Miracle-dz/UniFAR-FTUs huggingface dataset:
Protrusion of N-acetylglucosamine Kinase Clusters into the Base of Excitatory Synapses

N-Acetylglucosamine kinase (GlcNAc kinase or NAGK; EC 2.7.1.59) catalyzes the phosphorylation of GlcNAc to GlcNAc-6-phosphate (GlcNAc-6-P). Despite detailed characterization of the enzyme itself, there have been few studies on the expression of NAGK in mammalian tissues. In the rat hippocampal neuron in culture, NAGK-immunoreactivity (IR) formed clusters in somatodendritic domains. In this study we characterized the NAGK clusters that protrude out the long axis of dendritic shafts. By double-labeling of the neurons with antibodies against NAGK and various synaptic proteins, we show that NAGK is positioned at the base of spines, while there were no NAGK protrusions into inhibitory postsynaptic sites. Immunoblot analysis showed that NAGK was included in synaptosomes but not in PSD fractions. Our results indicate that the NAGK clusters at the dendritic periphery protrude into spines.
Racial differences in survival among men with prostate cancer and comorbidity at time of diagnosis.

OBJECTIVES This study evaluated the effect of comorbidity at diagnosis on racial differences in survival among men with prostate cancer. METHODS Clinical and demographic data were abstracted from records of 864 patients diagnosed at 4 Chicago area hospitals between 1986 and 1990. Comorbidity was scored on the basis of clinical information in the Charlson index. Cause-specific relative mortality adjusted for age, stage, differentiation, and treatment was compared across Charlson scores with Cox proportional hazards functions. RESULTS Blacks had significantly greater mortality from prostate cancer and other causes (vs Whites, relative risk [95% confidence interval] = 1.84 [1.22, 2.79] and 1.69 [1.33, 2.29], respectively; P <.001). However, differences disappeared as initial comorbidity increased (1.75 [1.33, 2.31] vs 0.90 [0.59, 1.29] for scores = 0 and > or =5, respectively). CONCLUSIONS Absence of a significant preexisting medical diagnosis is associated with a higher risk for excess mortality among Black men diagnosed with prostate cancer.
Income distribution in macroeconomic models

This book looks at the distribution of income and wealth and the effects that this has on the macroeconomy, and vice versa. Is a more equal distribution of income beneficial or harmful for macroeconomic growth, and how does the distribution of wealth evolve in a market economy? Taking stock of results and methods developed in the context of the 1990s revival of growth theory, the authors focus on capital accumulation and long-run growth. They show how rigorous, optimization-based technical tools can be applied, beyond the representative-agent framework of analysis, to account for realistic market imperfections and for political-economic interactions. The treatment is thorough, yet accessible to students and nonspecialist economists, and it offers specialist readers a wide-ranging and innovative treatment of an increasingly important research field. The book follows a single analytical thread through a series of different growth models, allowing readers to appreciate their structure and crucial assumptions. This is particularly useful at a time when the literature on income distribution and growth has developed quickly and in several different directions, becoming difficult to overview.
Warrants Price Forecasting using Kernel Machine and EKF-ANN: a Comparative Study

Due to the six unreasonable assumptions companioned with the Black-Scholes options pricing model (BSM), which often make the miss-pricing result because of the difference of market convention in practical. This study try to combine the BSM and extended Kalman filters-based artificial neural networks (EKF-ANN) to deal with the limitation of consideration of the influences from many unexpected real world phenomena. If we were to soundly take these phenomena into account, the pricing error could be reduced. In this paper, we try to make a comparative study with examined the forecasting accuracy between the BSM-based kernel machines (KM-BSM) and the BSM-based EKF-ANN (EKF-ANN-BSM). From the evidence of Taiwan Warrants market, we found that the performance indicates the KM is superior to the others, and the hybrid EKF-ANN-BSM framework is also better than the pure EKF-ANN. The results show that the KM-BSM and hybrid model could significantly reduce the normalized root-mean-squareerrors (NRMSE) of forecasting, it helps to provide an alternative way to refine the options valuation.
An Analog Multilayer Perceptron Neural Network for a Portable Electronic Nose

This study examines an analog circuit comprising a multilayer perceptron neural network (MLPNN). This study proposes a low-power and small-area analog MLP circuit to implement in an E-nose as a classifier, such that the E-nose would be relatively small, power-efficient, and portable. The analog MLP circuit had only four input neurons, four hidden neurons, and one output neuron. The circuit was designed and fabricated using a 0.18 μm standard CMOS process with a 1.8 V supply. The power consumption was 0.553 mW, and the area was approximately 1.36 × 1.36 mm2. The chip measurements showed that this MLPNN successfully identified the fruit odors of bananas, lemons, and lychees with 91.7% accuracy.
Adaptive Representation of Specular Light Flux

Caustics produce beautiful and intriguing illumination patterns. However, their complex behavior make them difficult to simulate accurately in all but the simplest configurations. To capture their appearance, we present an adaptive approach based upon light beams. The coherence between light rays forming a light beam greatly reduces the number of samples required for precise illumination reconstruction. The light beams characterize the distribution of light due to interactions with specular surfaces (specular light flux) in 3D space, thus allowing for the treatment of illumination within single-scattering participating media. The hierarchical structure enclosing the light beams possesses inherent properties to detect efficiently every light beam reaching any 3D point, to adapt itself according to illumination effects in the final image, and to reduce memory consumption via caching.
Data Segmentation for Region Detection in a Sensor Network

We present an online method for automatically segmenting the data stream generated at a sensor node into distinct sub-sequences. The data at each node are assumed to be generated by a stochastic process. The data stream (sequence) sensed at each node is split into a new sub-sequence when the Kullback-Leibler distance between the probability distributions induced by the previously observed data and that induced by the most recently observed data exceeds a threshold. A distributed region growing algorithm is used to cluster spatially proximal sensor nodes that observe similar data streams. Thus, the space being monitored by the sensors can be divided into regions that observe similar activity. Continuous data segmentation and region growing enables changes in the pattern of activity to be observed as shifting spatial regions. We provide an analysis of the time taken for convergence and the power required for transmitting messages when the nodes are arranged in a regular grid and when they are randomly distributed. We present simulation results that show the performance of these algorithms on a shadow tracking application. The data used in these simulations was obtained from a sensor network equipped with light sensors and deployed outdoors.
Novel adaptive algorithm based on least mean p-power error criterion for fourier analysis in additive noise

This paper presents a novel adaptive algorithm for the estimation of discrete Fourier coefficients (DFC) of sinusoidal and/or quasi-periodic signals in additive noise. The algorithm is derived using a least mean p-power error criterion. It reduces to the conventional LMS algorithm when p takes on 2. It is revealed by both analytical results and extensive simulations that the new algorithm for p - 3,4 generates much improved DFC estimates in moderate and high SNR environments compared to the LMS algorithm, while both have similar degrees of complexity. Assuming the Gaussian property of the estimation error, the proposed algorithm including the LMS algorithm is analyzed in detail. Elegant dynamic equations and closed form noise misadjustment expressions are derived and clarified.
Movement Smoothness Changes during Stroke Recovery

Smoothness is characteristic of coordinated human movements, and stroke patients' movements seem to grow more smooth with recovery. We used a robotic therapy device to analyze five different measures of movement smoothness in the hemiparetic arm of 31 patients recovering from stroke. Four of the five metrics showed general increases in smoothness for the entire patient population. However, according to the fifth metric, the movements of patients with recent stroke grew less smooth over the course of therapy. This pattern was reproduced in a computer simulation of recovery based on submovement blending, suggesting that progressive blending of submovements underlies stroke recovery.
Face Fusion Recognition Based on Bit-Plane Image

Feature extraction is the main technology in the process of human face recognition system which is widely uti- lized as the mainstream approach in identity verification system. However, the traditional human face feature extraction algorithm of Eigenface has strict restraint on the light in the original human face image, posing as a hindrance in the prac- tical application. Aiming at this problem, this paper proposes a novel method for human face feature extraction based on human face bit-plane image using Eigenface and Fisher's linear discriminant analysis at the same time. The constitution and characteristic of the fusion recognition method is analyzed and the experiments on performance study are proved the superiority of the new fusion recognition method.
Noise selection approach to image restoration

This paper deals with a restoration (both denoising and deblurring) method. For instance in the case of denoising, this latter is only a small modification from the usual wavelet thresholding. However, it has the significant advantage to allow the use of several bases in such a way that we select what is considered as information by a basis or another basis or another basis, and so on for as many bases as we want. The computational cost of the method is mainly the computation of the coordinates of the signal (or image) in the bases.
Aging and subjective well-being in later life.

OBJECTIVES This paper examines age-related changes in subjective well-being (SWB) in later life using multiple measures that cover eudemonic, evaluative, and affective dimensions of well-being. METHOD Using data from 5 waves of respondents aged 50 and older from the English Longitudinal Study of Ageing (2002-11), we fit multilevel linear growth curve models to examine the cohort differences and individual aging effects on quality of life, depressive symptomatology, and life satisfaction. RESULTS Older cohorts are shown to have equivalent or better SWB than younger cohorts for each well-being measure. Nonetheless, individual aging effects for each well-being measure were observed with deterioration in well-being being greatest in older cohorts, even when adjusting for age-related changes in later life, including widowhood, retirement, and declining health. DISCUSSION The results suggest that although older cohorts enjoy higher levels of SWB than their younger counterparts when under similar circumstances, they experience sharper declines, especially in the very oldest cohorts. The findings demonstrate the importance of separating out cohort differences and aging effects and also of taking into account the multidimensionality of SWB to determine the point at which age deterioration begins to occur across different measures.
A Bio-Inspired Swarming Algorithm for Decentralized Access in Cognitive Radio

The goal of this paper is to propose a bio-inspired radio access mechanism for cognitive networks mimicking the behavior of a flock of birds swarming in search for food in a cohesive fashion without colliding with each other. The equivalence between swarming and radio resource allocation is established by modeling the interference distribution in the resource domain, e.g., frequency and time, as the spatial distribution of food, while the position of the single bird represents the radio resource chosen by each radio node. The swarming mechanism is enforced by letting every node allocate its resources (power/bits) in the time-frequency regions where the interference is minimum (the food density is maximum), avoiding collisions with other nodes (birds), yet limiting the spread in the time-frequency domain (i.e., maintaining the swarm cohesion). The solution is given as the distributed minimization of a functional, borrowed from social foraging swarming models, containing the average interference plus repulsion and attraction terms that help to avoid conflicts and maintain cohesiveness, respectively.
COMPLEXITY AND LOCOMOTION IN VIRTUALLY EMBODIED QUADRUPED ROBOT

This paper investigates the use of a multi-objective approach for evolving artificial neural networks that act as controllers for the legged locomotion of a 3-dimensional, artificial quadruped creature simulated in a physics-based environment. The Paretofrontier Differential Evolution (PDE) algorithm is used to generate a pareto optimal set of artificial neural networks that optimizes the conflicting objectives of maximizing locomotion behavior and minimizing neural network complexity. Here we also analyze the evolutionary dynamics of controller evolution.
The Price Is Right, but Are the Bids? An Investigation of Rational Decision Theory

The television game show The Price Is Right is used as a laboratory to conduct a preference-free test of rational decision theory in an environment with substantial economic incentives. It is found that contestants' strategies are transparently suboptimal. In response to this evidence, simple rules of thumb are developed that are shown to explain observed bidding patterns better than rational decision theory. Further, learning during the show reduces the frequency of strategic errors. This is interpreted as evidence of bounded rationality. Finally, there is no evidence that a concern for fairness significantly alters bidding behavior. Copyright 1996 by American Economic Association.
An Anti-β-Amyloid Vaccine for Treating Cognitive Deficits in a Mouse Model of Down Syndrome

In Down syndrome (DS) or trisomy of chromosome 21, the β-amyloid (Aβ) peptide product of the amyloid precursor protein (APP) is present in excess. Evidence points to increased APP gene dose and Aβ as playing a critical role in cognitive difficulties experienced by people with DS. Particularly, Aβ is linked to the late-life emergence of dementia as associated with neuropathological markers of Alzheimer’s disease (AD). At present, no treatment targets Aβ–related pathogenesis in people with DS. Herein we used a vaccine containing the Aβ 1–15 peptide embedded into liposomes together with the adjuvant monophosphoryl lipid A (MPLA). Ts65Dn mice, a model of DS, were immunized with the anti-Aβ vaccine at 5 months of age and were examined for cognitive measures at 8 months of age. The status of basal forebrain cholinergic neurons and brain levels of APP and its proteolytic products were measured. Immunization of Ts65Dn mice resulted in robust anti-Aβ IgG titers, demonstrating the ability of the vaccine to break self-tolerance. The vaccine-induced antibodies reacted with Aβ without detectable binding to either APP or its C-terminal fragments. Vaccination of Ts65Dn mice resulted in a modest, but non-significant reduction in brain Aβ levels relative to vehicle-treated Ts65Dn mice, resulting in similar levels of Aβ as diploid (2N) mice. Importantly, vaccinated Ts65Dn mice showed resolution of memory deficits in the novel object recognition and contextual fear conditioning tests, as well as reduction of cholinergic neuron atrophy. No treatment adverse effects were observed; vaccine did not result in inflammation, cellular infiltration, or hemorrhage. These data are the first to show that an anti-Aβ immunotherapeutic approach may act to target Aβ-related pathology in a mouse model of DS.
An exploratory study of Hofstede’s cross‐cultural dimensions in construction projects

Singaporean construction firms have increasingly invested in China in recent years. It is, therefore, important for Singaporean construction firms as well as their Chinese counterparts to appreciate and understand each other’s cultural differences/similarities. Although Singapore culture appears to be one based predominately on Chinese culture, there remain differences between the two, which, if not properly understood, can lead to ineffectiveness and misunderstandings. Using the four dimensions of a national culture established by Hofstede, this exploratory study examines what constitute Singapore culture and Chinese culture. Through a survey of Singaporean and Chinese respondents working in China and an analysis of Hofstede’s four dimensions of a national culture, the study extrapolates the cross‐cultural dimensions brought about by the two cultures within the context of construction projects.
miR-497 suppresses epithelial–mesenchymal transition and metastasis in colorectal cancer cells by targeting fos-related antigen-1

Objective MicroRNAs have key roles in tumor metastasis. The acquisition of metastatic capability by cancer cells is associated with epithelial–mesenchymal transition (EMT). Here, we describe the role and molecular mechanism of miR-497 in colorectal cancer (CRC) cell EMT, migration, and invasion. Methods Quantitative real-time polymerase chain reaction and Western blot assays were performed to detect the expression levels of miR-497 and Fos-related antigen-1 (Fra-1) in the CRC cells. HCT116 and SW480 cells with miR-497 overexpression or Fra-1 low expression were constructed by lipofection. Target prediction and luciferase reporter assays were performed to investigate whether Fra-1 is one of the targets of miR-497. Western blot and Transwell assays were performed to detect the effects of miR-497 and Fra-1 on CRC cell EMT, migration and invasion. Results We searched the miRanda, TargetScan, and PicTar databases and found that Fra-1, a key driver of CRC metastasis, is a potential target of miR-497. Quantitative real-time polymerase chain reaction and Western blot analysis verified downregulation of miR-497 and upregulation of Fra-1 in CRC cells. Western blot and Transwell assays showed that overexpression of miR-497 suppresses CRC cell EMT, migration, and invasion. Luciferase gene reporter assay revealed that Fra-1 is a downstream target of miR-497 as miR-497 bound directly to the 3′ untranslated region of Fra-1 messenger RNA. An inverse correlation was also found between miR-497 and Fra-1 in HCT116 and SW480 cells. Furthermore, knockdown of Fra-1 recuperated the effects of miR-497 overexpression. Conclusion miR-497 suppresses CRC cell EMT, migration, and invasion partly by targeting Fra-1.
A Hybrid Regression Test Selection Technique for Object-Oriented Programs

We propose a regression test selection technique that is based on analysis of both the source code of an object-oriented program as well as the UML state machine models of the affected classes. We first construct a dependency graph model of the original program from the source code. When the program is suitably modified, the constructed model is updated to reflect the changes. Our model in addition to capturing control and data dependencies represents the dependencies arising from object-relations. To find the model elements affected due to a program change, we construct a forward slice of the constructed graph model, where each changed model element is used as a slicing criterion. Subsequently, we determine the affected methods from an analysis of the state machine models based on the changed statements. The test cases that exercise the affected model elements in the program model as well as the transitions caused by the affected methods in state machine models are selected for regression testing. Empirical studies carried out by us show that our technique selects on an average of 27.89 % more fault-revealing test cases compared to a purely dependence graph model-based technique while incurring about 38.21% increase in regression test suite size.
Communication, symbolic communication, and language: Comment on Savage-Rumbaugh, McDonald, Sevcik, Hopkins, and Rupert (1986).

Savage-Rumbaugh, McDonald, Sevcik, Hopkins, and Rupert's (1986) description of their pygmy chimpanzees' behavior raises many interesting questions about what they have learned. Their behavior is communicative, but is it symbolic and how does it relate to the child's use oflanguage? SavageRumbaugh et al, interpreted this behavior as "symbolic communication. " However, this interpretation does not account for significant aspects of the apes' performance. For example, Kanzi's performance on the vocabulary test differed greatly from his performance in naturalistic exchanges, which would not have been expected if he had in fact learned that lexigrams are symbols. The apes' performance is consistent with the hypothesis that they have learned the instrumental functions of lexigrams in the experimental context. That is, they use lexigrams to mediate the receipt of desired outcomes such as food or travel. This behavior, which Skinner (1957) termed manding, does not require knowledge of words or symbols at all. The apes' use oflcxigrams appears to be more like the nonlinguistic gestural communication of very young children than the use of full lexical items. The dichotomy between the apes' linguistic and cognitive capacities is discussed in terms of implications concerning the possible species specificity oflanguage. Savage-Rumbaugh, McDonald, Sevcik, Hopkins, and Rupert (1986) reported intriguing results from an ongoing study of two pygmy chimpanzees. We share their excitement over this research, but for a different reason: Their data, together with our own data on children's early communication, put us on the brink of being able to situate the apes' communicative capacities vis a vis those of children. Their behaviors are similar to nonlinguistic gestures used by 9- to 16-month-old children. Kanzi's behavior raises important questions about haw he learns; we agree that his remarkable learning abilities distinguish him from other chimpanzees. The study also raises questions about what was learned, specifically how Kanzi's "symbolic communication" compares with the behavior of other language-trained apes and with that of children acquiring first languages. Here we disagree with many of Savage-Rumbaugh et al.'s (1986) conclusions. Kanzi's behavior differs from children's use oflanguage in systematic ways; positive comparisons to children can only be sustained by holding underspecified theories of symbolic communication and naming. We also disagree with their characterization of the extent to which Kanzi's communicative behavior differs from that of other language-trained apes. The similarities in the behaviors of several apes, including Kanzi, who have been exposed to very different experimental conditions suggest to us some basic generalizations about the