentation. ECAR analysis showed that HX531 significantly promoted glycolytic activity of CB CD34+ HSCs and HPCs. Our studies suggest that RXR is a negative regulator of ex vivo expansion of CB HSCs and HPCs

A critical review of methods for analyzing freshwater eutrophication

Water eutrophication is a global environmental problem that poses serious threats to aquatic ecosystems and human health. The evaluation of eutrophication provides a theoretical basis and technical guidance for the management and rehabilitation of water ecosystems. In the last four decades, dozens of evaluation methods have been applied to freshwater eutrophication, but there is a clear need to optimize and standardize the most suitable methods. We have addressed this gap by presenting a systematic review of methodologies. Due to the diversity and complexity of water bodies, no single evaluation method was identified that would adequately represent eutrophication under all scenarios. We demonstrate that lakes can best be assessed using the trophic level index (TLI) method, reservoirs and wetlands the trophic state index (TSI) and fuzzy comprehensive evaluation (FCE) method, respectively, and rivers the FCE method or back propagation (BP) neural network methods. More recently applied methodologies including spectral imaging and 3-D mapping of water quality using underwater gliders allow greater resolution and can be effective in managing waterbodies to avoid future eutrophication. The aim of this review is to guide future studies on the most appropriate methods available for assessing and reporting water eutrophication

Hippocampal and cortical mechanisms at retrieval explain variability in episodic remembering in older adults

Age-related episodic memory decline is characterized by striking heterogeneity across individuals. Hippocampal pattern completion is a fundamental process supporting episodic memory. Yet, the degree to which this mechanism is impaired with age, and contributes to variability in episodic memory, remains unclear. We combine univariate and multivariate analyses of fMRI data from a large cohort of cognitively normal older adults (N=100) to measure hippocampal activity and cortical reinstatement during retrieval of trial-unique associations. Trial-wise analyses revealed that (a) hippocampal activity scaled with reinstatement strength, (b) cortical reinstatement partially mediated the relationship between hippocampal activity and associative retrieval, (c) older age weakened cortical reinstatement and its relationship to memory behaviour. Moreover, individual differences in the strength of hippocampal activity and cortical reinstatement explained unique variance in performance across multiple assays of episodic memory. These results indicate that fMRI indices of hippocampal pattern completion explain within-and across-individual memory variability in older adults