A neuromorphic network for generic multivariate data classification

Computational neuroscience has uncovered a number of computational principles used by nervous systems. At the same time, neuromorphic hardware has matured to a state where fast silicon implementations of complex neural networks have become feasible. En route to future technical applications of neuromorphic computing the current challenge lies in the identification and implementation of functional brain algorithms. Taking inspiration from the olfactory system of insects, we constructed a spiking neural network for the classification of multivariate data, a common problem in signal and data analysis. In this model, real-valued multivariate data are converted into spike trains using “virtual receptors” (VRs). Their output is processed by lateral inhibition and drives a winner-take-all circuit that supports supervised learning. VRs are conveniently implemented in software, whereas the lateral inhibition and classification stages run on accelerated neuromorphic hardware. When trained and tested on real-world datasets, we find that the classification performance is on par with a naïve Bayes classifier. An analysis of the network dynamics shows that stable decisions in output neuron populations are reached within less than 100 ms of biological time, matching the time-to-decision reported for the insect nervous system. Through leveraging a population code, the network tolerates the variability of neuronal transfer functions and trial-to-trial variation that is inevitably present on the hardware system. Our work provides a proof of principle for the successful implementation of a functional spiking neural network on a configurable neuromorphic hardware system that can readily be applied to real-world computing problems.The remarkable sensory and behavioral capabilities of all higher organisms are provided by the network of neurons in their nervous systems. The computing principles of the brain have inspired many powerful algorithms for data processing, most importantly the perceptron and, building on top of that, multilayer artificial neural networks, which are being applied with great success to various data analysis problems (1). Although these networks operate with continuous values, computation in biological neuronal networks relies on the exchange of action potentials, or “spikes.”Simulating networks of spiking neurons with software tools is computationally intensive, imposing limits to the duration of simulations and maximum network size. To overcome this limitation, several groups around the world have started to develop hardware realizations of spiking neuron models and neuronal networks (2–10) for studying the behavior of biological networks (11). The approach of the Spikey hardware system used in the present study is to enable high-throughput network simulations by speeding up computation by a factor of 10⁴ compared with biological real time (12, 13). It has been developed as a reconfigurable multineuron computing substrate supporting a wide range of network topologies (14).In addition to providing faster tools for neurosimulation, high-throughput spiking network computation in hardware offers the possibility of using spiking networks to solve real-world computational problems. The massive parallelism is a potential advantage over conventional computing when processing large amounts of data in parallel. However, conventional algorithms are often difficult to implement using spiking networks for which many neuromorphic hardware substrates are designed. Novel algorithms have to be designed that embrace the inherent parallelism of a brain-like computing architecture.A common problem in data analysis is classification of multivariate data. Many problems in artificial intelligence relate to classification in some way or the other, such as object recognition or decision making. It is the basis for data mining and, as such, has widespread applications in industry. We interact with classification systems in many aspects of daily life, for example in the form of Web shop recommendations, driver assistance systems, or when sending a letter with a handwritten address that is deciphered automatically in the post office.In this work, we present a neuromorphic network for supervised classification of multivariate data. We implemented the spiking network part on a neuromorphic hardware system. Using a range of datasets, we demonstrate how the classifier network supports nonlinear separation through encoding by virtual receptors, whereas lateral inhibition transforms the input data into a sparser encoding that is better suited for learning.     

From the Cover: Algorithms,games, and evolution

Even the most seasoned students of evolution, starting with Darwin himself, have occasionally expressed amazement that the mechanism of natural selection has produced the whole of Life as we see it around us. There is a computational way to articulate the same amazement: “What algorithm could possibly achieve all this in a mere three and a half billion years?” In this paper we propose an answer: We demonstrate that in the regime of weak selection, the standard equations of population genetics describing natural selection in the presence of sex become identical to those of a repeated game between genes played according to multiplicative weight updates (MWUA), an algorithm known in computer science to be surprisingly powerful and versatile. MWUA maximizes a tradeoff between cumulative performance and entropy, which suggests a new view on the maintenance of diversity in evolution.Precisely how does selection change the composition of the gene pool from generation to generation? The field of population genetics has developed a comprehensive mathematical framework for answering this and related questions (1). Our analysis in this paper focuses particularly on the regime of weak selection, now a widely used assumption (2, 3). Weak selection assumes that the differences in fitness between genotypes are small relative to the recombination rate, and consequently, through a result due to Nagylaki et al. (4) (see also ref. 1, section II.6.2), evolution proceeds near linkage equilibrium, a regime where the probability of occurrence of a certain genotype involving various alleles is simply the product of the probabilities of each of its alleles. Based on this result, we show that evolution in the regime of weak selection can be formulated as a repeated game, where the recombining loci are the players, the alleles in those loci are the possible actions or strategies available to each player, and the expected payoff at each generation is the expected fitness of an organism across the genotypes that are present in the population. Moreover, and perhaps most importantly, we show that the equations of population genetic dynamics are mathematically equivalent to positing that each locus selects a probability distribution on alleles according to a particular rule which, in the context of the theory of algorithms, game theory, and machine learning, is known as the multiplicative weight updates algorithm (MWUA). MWUA is known in computer science as a simple but surprisingly powerful algorithm (see ref. 5 for a survey). Moreover, there is a dual view of this algorithm: each locus may be seen as selecting its new allele distribution at each generation so as to maximize a certain convex combination of (i) cumulative expected fitness and (ii) the entropy of its distribution on alleles. This connection between evolution, game theory, and algorithms seems to us rife with productive insights; for example, the dual view just mentioned sheds new light on the maintenance of diversity in evolution.Game theory has been applied to evolutionary theory before, to study the evolution of strategic individual behavior (see, e.g., refs. 6, 7). The connection between game theory and evolution that we point out here is at a different level, and arises not in the analysis of strategic individual behavior, but rather in the analysis of the basic population genetic dynamics in the presence of sexual reproduction. The main ingredients of evolutionary game theory, namely strategic individual behavior and conflict between individuals, are extraneous to our analysis.We now state our assumptions and results. We consider an infinite panmictic population of haplotypes involving several unlinked (i.e., fully recombining) loci, where each locus has several alleles. These assumptions are rather standard in the literature. They are made here to simplify exposition and algebra, and there is no a priori reason to believe that they are essential for the results, beyond making them easily accessible. For example, Nagylaki’s theorem (4), which is the main analytical ingredient of our results, holds even in the presence of diploidy and partial recombination.Nagylaki’s theorem states that weak selection in the presence of sex proceeds near the Wright manifold, where the population genetic dynamics becomes (SI Text)xit+1(j)=1Xtxit(j)(Fit(j)),where xit(j) is the frequency of allele j of locus i in the population at generation t, X is a normalizing constant to keep the frequencies summing to 1, and Fit(j) is the mean fitness at time t among genotypes that contain allele j at locus i (see ref. 4 and SI Text). Under the assumption of weak selection, the fitnesses of all genotypes are close to one another, say within the interval [1 − ε, 1 + ε], and so the fitness of genotype g can be written as F_g = 1 + εΔ_g, where ε is the selection strength, assumed here to be small, and Δ_g ∈ [−1, 1] can be called the differential fitness of the genotype. With this in mind, the equation above can be writtenxit+1(j)=1Xtxit(j)(1+ϵΔit(j)),[1]where Δit(j) is the expected differential fitness among genotypes that contain allele j at locus i (see Fig. 1 for an illustration of population genetics at linkage equilibrium).Open in a separate windowFig. 1.Equations of population genetics formulated in the 1930s constitute the standard mathematical way of understanding evolution of a species by tracking the frequencies of various genotypes in a large population. In the simple example shown here, a haploid organism with two genetic loci A and B has three alleles in each of its two loci named A1, A2, A3 and B1, B2, B3 for a total of nine genotypes. In A we show the fitness of each genotype, that is, its expected number of offspring. The fitness numbers shown in A are all close to each other, reflecting weak selection, where the individual alleles’ contributions to fitness are typically minuscule. Initially, each genotype occurs in the population with some frequency; in this particular example these numbers are initially equal (B); naturally, their sum over all nine genotypes is 1 (frequencies are truncated to the fourth decimal digit). C shows how the genotype frequencies evolve in the next generation: each individual of a given genotype produces a number of offspring that is proportional to its fitness shown in A, and the resulting offspring inherits the alleles of its parents with equal probability (because we are assuming, crucially, sexual reproduction). The genotype frequencies in the next generation are shown in C, calculated through the standard recurrence equations of population genetics. We also show in the margins of the table the allele frequencies, obtained by adding the genotype frequencies along the corresponding row or column. Ten generations later, the frequencies are as shown in D.We now introduce the framework of game theory (see Fig. 2 for an illustration) and the MWUA (SI Text), studied in computer science and machine learning, and rediscovered many times over the past half-century; as a result of these multiple rediscoveries, the algorithm is known with various names across subfields: “the experts algorithm” in the theory of algorithms, “Hannan consistency” in economics, “regret minimization” in game theory, “boosting” and “winnow” in artificial intelligence, etc. Here we state it in connection to games, which is only a small part of its applicability (see SI Text for an introduction to the MWUA in connection to the so-called “experts problem” in computer science).Open in a separate windowFig. 2.A simple coordination game is played by two players: the row player, who chooses a row, and the column player, who chooses a column. After the two players make a choice, they both receive (or both pay, in case of a negative entry) the same amount of money, equal to the number at the chosen row and column (A). Coordination games are the simplest possible kind of a game, one in which the strategic interests of all players are completely aligned—that is to say, there is no conflict at all. They are of interest when it is difficult for the players to know these numbers, or to communicate and agree on a mutually beneficial combination (in this example, third row and second column). Notice that this particular coordination game is closely related to the fitness landscape shown in Fig. 1A: If P is a payoff in this game, the corresponding entry of Fig. 1A is equal to 1 + ε ⋅ P, where ε is a small parameter here taken to be 0.01. Suppose that each of the two players chooses each of the three options with some probability, initially 1/3 for all (B); in game theory such probabilistic play is called a mixed action. How do we expect these probabilities to change over repeated play? One famous recipe is the MWUA, in which a player “boosts” the probability of each option by multiplying it by 1 + εG, where G is the expected amount of money this option is going to win the player in the current round of play, and ε is the same small parameter as above. For example, the second action of the row player has G equal to 2 (the average of 3, −1, and 4), and so the probability of playing the second row will be multiplied by 1.02. Then these weights are “renormalized” so they add up to 1, yielding the marginal probabilities shown in C. The probabilities after 10 such rounds of play are shown in D. Comparing now the numbers in the margins of Figs. 1D and ​and2D,2D, we notice that they are essentially the same. This is what we establish mathematically in this paper: the two processes—repeated coordination games played through multiplicative updates, and evolution under weak selection—are essentially identical. This conclusion is of interest because the MWUA is known in computer science to be surprisingly powerful.A game has several players, and each player i has a set A_i of possible actions. Each player also has a utility, capturing the way whereby her actions and the actions of the other players affect this player’s well-being. Formally the utility of a player is a function that maps each combination of actions by the players to a real number (intuitively denoting the player’s gain, in some monetary unit, if all players choose these particular actions). In general, rather than choosing a single action, a player may instead choose a mixed or randomized action, that is, a probabilistic distribution over her action set. Here we only need to consider coordination games, in which all players have the same utility function—that is, the interests of the players are perfectly aligned, and their only challenge is to coordinate their choices effectively. Coordination games are among the simplest games; the only challenge in such a game is for the players to “agree” on a mutually beneficial action.How do the players choose and adjust their choice of randomized (mixed) actions over repeated play? Assume that at time t, player i has mixed action xit, assigning to each action j ∈ A_i the probability xit(j). The MWUA algorithm (5) adjusts the mixed strategy for player i in the next round of the game according to the following rule:xit+1(j)=1Ztxit(j)(1+ϵuit(j)),[2]where Z^t is a normalizing constant designed to ensure that ∑jxit(j)=1, so xit+1 is a probability distribution; ε is a crucial small positive parameter, and uit(j) denotes the expected utility gained by player i choosing action j in the regime of the mixed actions by the other players effective at time t. This algorithm (i) is known to converge to the min–max actions if the game is two-player zero-sum; (ii) is also shown here to converge to equilibrium for the coordination games of interest in the present paper (SI Text, Corollary 5); (iii) is a general “learning algorithm” that has been shown to be very successful in both theory and practice; and (iv) if, instead of games, it is applied to a large variety of optimization problems, including linear programming, convex programming, and network congestion, it provably converges to the optimum quite fast.It can be now checked that the two processes expressed in Eqs. 1 and 2, evolution under natural selection in the presence of sex and multiplicative weight updates in a coordination game, are mathematically identical (SI Text, Theorem 3). That is, the interaction of weak selection and sex is equivalent to the MWUA in a coordination game between loci in which the common utility is the differential fitness of the organism. The parameter ε in the algorithm, which, when small signifies that the algorithm is taking a “longer-term view” of the process to be solved (SI Text), corresponds to the selection strength in evolution, i.e., the magnitude of the differences between the fitness of various genotypes.The MWUA is known in computer science as an extremely simple and yet unexpectedly successful algorithm, which has surprised us time and again by its prowess in solving sophisticated computational problems such as congestion minimization in networks and convex programming in optimization. The observation that multiplicative weight updates in a coordination game are equivalent to evolution under sex and weak selection makes an informative triple connection between three theoretical fields: evolutionary theory, game theory, and the theory of algorithms–machine learning.So far we have presented the MWUA by “how it works” (informally, it boosts alleles proportionally to how well they do in the current mix). There is an alternative way of understanding the MWUA in terms of “what it is optimizing.” That is, we imagine that the allele frequencies of each locus in each generation are the result of a deliberate optimization by the locus of some quantity, and we wish to determine that quantity.Returning to the game formulation, define Uit(j)=∑τ=0tuiτ(j) to be the cumulative utility obtained by player i by playing strategy j over all t first repetitions of the game, and consider the quantity∑jxit(j)Uit(j)−1ϵ∑jxit(j)ln⁡xit(j).[3]The first term is the current (at time t) expected cumulative utility. The second term of 3 is the entropy (expected negative logarithm) of the probability distribution {x_i(j), j = 1, … |A_i|}, multiplied by a large constant 1/?. Suppose now that player i wished to choose the probabilities of actions xit(j)s with the sole goal of maximizing the quantity 3. This is a relatively easy optimization problem, because the quantity 3 to be maximized is strictly concave, and therefore it has a unique maximum, obtained through the Karush–Kuhn–Tucker conditions of optimality (8) (SI Text, section 4):Uit(j)−1ϵ(1+ln⁡xit(j))+μt=0.[Here μ^t is the Lagrange multiplier associated with the constraint ∑jxit(j)=1 seeking to keep the xit(j)s a probability distribution; see SI Text.] Subtracting this equation from its homolog with t replaced by t + 1, and applying the approximation exp(ϵuit(j))≈(1+ϵuit(j)), we obtain the precise Eq. 2 (the normalization Z^t is obtained from μ^t and μ^t+1; see SI Text for the more detailed derivation).Thus, because Eqs. 1 and 2 are identical, we conclude that, in the weak selection regime, natural selection is tantamount to each locus choosing at each generation its allele frequencies in the population so as to maximize the sum of the expected cumulative differential fitness over the alleles, plus the distribution’s entropy. Note that quantity 3 is maximized by genes, not by individuals, and that, interestingly, it is maximized with respect to current frequencies while being dependent (through U^t) on all past frequencies, and although there is some precedent to the use of “historical fitness” (9), its importance in this context is unexpected.This alternative view of selection provides a new insight into an important question in evolutionary biology, namely: How is genetic diversity maintained in the presence of natural selection (10)? That the MWUA process enhances the entropy of the alleles’ distribution (while at the same time optimizes expected cumulative utility) hints at such a mechanism. In fact, entropy is enhanced inversely proportional to s (the quantity corresponding in the population genetics domain to the parameter ε), the selection strength: The weaker the selection, the more it favors high entropy. Naturally, entropy will eventually vanish when the process quiesces at equilibrium: One allele per locus will eventually be fixed, and in fact this equilibrium may be a local, as opposed to global, fitness maximum. However, we believe that it is interesting and significant that the entropy of the allele distribution is favored by selection in the transient; in any event, mutations, environmental changes, and finite population effects are likely to change the process before equilibrium is reached. This new way of understanding the maintenance of variation in evolution (selection as a tradeoff between fitness and entropy maximization) is quite different from previous hypotheses for the maintenance of variation (e.g., refs. 11, 12). Another rather surprising consequence of this characterization is that, under weak selection, all past generations, no matter how distant, have equal influence on the change in the allele mix of the current generation.Our discussion has focused on the evolution of a fixed set of alleles; that is, we have not discussed mutations. Mutations are, of course, paramount in evolution, as they are the source of genetic diversity, and we believe that introducing mutations to the present analysis is an important research direction. Here we focus on the selection process, which is rigorously shown to be tantamount to a tradeoff, for each locus, between maximizing diversity and maximizing expected cumulative fitness.We can now note a simple yet important point. Because multiplicative weight updates by the loci operate in the presence of sex, the triple connection uncovered in this paper is informative for the “queen of problems in evolutionary biology,” namely the role of sex in evolution (13, 14). The notion that the role of sex is the maintenance of diversity has been critiqued (15), because sex does not always increase diversity, and diversity is not always favorable. The MWUA connection sheds new light on the debate, because sex is shown to lead to a tradeoff between increasing entropy and increasing (cumulative) fitness.The connection between the three fields, evolution, game theory, and learning algorithms, described here was not accessible to the founders of the modern synthesis, and we hope that it expands the mathematical tracks that can be traveled in evolution theory.     

First multi-target QSAR model for predicting the cytotoxicity of acrylic acid-based dental monomers

ObjectiveAcrylic acid derivatives are frequently used as dental monomers and their cytotoxicity towards various cell lines is well documented. This study aims to probe the structural and physicochemical attributes responsible for higher toxicity of dental monomers, using quantitative structure-activity relationships (QSAR) modeling approaches.MethodsA regression-based linear single-target QSAR (st-QSAR) model was developed with a comparatively small dataset containing 39 compounds, the cytotoxicity of which has been assessed over the Hela S3 cell line. By contrast, a classification-based multi-target QSAR model was developed with 138 compounds, the cytotoxicity of which has been reported against 18 different cell lines. Both models were set up following rigorous validation protocols confirming their statistical significance and robustness.ResultsThe performance of the linear mt-QSAR model, developed with various feature selection and post-selection similarity searching-based schemes, superseded that of all non-linear models produced with six machine learning methods by hyperparameter optimization. The final derived st-QSAR and mt-QSAR linear models are shown to be highly predictive, as well as revealing the crucial structural and physicochemical factors responsible for higher cytotoxicity of the dental monomers.SignificanceThis study is the first attempt on unveiling the cytotoxicity of dental monomers over several cell lines by means of a single multi-target QSAR model. Further, such a model is ready to get widespread applicability in the screening of new monomers, judging from its almost accurate predictions over diverse experimental assay conditions.     

多学科协作教学模式在颞下颌关节病实践教学中的应用

目的: 探讨多学科协作（multidisciplinary team, MDT）结合案例教学（case-based learning,CBL）在颞下颌关节-颌骨-咬合联合诊治教学中的实践效果。方法: 选择上海交通大学附属第九人民医院口腔外科研究生、规培生及进修生共24名,随机分为实验组和对照组,每组12名。实验组采用MDT+CBL线上线下混合式教学法,对照组采用CBL线上线下教学法。经过3个月的教学活动,通过理论与实践操作考试及问卷调查评价教学效果。采用SPSS 26.0软件包对数据进行统计学分析。结果: 实验组理论知识及操作技能考试成绩均显著高于对照组（P<0.05）;问卷调查结果显示,实验组在学习兴趣、沟通技巧、团队协作、档案管理、病历书写、临床思维能力及文献查阅方面的自我满意度均显著高于对照组（P<0.05）。结论: 在颞下颌关节病实践教学中应用MDT结合CBL线上线下混合式教学法,能提高学习成绩,激发学习兴趣,提高职业素质,增强临床综合能力,从而显著提高教学质量。     

The organizational implications of the role NHS Service Managers played in the quality process in the mid-1990s

AIM: It is the intention of this paper to highlight the problems associated with the organizational implications of the role NHS Service Managers (SMs) played in the quality process of the mid-1990s. BACKGROUND: To provide quality care all staff must be committed and involved, in this study it appeared that few SMs played a part in the process. METHODS: Semistructured taped interviews were conducted with 33 SMs and three Chief Executives in seven Trusts. As part of a study they were asked the role SMs played in quality in their clinical directorate. The data was transcribed and analysed in a content-analysis approach. FINDINGS: Quality of care was not the SMs' primary objective. The role played by SMs was dependent on their background, experience and the organization in which they worked. Most Trusts' quality-control strategy was not standardized, co-ordinated or integrated, nor was the audit process regulated. For most, quality was seen as synonymous with professions, managers from a non-professional background found the monitoring of the quality of performance inherently difficult. Only one Trust (the most successful) appeared to undertake organizational learning, influenced by the philosophy of the Chief Executive.     

Melatonin affects the temporal pattern of vocal signatures in birds

In humans and other animals, melatonin is involved in the control of circadian biological rhythms. Here, we show that melatonin affects the temporal pattern of behavioral sequences in a noncircadian manner. The zebra finch (Taeniopygia guttata) song and the crow of the Japanese quail (Coturnix japonica) are courtship vocalizations composed of a stereotyped sequence of syllables. The zebra finch song is learned from conspecifics during infancy, whereas the Japanese quail crow develops normally without auditory input. We recorded and analyzed the complete vocal activity of adult birds of both species kept in social isolation for several weeks. In both species, we observed a shortening of signal duration following the transfer from a light-dark (LD) cycle to constant light (LL), a condition known to abolish melatonin production and to disrupt circadian rhythmicity. This effect was reversible because signal duration increased when the photoperiod was returned to the previous LD schedule. We then tested whether this effect was directly related to melatonin by removal of the pineal gland, which is the main production site of circulating melatonin. A shortening of the song duration was observed following pinealectomy in LD. Likewise, melatonin treatment induced changes in the temporal structure of the song. In a song learning experiment, young pinealectomized finches and young finches raised in LL failed to copy the temporal pattern of their tutor's song. Taken together, these results suggest that melatonin is involved in the control of motor timing of noncircadian behavioral sequences through an evolutionary conserved neuroendocrine pathway.     

Teaching dementia care to physical therapy doctoral students: A multimodal experiential learning approach

ABSTRACT

As the population aged 65 and older grows, it becomes imperative for health care providers to expand their knowledge regarding geriatric conditions and concerns. Dementia is a devastating degenerative disease process that is affecting millions of individuals in the United States, with significant economic and emotional burden on family and caregivers. The need for further dementia education in physical therapy school is essential to improve attitudes and treatment that affect patient outcomes and quality of care. This physical therapy program implemented a 12-hour multimodal experiential learning module designed to educate their students on the challenges associated with dementia to increase knowledge and confidence when treating these patients. The results of this study showed statistically significant improvements in overall confidence and knowledge of treating patients with dementia. The study finds the addition of experiential learning to traditional didactic coursework improves students’ reported confidence in working with patients with dementia and understanding the challenges associated with treating patients with dementia.     

不同强度电针治疗对血管性痴呆大鼠学习记忆功能及海马CA1区β淀粉样蛋白1-40表达的影响

目的观测不同强度电针治疗对血管性痴呆大鼠学习记忆功能及海马CA1区β淀粉样蛋白1-40(Aβ1-40)表达的影响,寻求最佳电针治疗强度。方法共纳入60只雄性SPF级SD大鼠,采用随机数字表法随机选取8只大鼠为假手术组,其余大鼠采用改良的四血管阻断(4-VO)法复制VD模型,将造模成功的大鼠(24只)按随机数字表法完全随机分为模型组、1 m A电针组(频率2/15 Hz,强度1 m A,留针时间20 min)、3 m A电针组(频率2/15 Hz,强度3 m A,留针时间20 min),每组8只。电针组针刺百会、大椎穴,1次/d,连续治疗10 d,休息2 d为1个疗程。2个疗程后,采用Morris水迷宫试验检测各组大鼠学习记忆能力,运用荧光定量聚合酶链反应法检测大鼠海马CA1区Aβ1-40 m RNA表达水平。结果假手术组、模型组、1 m A电针组、3 m A电针组大鼠Morris水迷宫试验第2~5天平均逃避潜伏期分别为(46.8±1.9)、(40.6±2.3)、(24.6±1.5)、(19.4±1.2)s;(56.3±3.5)、(51.2±2.6)、(45.9±2.1)、(40.8±1.4)s;(52.7±1.5)、(46.0±2.3)、(31.3±1.2)、(27.7±1.6)s;(50.8±3.9)、(41.5±2.1)、(29.0±1.1)、(25.6±1.3)s;首次跨越原平台时间分别为(23.3±1.6)、(53.9±1.3)、(30.2±1.4)、(28.1±0.8)s,120 s内跨越原平台次数分别为(9.4±0.9)、(2.6±0.5)、(6.4±0.7)、(7.2±0.9)次;CA1区中Aβ1-40 mRNA表达水平分别为(17.3±1.1)、(40.7±1.1)、(24.0±1.7)、(22.4±1.8),组间差异均有统计学意义(F值分别为195.88、861.605、103.876、380.609,均P0.01);1 m A、3 m A电针组大鼠平均逃避潜伏期、首次跨越原平台时间较模型组明显缩短,120 s内跨越原平台次数较模型组明显增加,CA1区中Aβ1-40 mRNA表达水平较模型组明显降低,且3 m A电针组均明显优于1 m A电针组,差异均有统计学意义(均P0.05)。结论电针治疗可改善VD大鼠学习记忆能力并降低海马CA1区Aβ1-4 0 mRNA表达水平,3 m A电针效果优于1 m A电针。     

Dispositional hope and life satisfaction among older adults attending lifelong learning programs

BackgroundThe aim of this study is to explore the indirect effects of dispositional hope in the life satisfaction of older adults attending a lifelong learning program at the University of Valencia, Spain. We examine the mediating impact of dispositional hope regarding its ability to impact life satisfaction while considering affective and confidant social support, perceived health and leisure activities, consciousness and spirituality as predictors.MethodsAnalysis were based on survey data (response rate 77.4%) provided by 737 adults 55 years old or more (Mean age = 65.41, SD = 6.60; 69% woman). A structural model with latent variables was specified and estimated in Mplus.ResultsThe results show the ability of just a few variables to sum up a reasonable model to apply to successful aging population. All these variables are correlated and significantly predict hope with the exception of health. The model additionally includes significant positive indirect effects from spirituality, affective support and consciousness on satisfaction. The model has a good fit in terms of both the measurement and structural model. Regarding predictive power, these comprehensive four main areas of successful aging account for 42% of hope and finally for one third of the life satisfaction variance.ConclusionsResults support the mediating role of dispositional hope on the life satisfaction among older adults attending lifelong learning programs. These findings also support the MacArthur model of successful aging adapted to older adults with high levels of functional, social and cognitive ability. Dispositional hope, perceived health, and social support were the strongest predictors of satisfaction with life.