Miracle-dz/UniFAR-FTUs huggingface dataset:
Stem Cell Therapy and Curcumin Synergistically Enhance Recovery from Spinal Cord Injury

Acute traumatic spinal cord injury (SCI) is marked by the enhanced production of local cytokines and pro-inflammatory substances that induce gliosis and prevent reinnervation. The transplantation of stem cells is a promising treatment strategy for SCI. In order to facilitate functional recovery, we employed stem cell therapy alone or in combination with curcumin, a naturally-occurring anti-inflammatory component of turmeric (Curcuma longa), which potently inhibits NF-κB. Spinal cord contusion following laminectomy (T9–10) was performed using a weight drop apparatus (10 g over a 12.5 or 25 mm distance, representing moderate or severe SCI, respectively) in Sprague-Dawley rats. Neural stem cells (NSC) were isolated from subventricular zone (SVZ) and transplanted at the site of injury with or without curcumin treatment. Functional recovery was assessed by BBB score and body weight gain measured up to 6 weeks following SCI. At the conclusion of the study, the mass of soleus muscle was correlated with BBB score and body weight. Stem cell therapy improved recovery from moderate SCI, however, it had a limited effect on recovery after severe SCI. Curcumin stimulated NSC proliferation in vitro, and in combination with stem cell therapy, induced profound recovery from severe SCI as evidenced by improved functional locomotor recovery, increased body weight, and soleus muscle mass. These findings demonstrate that curcumin in conjunction with stem cell therapy synergistically improves recovery from severe SCI. Furthermore, our results indicate that the effect of curcumin extends beyond its known anti-inflammatory properties to the regulation of stem cell proliferation.
Control of Resistance Training Intensity by the Omni Perceived Exertion Scale

Naclerio, F, Rodríguez-Romo, G, Barriopedro-Moro, MI, Jiménez, A, Alvar, BA, and Triplett, NT. Control of resistance training intensity by the omni perceived exertion scale. J Strength Cond Res 25(7): 1879-1888, 2011—The purpose of this study was to determine the applicability of the rating of perceived exertion (RPE) scale as a means of controlling resistance training intensity and establishing the relationship between the RPE value, load, and mechanical power (MP) produced during the bench press. Eleven men (22.1 ± 1.0 years) were evaluated on 8 separate days with 48 hours of rest between sessions. After determining the 1 repetition maximum (1RM) value, each subject underwent 7 tests until achieving muscular failure with the following percentage ranges: 30-40, >40-50, >50-60, >60-70, >70-80, >80-90, and >90%. A rotary encoder and the OMNI-RES (0-10) scale were used to estimate the power and to determine the perception of effort (RPE) expressed after each repetition of each set. The RPE produced from the start to the end of each set was related to the percentage of the load and the variability of the MP measured. Additionally, except for the >90% range, significant differences (p < 0.05) between the initial RPE (RPE I) and the average RPE of the first 3 repetitions (RPE 1_3 rep) with respect to the RPE produced with a 10% reduction in MP were identified for all the ranges. These relationships demonstrate the utility of RPE for controlling resistance training intensity.
Greedy heuristics for minimization of number of terminal nodes in decision trees

This paper describes, in detail, several greedy heuristics for construction of decision trees. We study the number of terminal nodes of decision trees, which is closely related with the cardinality of the set of rules corresponding to the tree. We compare these heuristics empirically for two different types of datasets (datasets acquired from UCI ML Repository and randomly generated data) as well as compare with the optimal results obtained using dynamic programming method.
A Rule Based Model for Efficient Representation and Accurate Recognition of Human Faces

In this paper, we investigate whether the rule based system derived from edges are effective and efficient for face representation and recognition when the number of classes is fixed and known. The characteristics of edges namely straightness and crookedness are used to derive rules. During training, a representative feature value is calculated for each class based on the average percentage of edges that satisfy the rule. In recognition, the representative feature value of an unknown face image is compared against the class representative feature value to assign a label to it. The proposed system does not require extensive feature extraction and classification techniques. Our experimental analysis on the standard CALTECH face databases shows that the edge based system is suitable for real time environment
SAR image classification by image intensity similarity and kernel method

Aiming at the classification problem of Synthetic aperture radar (SAR) images, a classifier based on image intensity and structure is constructed. To overcome the disadvantages of conventional template matching algorithms, similarity between two images is calculated by Hausdorff function, which can handle the distortions and pixel perturbations. The function is then fed into Support vector machines to eventually accomplish the task of image classification. Experiment results corresponding to field and simulated data show that this method characterizes target structure information well.
Analysis and design of an adaptive automated trust negotiation system

Automated trust negotiation (ATN) is used to establish trust between strangers on Internet. We have propose an adaptive automated trust negotiation (AATN) model, which can adjust negotiation strategies and access control policies according to trust evaluation dynamically. In this paper, we analyze and design an AATN system based on AATN model. We improve the adaptive negotiation policy, analyze and design an access control policy language and a consistency checker. The theoretical analysis and test results reveal that the completeness of the consistency checker is good, and AATN system can take both efficiency and security into account by using the adaptive negotiation strategy.
Application of multigrid techniques to image restoration problems

We briefly describe a multigrid strategy for unilevel and two-level linear systems whose coefficient matrix An belongs either to the Toeplitz class or to the cosine algebra of type III and such that An can be naturally associated, in the spectral sense, with a polynomial function f. The interest of the technique is due to its optimal cost of O(N) arithmetic operations, where N is the size of the algebraic problem. We remark that these structures arise in certain 2D image restoration problems or can be used as preconditioners for more complicated image restoration problems.
Lattice Attack on the Knapsack Cipher 0/255

A recently-proposed knapsack public key cryptosystem called knapsack cipher 0/255 is crypt analyzed. It is pointed out that a lattice can be derived from the public key of the knapsack cipher 0/255. The corresponding secret key can be recovered by finding the shortest nonzero vector with lattice basis reduction algorithms. Thus the knapsack cipher 0/255 is insecure.
Exploration vs Exploitation vs Safety: Risk-Aware Multi-Armed Bandits

Motivated by applications in energy management, this paper presents the Multi-Armed Risk-Aware Bandit (MARAB) algorithm. With the goal of limiting the exploration of risky arms, MARAB takes as arm quality its conditional value at risk. When the user-supplied risk level goes to 0, the arm quality tends toward the essential infimum of the arm distribution density, and MARAB tends toward the MIN multi-armed bandit algorithm, aimed at the arm with maximal minimal value. As a first contribution, this paper presents a theoretical analysis of the MIN algorithm under mild assumptions, establishing its robustness comparatively to UCB. The analysis is supported by extensive experimental validation of MIN and MARAB compared to UCB and state-of-art risk-aware MAB algorithms on artificial and real-world problems.
Improving privacy in distributed constraint optimization

Multi-agent systems that work with people to accomplish tasks require access to information that their users consider private. Mechanisms that protect this private information from the other participants and accurate characterizations of the extent to which these mechanisms do so are essential for the adoption of such systems. This thesis examines these issues in the context of algorithms for distributed constraint optimization (DCOP), a prominent technique for multi-agent coordination. Prior research on DCOP algorithms has focused on the tradeoffs between efficiency and optimality and largely ignored privacy questions. To characterize the level of privacy protection in DCOP algorithms, this thesis defines four privacy properties: the Global Loss Property, the Maximum Adversary Property, the Maximum Victim Property and the Cost-For-Loss Property. These properties provide a global view of the amount of private information lost during optimization as well as a more local view of the way that the leakage of private information affects individual participants. The thesis analyzes the extent to which existing metrics assess privacy loss as defined by these properties and introduces new methods for measuring those properties not assessed by existing metrics. An experimental analysis of DCOP algorithms shows that the privacy loss of distributed algorithms varies widely and is affected by a range of design decisions, including the topology the agents use for communication, whether the algorithm is asynchronous, and the computational resources of the participants. The thesis establishes that some distributed algorithms, particularly Adopt and DPOP, outperform centralized algorithms on most privacy properties, but not all. However, for all the algorithms studied, some participants suffer unacceptable levels of privacy loss, indicating a need for algorithms with improved privacy-protection properties. This privacy loss is the result of four identified vulnerabilities: initial, intersection, domain and solution. This thesis presents a new algorithm, SSDPOP, that uses the cryptographic technique of secret sharing to eliminate initial vulnerabily, a major source of privacy loss in DCOP. Overall, SSDPOP significantly reduces both global privacy loss and the maximal privacy loss of any individual agent, while introducing only small computational overhead.
Emergence of Fatal PRRSV Variants: Unparalleled Outbreaks of Atypical PRRS in China and Molecular Dissection of the Unique Hallmark

Porcine reproductive and respiratory syndrome (PRRS) is a severe viral disease in pigs, causing great economic losses worldwide each year. The causative agent of the disease, PRRS virus (PRRSV), is a member of the family Arteriviridae. Here we report our investigation of the unparalleled large-scale outbreaks of an originally unknown, but so-called “high fever” disease in China in 2006 with the essence of PRRS, which spread to more than 10 provinces (autonomous cities or regions) and affected over 2,000,000 pigs with about 400,000 fatal cases. Different from the typical PRRS, numerous adult sows were also infected by the “high fever” disease. This atypical PRRS pandemic was initially identified as a hog cholera-like disease manifesting neurological symptoms (e.g., shivering), high fever (40–42°C), erythematous blanching rash, etc. Autopsies combined with immunological analyses clearly showed that multiple organs were infected by highly pathogenic PRRSVs with severe pathological changes observed. Whole-genome analysis of the isolated viruses revealed that these PRRSV isolates are grouped into Type II and are highly homologous to HB-1, a Chinese strain of PRRSV (96.5% nucleotide identity). More importantly, we observed a unique molecular hallmark in these viral isolates, namely a discontinuous deletion of 30 amino acids in nonstructural protein 2 (NSP2). Taken together, this is the first comprehensive report documenting the 2006 epidemic of atypical PRRS outbreak in China and identifying the 30 amino-acid deletion in NSP2, a novel determining factor for virulence which may be implicated in the high pathogenicity of PRRSV, and will stimulate further study by using the infectious cDNA clone technique.
Efficient distributed algorithms for parallel I/O scheduling

In distributed systems, the lack of global information about data transfer between clients and servers makes implementation of parallel I/O a challenging task. In this paper, we propose two distributed algorithms for scheduling data transfer in parallel I/O with non-uniform data sizes, the maximum-size/maximum-load (MS/ML) algorithm and the minimum-size/earliest-completion-first (MS/ECF) algorithm. Experimental results indicate that both algorithms achieve good performance, compared with the results achieved by their centralized counterparts. Both algorithms yielded parallel performances within 6% of the centralized solutions. We also compare the performance of our algorithms with a distributed highest degree first (HDF) method, which handles non-uniform data transfers by dividing them into units of fixed-sized blocks which are then scheduled and transferred one at a time. Experimental results show that our algorithms require less scheduling and data transfer time, resulting in better overall parallel I/O performance. Our simulations also show that MS/ML is more suitable for parallel I/O with lighter data transfer traffic, while MS/ECF is more suitable for parallel I/O with heavy data transfer traffic.
In Vitro and in Vivo Anti-Inflammatory Activity of the New Glucocorticoid Ciclesonide

The glucocorticoid ciclesonide is the 2′R-epimer of 2′-cyclohexyl-11β-hydroxy-21-isobutyryloxy-16bH-dioxolo[5′,4′:16,17]pregna-1,4-diene-3,20-dione. The active metabolite desisobutyryl-ciclesonide (des-CIC) is derived from ciclesonide by esterase cleavage of isobutyrate at the C21 position. The relative binding affinities at the rat glucocorticoid receptor were dexamethasone, 100; ciclesonide, 12; des-CIC, 1212; and budesonide, 905. Des-CIC potently inhibited the activation of murine and human lymphocytes in a series of different in vitro systems. With the exception of concanavalin A-stimulated rat spleen cells, des-CIC was more potent than the parent compound. Des-CIC compared well with budesonide in all in vitro systems. Furthermore, the respective 2′S-epimers were always significantly less potent than the 2′R-epimers. In vivo, ciclesonide (intratracheal administration), des-CIC, and budesonide inhibited antigen-induced accumulation of eosinophils, protein, and tumor necrosis factor-α into the bronchoalveolar lavage fluid of ovalbumin-sensitized and -challenged Brown Norway rats with an ED50 value ranging from 0.4 to 1.3 mg/kg, indicating similar potency, which suggests in vivo activation of the parent compound. Ciclesonide and budesonide inhibited the bradykinin-induced protein leakage into the rat trachea. In the rat cotton pellet model, ciclesonide inhibited granuloma formation (ED50:= of 2 μg/pellet), whereas budesonide and des-CIC were 15- and 20-fold less active; thymus involution was induced with an ED50 of 303, 279, and 154 μg/pellet, respectively. When applied orally to rats for 28 days, ciclesonide showed low potency in reducing weight of thymus and adrenals, suggesting low oral bioavailability. The in vivo data on ciclesonide highlight its effective local action and a reduced potential for side effects.
Filters and Answers: The University of Iowa TREC-9 Results

The University of Iowa participated in the adaptive filtering and question answering tracks of TREC9. The filtering system used was an extension of the one used in TREC-7 [1] and TREC-8 [2]. Question answering was done using a rule-based system that employed a combination of public domain technologies and the SMART retrieval system.
BioPlan: An API for Classical Planning on BioCrowds

Crowd simulation for evacuation situations often assumes that all agents are trying to reach a single point within an environment. Although such an assumption is not entirely wrong, human agents often exhibit more complex behaviors, even if deviations from the standard behavior are not particularly frequent. Classical planning is far from the best way to achieve the minimal path or correct behavior for agents, but adds a deeper level of reasoning about complex goal-achievement and about actions that are more complex than simply moving about. In this paper, we describe a crowd simulation experiment that uses classical AI planning to enrich the behavior of the agents in the scenario. Using this approach, we can express not only the target destination of the agents, but also (sub)goals and path preferences.
Definability of Languages by Generalized First-Order Formulas over N+

We consider an extension of first-order logic by modular quantifiers of a fixed modulus $q$. Drawing on collapse results from finite model theory and techniques of finite semigroup theory, we show that if the only available numerical predicate is addition, then sentences in this logic cannot define the set of bit strings in which the number of 1's is divisible by a prime $p$ that does not divide $q$. More generally, we completely characterize the regular languages definable in this logic. The corresponding statement, with addition replaced by arbitrary numerical predicates, is equivalent to the conjectured separation of the circuit complexity class $ACC$ from $NC^1$. Thus our theorem can be viewed as proving a highly uniform version of the conjecture.
Cybervictimization and Substance Use Among Adolescents: The Moderation of Perceived Social Support

The purpose of this study was to investigate the buffering effect of perceived social support from parents, teachers, and close friends on the associations among cybervictimization and alcohol use, marijuana use, cigarette smoking, and nonmarijuana illicit drug use over 3 years. Participants were 867 8th graders from the Midwestern United States (13–15 years old; 51% female). The findings revealed positive associations between cybervictimization and alcohol abuse as well as nonmarijuana drug use, with high and low levels of social support moderating these relationships. Implications for social workers and prevention and intervention efforts are also discussed.
Multi-objective Optimization Based Virtual Resource Allocation Strategy for Cloud Computing

The increasing requirements on cloud computing entail building up large numbers of large-scale data centers which require a surprising amount of energy. With the gradual depletion and price escalation of traditional energy, operating data center in an energy efficient way is an emerging urgent problem. However, most existing researches of resource allocation in datacenter did not take into full consideration how to decrease energy consumption. In this paper, we formulate the energy efficiency virtual resource allocation for cloud computing as a multi-objective optimization problem, which is then solved by intelligent optimization algorithm. The simulation results reveal that the strategy can successfully generate schedule scheme of different numbers of servers-VMs with diverse characteristics in a reasonable time period and decrease the total operating energy of data center effectively.
Support for Collaboration between Large and Small & Medium Enterprises

Collaboration between large and small & medium enterprises is still not adequately supported by current groupware solutions. In this paper, we present the VENIS approach for lightweight collaboration that addresses the challenges of inter-enterprise collaboration. Key elements of our approach are interoperability to legacy applications, basic services for sharing and management of shared collaboration spaces, use of email for collaboration on the SME's side and the application of lightweight semantic technologies to enable semantic search in inter-enterprise collaborations. We tested the approach on a use case from software development and describe the results.
ph / 0 40 60 09 v 3 4 O ct 2 00 4 Covariant gauge-natural conservation laws

When a gauge-natural invariant variational principle is assigned, to determine canonical covariant conservation laws, the vertical part of gauge-natural lifts of infinitesimal principal automorphisms – defining infinites-imal variations of sections of gauge-natural bundles – must satisfy generalized Jacobi equations for the gauge-natural invariant Lagrangian. Vice versa all vertical parts of gauge-natural lifts of infinitesimal principal au-tomorphisms which are in the kernel of generalized Jacobi morphisms are generators of canonical covariant currents and superpotentials. In particular, only a few gauge-natural lifts can be considered as canonical generators of covariant gauge-natural physical charges.
Decentralized active gate control for current balancing of parallel connected IGBT modules

In modern power converters operating at currents of few kA and voltages of several kV, Insulated Gate Bipolar Transistor (IGBT) modules are typically used. The increased power demand of modern inverters and rectifiers results in higher currents and voltages. Currents up to tens of kA and voltages up to tens of kV are required, which exceed the ratings of IGBT semiconductors currently available. Multiple IGBT modules can be connected in parallel or in series to provide the requested current or voltage rating. Depending on the interconnection of the switches, parameter variations in the semiconductors and in the system, as well as tolerances and delay times in the gate driving circuits, an unbalanced current or voltage distribution may occur. Therefore, the IGBT modules are generally derated, which results in an increased number of devices and volume being required. In this paper, a modular concept of a decentralized active gate control for current balancing of parallel connected IGBT modules is presented. It operates distributed to the gate drive units (GDU), the hardware and software configurations are independent of the system design and no restrictions on the number of parallel connected IGBT modules exist.
Hybrid concurrency control for abstract data types

We define a new locking protocol that permits more concurrency than existing commutativity-based protocols. The protocol uses timestamps generated when transactions commit to provide more information about the serialization order of transactions, and hence to weaken the constraints on conflicts. In addition, the protocol permits operations to be both partial and non-deterministic, and it permits results of operations to be used in choosing locks. The protocol exploits type-specific properties of objects, necessary and sufficient constraints on lock conflicts are defined directly from a data type specification. We give a complete formal description of the protocol, encompassing both concurrency control and recovery, and prove that the protocol satisfies hybrid atomicity, a local atomicity property that combines aspects of static and dynamic atomic protocols. We also show that the protocol is optimal in the sense that no hybrid atomic locking scheme can permit more concurrency.