Miracle-dz/UniFAR-FTUs huggingface dataset:
Journal of Cerebral Blood Flow and Metabolism Changes in Pyruvate Dehydrogenase Complex Activity during and following Severe Insulin-induced Hypoglycemia

The effect of severe insulin-induced hypogly­ cemia on the activity of the pyruvate dehydrogenase en­ zyme complex (PDHC) was investigated in homogenates of frozen rat cerebral cortex during burst suppression EEG, after 10, 30, and 60 min of isoelectric EEG, and after 30 and 180 min and 24 h of recovery following 30 min of hypoglycemic coma. Changes in PDHC activity were correlated to levels of labile organic phosphates and gly­ colytic metabolites. In cortex from control animals, the rate of [1-14C]pyruvate decarboxylation was 7.1 ± 1.3 U/mg of protein, or 35% of the total PDHC activity. The activity was unchanged during burst suppression EEG whereas the active fraction increased to 81-87% during hypoglycemic coma. Thirty minutes after glucose­ induced recovery, the PDHC activity had decreased by 33% compared to control levels, and remained signifi­ cantly depressed after 3 h of recovery. This decrease in activity was not due to a decrease in the total PDHC Hypoglycemia, severe enough to cause cessation of EEG activity, is accompanied by membrane de­ During hypoglycemic coma, tissue concen­ trations of A TP and other nucleotide triphosphates are reduced to 20-40% of control within the first 5 min, and remain at these levels during sustained coma (Agardh et aI. , 1978; Chapman et ai, \981). In 122 activity. At 24 h of recovery, PDHC activity had returned to control levels. We conclude that the activation of PDHC during hypoglycemic coma is probably the result of an increased PDH phosphatase activity following de­ polarization and calcium influx, and allosteric inhibition of PDH kinase due to increased ADP! A TP ratio. The de­ pression of PDHC activity fo llowing hypoglycemic coma is probably due to an increased phosphorylation of the enzyme, as a consequence of an imbalance between PDH phosphatase and kinase activities. Since some reduction of the A TPI ADP ratio persisted and since the lactate! pyruvate ratio had normalized by 3 h of recovery, the depression of PDHC most likely reflects a decrease in PDH phosphatase activity, probably due to a decrease in intramitochondrial Ca2 +. the recovery period following glucose administra­ tion, brain metabolism is successively restored During the recovery period, however, there is prolonged neurological deficit (Ghajar et aI. , 1982) and depressed cerebral metabolic rate (Abdul-Rahman and Siesjo, 1980; Ghajar et aI. , 1982) as well as reduced cerebral blood flow (Abdul-Rahman et aI. , 1980). In the case of transient …
Constraints on Planetary Companions in the Magnification A = 256 Microlensing Event OGLE-2003-BLG-423

We develop a new method of modeling microlensing events based on a Monte Carlo simulation that incorporates both a Galactic model and the constraints imposed by the observed characteristics of the event. The method provides an unbiased way to analyze the event, especially when parameters are poorly constrained by the observed light curve. We apply this method to search for planetary companions of the lens in OGLE-2003-BLG-423, whose maximum magnification Amax = 256 ± 43 (or Amax = 400 ± 115 from the light-curve data alone) is the highest among single-lens events ever recorded. The method permits us for the first time to place constraints directly in the planet mass-projected physical separation plane rather than in the mass ratio-Einstein radius plane as was done previously. For example, Jovian-mass companions of main-sequence stars at 2.5 AU are excluded with 80% efficiency.
On Channel-Aware Secure HARQ-IR

In this paper, we study secure hybrid automatic retransmission request (HARQ) over block-fading channels when the channel from a legitimate transmitter (i.e., Alice) to an eavesdropper (i.e., Eve) is less noisy than that to a legitimate receiver (i.e., Bob). In order to have a positive secrecy rate, we exploit the notion of the channel reciprocity in time division duplex mode, where the shared channel state information between Alice and Bob is used as a secret key. The resulting HARQ scheme is referred to as channel-aware secure HARQ (CAS-HARQ) in this paper. In this scheme, in order to keep Eve ignorant of the confidential message from Alice to Bob, deliberative transmissions of random message blocks are considered when the channel to Bob is weak. We derive closed-form expressions for bounds on the probabilities of connection outage and secrecy outage to see reliability and security, respectively, of CAS-HARQ. Based on our analysis, we can see that it is possible to have a positive secrecy rate with certain outage probabilities even if the channel to Eve is less noisy than that to Bob.
Credit Card Fraud Detection Using Neural Network

- The payment card industry has grown rapidly the last few years. Companies and institutions move parts of their business, or the entire business, towards online services providing e-commerce, information and communication services for the purpose of allowing their customers better efficiency and accessibility. Regardless of location, consumers can make the same purchases as they previously did “over the desk”. The evolution is a big step forward for the efficiency, accessibility and profitability point of view but it also has some drawbacks. The evolution is accompanied with a greater vulnerability to threats. The problem with making business through the Internet lies in the fact that neither the card nor the cardholder needs to be present at the point-of-sale. It is therefore impossible for the merchant to check whether the customer is the genuine cardholder or not. Payment card fraud has become a serious problem throughout the world. Companies and institutions loose huge amounts annually due to fraud and fraudsters continuously seek new ways to commit illegal actions. The good news is that fraud tends to be perpetrated to certain patterns and that it is possible to detect such patterns, and hence fraud. In this paper we will try to detect fraudulent transaction through the neural network along with the genetic algorithm. As we will see that artificial neural network when trained properly can work as a human brain, though it is impossible for the artificial neural network to imitate the human brain to the extent at which brain work, yet neural network and brain, depend for there working on the neurons, which is the small functional unit in brain as well as ANN. Genetic algorithm are used for making the decision about the network topology, number of hidden layers, number of nodes that will be used in the design of neural network for our problem of credit card fraud detection. For the learning purpose of artificial neural network we will use supervised learning feed forward back propagation algorithm. Finally we will see what future work can be done in making fraud detection.
Automatic Determination of Fiber-Length Distribution in Composite Material Using 3D CT Data

Determining fiber length distribution in fiber reinforced polymer components is a crucial step in quality assurance, since fiber length has a strong influence on overall strength, stiffness, and stability of the material. The approximate fiber length distribution is usually determined early in the development process, as conventional methods require a destruction of the sample component. In this paper, a novel, automatic, and nondestructive approach for the determination of fiber length distribution in fiber reinforced polymers is presented. For this purpose, high-resolution computed tomography is used as imaging method together with subsequent image analysis for evaluation. The image analysis consists of an iterative process where single fibers are detected automatically in each iteration step after having applied image enhancement algorithms. Subsequently, a model-based approach is used together with a priori information in order to guide a fiber tracing and segmentation process. Thereby, the length of the segmented fibers can be calculated and a length distribution can be deduced. The performance and the robustness of the segmentation method is demonstrated by applying it to artificially generated test data and selected real components.
Research on CGF Entity Model Based on Factory Mechanism

The model engine development for CGF entities is designed and realized based on ldquoabstract factoryrdquo and ldquofactory methodrdquo design pattern. Firstly, the entities factory manager encapsulates the responsibility and the process of creating CGF entities, and provides an interface for creating families of related or dependent CGF entities without specifying their concrete classes; then, each created families of CGF entities is defined a corresponding parameterized factory, and it is overridden by the parameterized class identifier for each to extend or change the CGF entity product; Finally, configure and modify the created CGF entities or its components, and edit the loading information in a MTL file. The result of the engine design shows that it isolates concrete classes and makes exchanging and extending product families easy. The method also can help the client construct the CGF entity models quickly in order to reduce time and complexity.
Pricing Prototypical Products

When we think of colas, Coca-Cola first comes to mind. Products such as Cola-Cola, Tide laundry detergent, and Chapstick lip balm are the prototypical products in their respective categories. For more than three decades, research in consumer psychology has accumulated evidence on how prototypicality influences memory, shapes the composition of consideration set, and affects purchase decision. Yet there is no research on how it changes the competitive behavior of firms in a horizontally differentiated market. For example, some prototypical products are priced lower than other products in their category, whereas in certain other categories the prototypical product is priced higher. We propose a novel model of spatial competition, where the prototypicality of a product influences the probability of the product being included in consumers' consideration sets without affecting its valuation. Using the model, we examine theoretically the impact of prototypicality on the pricing decisions of competing firms. Our analysis shows that when consumer valuations are low, the prototypical product is priced lower than a nonprototypical product and earns more profits. However, when consumer valuations are high, the rank order of the prices of the prototypical product and a nonprototypical product is reversed, but not the order of profits. We subject these predictions to an empirical test. The experimental results lend support for the qualitative predictions of the model.
Results and Discussion of Str Analysis by Mass Spectrometry Marker Selection and Feasibility Studies with Str Loci

I n the course of this work, thousands of data points were collected using STR markers of forensic interest verifying that GeneTrace' s mass spectrometry technology works. During this same time, tens of thousands of data points were gathered across hundreds of different microsatel-lite markers from corn and soybean as part of an ongoing plant genomics partnership with Monsanto Company (St. Louis, MO). Whether the DNA markers used come from humans or plants, the characteristics described below apply when analyzing polymor-phic repeat loci. Prior to receiving grant funding, feasibility work had been completed using the STR markers TH01, CSF1PO, FES/FPS, and F13A1 in the summer and fall of 1996 (Becker et al., 1997). At the start of this project, a number of STR loci were considered as possible candidates to expand upon the initial four STR markers and to develop a set of markers that would work well in the mass spectrometer and would be acceptable to the forensic DNA community. Searches were made of publicly available databases, including the Cooperative Human Linkage Center Literature was also searched for possible tetranucleotide markers with PCR product sizes below 140 bp in size to avoid having to redesign the PCR primers to meet our limited size range needs (Hammond et al., 1994; Lindqvist et al., 1996). The desired characteristics also included high het-erozygosity, moderate number of alle-les (<7 or 8 to maintain a narrow mass range) with no known microvariants (to avoid the need for a high degree of resolution), and balanced allele frequencies (most commonly allele <40% and least common allele >5%). This type of marker screen was found to be rather inefficient because the original primer sets reported in public STR databases were designed for gel-based separations, which were optimal over a size range of 100–400 bp. In fact, most of the PCR product sizes were in the 200–300 bp range. From a set of several thousand publicly available STRs, only a set of eight candidate tetranu-cleotide STRs were initially identified; three of which were tested using the original reported primers (exhibit 33). The initial goal was to identify ~25 markers that spanned all 22 autosomal chromosomes as well as the X and Y sex chromosomes. Researchers quickly realized that population data were not available on these " new " markers and would not be readily accepted without extensive testing and validation. Since one of the objectives was to produce …
Optimal Joint User Association and Resource Allocation in Heterogeneous Networks via Sparsity Pursuit

This paper studies the joint user association and resource allocation in heterogeneous networks (HetNets) from a novel perspective, motivated by and generalizing the idea of fractional frequency reuse. By treating the multi-cell multi-user resource allocation as resource partitioning among multiple reuse patterns, we propose a unified framework to analyze and compare a wide range of user association and resource allocation strategies for HetNets, and provide an optimal benchmark for network performance. The enabling mechanisms are a novel formulation to consider all possible interference patterns or any pre-defined subset of patterns, and efficient sparsity-pursuit algorithms to find the solution. A notable feature of this formulation is that the patterns remain fixed during the resource optimization process. This creates a favorable opportunity for convex formulations while still considering interference coupling. More importantly, in view of the fact that multi-cell resource allocation is very computational demanding, our framework provides a systematic way to trade off performance for the reduction of computational complexity by restricting the candidate patterns to a small number of feature patterns. Relying on the sparsity-pursuit capability of the proposed algorithms, we develop a practical guideline to identify the feature patterns. Numerical results show that the identified feature patterns can significantly improve the existing strategies, and jointly optimizing the user association and resource allocation indeed brings considerable gain.
Symbiosis of Search and Heuristics for Random 3-SAT

Delft University of Technology, Delft, The NetherlandsAbstract. When combined properly, search techniques can reveal the full poten-tial of sophisticated branching heuristics. We demonstrate this observation on thewell-known class of random 3-SAT formulae. First, a new branching heuristic ispresented, which generalizes existing work on this class. Much smaller searchtrees can be constructed by using this heuristic. Second, we introduce a variantof discrepancy search, called ALDS. Theoretical and practical evidence supportthat ALDS traverses the search tree in a near-optimal order when combined withthe new heuristic. Both techniques, search and heuristic, have been implementedin the look-ahead solvermarch. The SAT 2009 competition results show thatmarch is by far the strongest complete solver on random k-SAT formulae.
Transition Detection and Activity Classification from Wearable Sensors Using Singular Spectrum Analysis

This paper proposes the use of singular spectrum analysis (SSA) to segment and classify human activities in real time by using an ear-worn Activity Recognition (e-AR) sensor. A similarity measure is calculated using SSA to construct a 3D feature vector from the 3 axes of e-AR signal. An algorithm based on the concept of clustering and buffering is then implemented in order to detect activity transition in real time as subjects perform their daily activities. An incremental subspace learning algorithm based on SSA is also proposed for activity classification. The proposed algorithm is applied to a group of five subjects performing daily activities and the results have shown the effectiveness of the method for transition detection and activity classification.
D 2 Dopamine Receptors Recruit a GABA Component for Their Attenuation of Excitatory Synaptic Transmission in the Adult Rat Prefrontal Cortex

The dopamine modulation of neuronal excitability in the prefrontal cortex (PFC) changes during critical late periods of postnatal development. In particular, D2 receptors activate fast-spiking interneurons after, and not before, adolescence. To test the functional impact of this change, we investigated the effects of dopamine agonists on PFC excitatory synaptic transmission with whole-cell recordings from deep-layer pyramidal neurons in brain slices obtained from prepubertal [postnatal day (PD) 28–35] and postpubertal (PD > 51) rats. Electrical stimulation of superficial layers elicited a fast AMPA/kainate excitatory postsynaptic potential (EPSP). In the adult PFC, the D2 agonist quinpirole decreased EPSP amplitude, an effect that lasted for at least 25 min after drug washout and was blocked by the D2 antagonist eticlopride. The late component of this effect was blocked by the GABA-A antagonist picrotoxin without affecting the early inhibition. Quinpirole also decreased EPSP amplitude in deep-layer pyramidal neurons from prepubertal rats, but this response was not affected by picrotoxin. A D1 agonist, on the other hand, did not affect the pyramidal neuron EPSP. These results indicate that D2, not D1, receptors attenuate local excitatory synaptic transmission in the adult PFC, and this effect of D2 involves a recruitment of local GABAergic activity. Synapse 61:843–850, 2007. VC 2007 Wiley-Liss, Inc.
The Recovery of High Speed Vibration Video Based on Compressive Sensing

The compressive sensing is a technique which can reduce the sampling rate at the time of sampling the signal. Most high speed video cameras have relatively low spatial resolution. It is hard to overcome the tradeoff between temporal and spatial resolution without incurring a remarkable increase in hardware costs. In this paper, in order to overcome this tradeoff, we adopt compressive sensing techniques for time sampling of video, representing and reconstructing the high speed video from low frame rate image sample which is compressed by the space-time coded exposure measurement matrix, and accomplish sparse representation of a video by learning an over-complete dictionary on video patches. The experimental results prove the effectiveness of the method, and realize the recovery of the high speed vibration video from low frame rate compressed sampling video.
Emotional Consequences of Categorizing Victims of Negative Outgroup Behavior as Ingroup or Outgroup

This research examined whether people experience anger after perceiving intentional and unfair behavior of an outgroup which has negative consequences for others, but not for themselves. It was predicted that such outgroup behavior causes anger in the observer, dependent on the categorization of the victims as part of its own group or as part of another group. Participants were primed with information that made either differences or similarities between them and the victims salient, after which they were confronted with negative behavior of an outgroup. Results confirmed the prediction that the same information concerning unfair and intentional behavior of an outgroup harming others led to more anger in the observer when the victims were perceived as ingroup rather than outgroup. Moreover, anxiety was not affected by perception of victims as part of the ingroup or outgroup, suggesting that specific emotions rather than just negative affect were influenced.
Managing Scope Creep with Design Patterns in Formal Specifications

Scope creep can affect the timeliness of the software engineering process. Scope creep often occurs when features are changed or added. Software specification languages provide a formal method for extrapolating and expanding requirements. Several design patterns provide object oriented constructs whose goal are to mitigate changes in code. Applying these design patterns to formal specifications can help mitigate changes. Structural patterns realize communication between objects, thus identifying how these objects and communications can change. Using a combination of these pattern types, specifications can be redesigned with the focus of mitigating scope creep. This paper will focus on the patterns which fall into these two categories to design specifications, even if the requirements don’t specify this behavior. This paper will identify a template of specifications which will aide developers in identifying how specifications can be designed with mitigating scope creep in mind.
Differential Inhibition of Ex-Vivo Tumor Kinase Activity by Vemurafenib in BRAF(V600E) and BRAF Wild-Type Metastatic Malignant Melanoma

Background Treatment of metastatic malignant melanoma patients harboring BRAF(V600E) has improved drastically after the discovery of the BRAF inhibitor, vemurafenib. However, drug resistance is a recurring problem, and prognoses are still very bad for patients harboring BRAF wild-type. Better markers for targeted therapy are therefore urgently needed. Methodology In this study, we assessed the individual kinase activity profiles in 26 tumor samples obtained from patients with metastatic malignant melanoma using peptide arrays with 144 kinase substrates. In addition, we studied the overall ex-vivo inhibitory effects of vemurafenib and sunitinib on kinase activity status. Results Overall kinase activity was significantly higher in lysates from melanoma tumors compared to normal skin tissue. Furthermore, ex-vivo incubation with both vemurafenib and sunitinib caused significant decrease in phosphorylation of kinase substrates, i.e kinase activity. While basal phosphorylation profiles were similar in BRAF wild-type and BRAF(V600E) tumors, analysis with ex-vivo vemurafenib treatment identified a subset of 40 kinase substrates showing stronger inhibition in BRAF(V600E) tumor lysates, distinguishing the BRAF wild-type and BRAF(V600E) tumors. Interestingly, a few BRAF wild-type tumors showed inhibition profiles similar to BRAF(V600E) tumors. The kinase inhibitory effect of vemurafenib was subsequently analyzed in cell lines harboring different BRAF mutational status with various vemurafenib sensitivity in-vitro. Conclusions Our findings suggest that multiplex kinase substrate array analysis give valuable information about overall tumor kinase activity. Furthermore, intra-assay exposure to kinase inhibiting drugs may provide a useful tool to study mechanisms of resistance, as well as to identify predictive markers.
Large-Scale Query Understanding

In this paper, we propose a large-scale multi-dimensional co-clustering framework for understanding queries in a search engine. To achieve this goal, the system simultaneously clusters queries along with attributes of results that were shown (and clicked) on these queries. In our application, we co-cluster queries along with advertisements (commercial results), advertisement keywords, and query terms — this gives us the ability to look at concepts that correspond to groups of queries and ads, as well as do better noise filtering in query clustering [1]. After getting query clusters, we identify representative queries for each cluster, in an attempt to explain what concept underlies a cluster. Our system extends the co-clustering MapReduce framework [2] to perform multi-dimensional co-clustering at scale.