Miracle-dz/UniFAR-FTUs huggingface dataset:
Gene Transfer of Hepatocyte Growth Factor to Subarachnoid Space in Cerebral Hypoperfusion Model

Although cerebral hypoperfusion caused by cerebral occlusive disease leads to cerebral ischemic events, an effective treatment has not yet been established. Recently, a novel therapeutic strategy for ischemic disease using angiogenic growth factors to expedite and/or augment collateral artery development has been proposed. Therapeutic angiogenesis might be useful for the treatment of cerebral occlusive disease. Hepatocyte growth factor (HGF) is a potent angiogenic factor, in addition to vascular endothelial growth factor (VEGF), whereas in the nervous system HGF also acts as neurotrophic factor. Therefore, we hypothesized that gene transfer of these angiogenic growth factors could induce angiogenesis, thus providing an effective therapy for cerebral hypoperfusion or stroke. In this study, we employed a highly efficient gene transfer method, the viral envelop (Hemagglutinating Virus of Japan [HVJ]-liposome) method, because we previously documented that &bgr;-galactosidase gene could be transfected into the brain by the HVJ-liposome method. Indeed, we confirmed wide distribution of transgene expression using &bgr;-galactosidase via injection into the subarachnoid space. Of importance, transfection of HGF or VEGF gene into the subarachnoid space 7 days before occlusion induced angiogenesis on the brain surface as assessed by alkaline phosphatase staining (P <0.01). In addition, significant improvement of cerebral blood flow (CBF) was observed by laser Doppler imaging (LDI) 7 days after occlusion (P <0.01). Unexpectedly, transfection of HGF or VEGF gene into the subarachnoid space immediately after occlusion of the bilateral carotid arteries also induced angiogenesis on the brain surface and had a significant protective effect on the impairment of CBF by carotid occlusion (P <0.01). Interestingly, coinjection of recombinant HGF with HGF gene transfer revealed a further increase in CBF (P <0.01). Here, we demonstrated successful therapeutic angiogenesis using HGF or VEGF gene transfer into the subarachnoid space to improve cerebral hypoperfusion, thus providing a new therapeutic strategy for cerebral ischemic disease.
Capacity-approaching codes on the q-ary symmetric channel for large q

We give a short survey of several techniques to construct codes on GF(q) that approach the capacity of the q-ary symmetric channel. The q-ary symmetric channel represents the next level of difficulty after the binary erasure channel (BEC). Since the channel is more complex than the BEC, one may hope that codes and decoding algorithms that approach the capacity of this channel for large q may be modified to yield capacity-approaching codes for smaller values of q as well.
VELIKIH PET PLUS DVA: VALIDACIJA SKRAĆENE VERZIJE

Osnovni cilj istraživanja je konstrukcija skracene verzije upitnika Velikih pet plus dva (VP+2-70). Na osnovu korelacija stavki integralne verzije VP+2 (184 stavke) sa glavnim komponentama skala viseg reda, formirano je sedam 10-ajtemskih skala. Konstrukcija instrumenta VP+2-70 sprovedena je na kalibracionom uzorku od 2924 ispitanika (56% žena), starosti 18‒68 godina, a stabilnost faktorske strukture proverena je na validacionom uzorku od 802 ispitanika (60% žena), starosti 18‒60 godina. Na ovim uzorcima proverena je i konvergentna validnost upitnika kroz ispitivanje povezanosti njegovih skala sa skalama Inventara velikih pet. Na uzorku od 374 studenta, starosti 19‒33 godine, ispitana je povezanost skorova skraceneverzije VP+2 u domenu samoprocene i procene od strane drugih. Kriterijska validnost ispitana je utvrđivanjem relacija skorova na skalama VP+2-70 i indikatora ponasanja obuhvacenih Upitnikom za procenu ponasanja (The Behavior Report Form ‒BRF), na uzorku od 205 studenata starosti 18‒19 godina. Rezultati ukazuju na zadovoljavajucu pouzdanost, konvergentnu i prediktivnu validnost upitnika VP+2-70.
A survey and taxonomy of various packet classification algorithms

Routers can also function as firewalls and perform various operations on the incoming and outgoing packets. In case when all the packets share common header characteristics, it is termed as a packet flow. In order to classify a packet, routers perform a lookup on a classifier table using one or more fields from the packet header to classify the packet into its corresponding flow. A classifier is a set of rules which identify each flow and the appropriate actions to be taken for any packet belonging to that flow. The paper examines the problem of packet classification and various proposed techniques for the same.
Cognitive impairment in euthymic bipolar patients with and without prior alcohol dependence. A preliminary study.

BACKGROUND Few studies of the neurocognitive performance of patients with bipolar disorder have been performed while patients are in the euthymic state. METHODS Twenty-five euthymic bipolar patients (12 with and 13 without a history of alcohol dependence) were compared with 22 normal control subjects on a neuropsychological test battery assessing a range of cognitive domains. The relationship between subjects' neurocognitive performance and the course-of-illness variables (lifetime episodes and duration of mania, depression, or both), as well as current lithium level, was determined. RESULTS The results indicated differences across the groups, with the bipolar patients with and without alcohol dependence performing more poorly than controls on tests of verbal memory. Furthermore, bipolar subjects with a history of alcohol dependence had additional decrements in executive (i.e., frontal lobe) functions when compared with controls. For subjects in the bipolar group, lifetime months of mania and depression were negatively correlated with performance in verbal memory and several executive function measures. CONCLUSIONS Our findings support the presence of persistent neurocognitive difficulties in patients with long-standing bipolar disorder who are not in the psychiatrically acute state or who are suffering the effects of alcohol abuse and suggest that there may be an aggregate negative effect of lifetime duration of bipolar illness on memory and frontal or executive systems.
A novel method of surface-normal measurement in robotic drilling for aircraft fuselage using three laser range sensors

Surface-normal measurement is an essential part in robotic drilling system. A novel method to measure the surface-normal of aircraft skin is proposed in this paper. Resumptively, three laser range sensors evenly distributed around the drill bit are used to measure the distance from them to aircraft skin within a small area. So the coordinates of three measured points can be obtained, and the normal vector is calculated from the cross product of any two vectors that are tangent to the surface at the drilling point. The simulation results and experiment conducted on an aeronautical drilling robot validate the precision and efficiency improvement of the drilling. The presented method is appropriate for intelligent robotic drilling for aircraft fuselage.
Pyrethroid-Resistance and Presence of Two Knockdown Resistance (kdr) Mutations, F1534C and a Novel Mutation T1520I, in Indian Aedes aegypti

Background Control of Aedes aegypti, the mosquito vector of dengue, chikungunya and yellow fever, is a challenging task. Pyrethroid insecticides have emerged as a preferred choice for vector control but are threatened by the emergence of resistance. The present study reports a focus of pyrethroid resistance and presence of two kdr mutations—F1534C and a novel mutation T1520I, in Ae. aegypti from Delhi, India. Methodology/Principal Findings Insecticide susceptibility status of adult-female Ae. aegypti against DDT (4%), deltamethrin (0.05%) and permethrin (0.75%) was determined using WHO's standard insecticide susceptibility kit, which revealed resistance to DDT, deltamethrin and permethrin with corrected mortalities of 35%, 72% and 76% respectively. Mosquitoes were screened for the presence of kdr mutations including those reported earlier (I1011V/M, V1016G/I, F1534C, D1794Y and S989P), which revealed the presence of F1534C and a novel mutation T1520I. Highly specific PCR-RFLP assays were developed for genotyping of these two mutations. Genotyping using allele specific PCR and new PCR-RFLP assays revealed a high frequency of F1534C (0.41–0.79) and low frequency of novel mutation T1520I (0.13). The latter was observed to be tightly linked with F1534C and possibly serve as a compensatory mutation. A positive association of F1534C mutation with DDT and deltamethrin resistance in Ae. aegypti was established. However, F1534C-kdr did not show significant protection against permethrin. Conclusions/Significance The Aedes aegypti population of Delhi is resistant to DDT, deltamethrin and permethrin. Two kdr mutations, F1534C and a novel mutation T1520I, were identified in this population. This is the first report of kdr mutations being present in the Indian Ae. aegypti population. Highly specific PCR-RFLP assays were developed for discrimination of alleles at both kdr loci. A positive association of F1534C mutation with DDT and deltamethrin resistance was confirmed.
A Characterization of Ten Hidden-Surface Algorithms

Abstract : The paper asserts that the hidden-surface problem is mainly one of sorting. The various surfaces of an object to be shown in hidden-surface or hidden-line form must be sorted to find out which ones are visible at various places on the screen. Surfaces may be sorted by lateral position in the picture (XY), by depth (Z), or by other criteria. The paper shows that the order of sorting and the types of sorting used form differences among the existing hidden-surface algorithms. (Modified author abstract)
A Loss Tomography Algorithm in Wireless Sensor Networks Using Gibbs Sampling

End-to-end application data in wireless sensor networks can be a valuable health indicator, if they can be used for network measurement purposes. This paper therefore applies network tomography technology to identify lossy nodes using end-to-end application traffic. Based on the path information piggybacked by data packets and the end-to-end performance observations, the problem of lossy nodes inference is modeled as a Bayesian inference problem and a Markov Chain Monte Carlo (MCMC) algorithm using Gibbs sampling was proposed. The algorithm is evaluated via simulation and achieves high detection and low false positive rates.
Sleeping Sickness Elimination in Sight: Time to Celebrate and Reflect, but Not Relax

Sleeping sickness, also known as human African trypanosomiasis (HAT), is one of the neglected tropical diseases of sub-Saharan Africa that has plagued human health and agricultural development. In West Africa, infection with Trypanosoma brucei gambiense gives rise to a chronic disease that mainly affects humans. Infection with T. b. gambiense accounts for more than 90% of reported sleeping sickness cases. In East Africa, infection with Trypanosoma brucei rhodesiense generates acute disease in humans, and also circulates in a relatively unaffected livestock reservoir. Both Gambian and Rhodesian sleeping sickness are fatal if left untreated. The parasites are transmitted to the mammalian host through the bite of an infected tsetse fly.
IMPROVING STATOR WINDING FAULT DIAGNOSIS IN INDUCTION MOTOR USING DEMPSTER -SHAFER THEORY

Due to the important role of induction motors in industry, reliability in fault detection of these motors is of great importance. In this paper, a method is proposed for increasing the reliability of stator winding fault diagnosis using Dempster-Shafer theory. Out of the measured current and vibration signals, some features are extracted and selected according to the method, and a neural network based on DempsterShafer theory is trained by the selected features. The proposed method is then tested for various fault. Finally it is shown how data fusion can increase the accuracy of fault diagnosis. The system performance is also evaluated on a laboratory motor showing satisfactory results.
Fixed channel assignment in cellular radio networks using a modified genetic algorithm

With the limited frequency spectrum and an increasing demand for cellular communication services, the problem of channel assignment becomes increasingly important. However, finding a conflict-free channel assignment with the minimum channel span is NP hard. Therefore, we formulate the problem by assuming a given channel span. Our objective is to obtain a conflict-free channel assignment among the cells, which satisfies both the electromagnetic compatibility (EMC) constraints and traffic demand requirements. We propose an approach based on a modified genetic algorithm (GA). The approach consists of a genetic-fix algorithm that generates and manipulates individuals with fixed size (i.e., in binary representation, the number of ones is fixed) and a minimum-separation encoding scheme that eliminates redundant zeros in the solution representation. Using these two strategies, the search space can be reduced substantially. Simulations on the first four benchmark problems showed that this algorithm could achieve at least 80%, if not 100%, convergence to solutions within reasonable time. In the fifth benchmark problem, our algorithm found better solutions with shorter channel span than any existing algorithms. Such significant results indicate that our approach is indeed a good method for solving the channel-assignment problem.
Neural implementation of musical expertise and cognitive transfers: could they be promising in the framework of normal cognitive aging?

Brain plasticity allows the central nervous system of a given organism to cope with environmental demands. Therefore, the quality of mental processes relies partly on the interaction between the brain’s physiological maturation and individual daily experiences. In this review, we focus on the neural implementation of musical expertise at both an anatomical and a functional level. We then discuss how this neural implementation can explain transfers from musical learning to a broad range of non-musical cognitive functions, including language, especially during child development. Finally, given that brain plasticity is still present in aging, we gather arguments to propose that musical practice could be a good environmental enrichment to promote cerebral and cognitive reserves, thereby reducing the deleterious effect of aging on cognitive functions.
The Exbed Project -some Experiences

We report on experiences of the ExBed project, speciically on adding a rule based expression mechanism to an existing procedural programming language (C++) and on designing and implementing a self-contained language (XE) { and its programming environment { supporting similar but more general capabilities. 1. Background Embedded systems are claimed to be a promising industrial AI application. The ExBed project (expert system framework for embedded applications) was established to determine how expert system techniques can be applied within embedded systems running on a microcomputer. Fagan (1980) was the rst to attempt the construction of a continuously operating expert system. Based on his experiences the forward chaining inference strategy is usually assumed most suitable for these systems. The state-of-the-art of the OPS production system family, based on the RETE algorithm (Forgy 1982), is represented by OPS83 (Forgy 1984). YES/L1 (Milliken 1985) is a rule-based extension of PL/1. Hexscon (Wright 1986) and Escort (Sachs 1986) are more rigid languages than what we aim for, while PICON (Moore 1985) is large and specialized to continuous processes. Stimulus (Robertson 1985) has many objectives in common with us. See (Laaey et al. 1988) for a recent survey on on real-time knowledge-based systems. According to the charter of ExBed the constructor of embedded systems was to be provided with a rule-based expression mechanism and the above references lead to the choice of the forward chaining inference strategy. It was decided to approach the problem, not by using or designing an "expert system shell", but by embedding the 1
An adaptive subspace based multichannel equalization algorithm for room acoustics equalization with sparseness constraint

Multichannel equalization (MCEQ) techniques are employed in applications such as speech dereverberation to mitigate channel effects. In this paper, we propose a subspace-based adaptive MCEQ algorithm with the aim of addressing the problem of high computational complexity involved in the multiple input/output inverse theorem based non-adaptive MCEQ algorithms. In the proposed algorithm, equalization is achieved using multiple sets of inverse filters such that each set of filters corresponds to each subspace and these filters are adaptively estimated using the least-mean-square type algorithm. In order to improve the convergence performance, the sparseness of the estimated Kronecker delta function is used as a constraint to the adaptive algorithm. Simulation and experimental results show that the proposed adaptive algorithms yield better equalization with improved convergence performance.
Proving security protocols correct

Security protocols use cryptography to set up private communication channels on an insecure network. Many protocols contain flaws, and because security goals are seldom specified in detail, we cannot be certain what constitutes a flaw. Thanks to recent work by a number of researchers, security protocols can now be analyzed formally. The paper outlines the problem area, emphasizing the notion of freshness. It describes how a protocol can be specified using operational semantics and properties proved by rule induction, with machine support from the proof tool Isabelle. The main example compares two versions of the Yahalom protocol. Unless the model of the environment is sufficiently detailed, it cannot distinguish the correct protocol from a flawed version. The paper attempts to draw some general lessons on the use of formalisms. Compared with model checking, the inductive method performs a finer analysis, but the cost of using it is greater.
Dense Subgraph Discovery: KDD 2015 tutorial

Finding dense subgraphs is a fundamental graph-theoretic problem, that lies in the heart of numerous graph-mining applications, ranging from finding communities in social networks, to detecting regulatory motifs in DNA, and to identifying real-time stories in news. The problem of finding dense subgraphs has been studied extensively in theoretical computer science, and recently, due to the relevance of the problem in real-world applications, it has attracted considerable attention in the data-mining community. In this tutorial we aim to provide a comprehensive overview of (i) major algorithmic techniques for finding dense subgraphs in large graphs and (ii) graph mining applications that rely on dense subgraph extraction. We will present fundamental concepts and algorithms that date back to 80's, as well as the latest advances in the area, from theoretical and from practical point-of-view. We will motivate the problem of finding dense subgraphs by discussing how it can be used in real-world applications. We will discuss different density definitions and the complexity of the corresponding optimization problems. We will also present efficient algorithms for different density measures and under different computational models. Specifically, we will focus on scalable streaming, distributed and MapReduce algorithms. Finally we will discuss problem variants, extensions, and will provide pointers for future research directions.
Usage of Traditional and Complementary Medicine ( T & CM ) : Prevalence , Practice and Perception among Post Stroke Patients Attending Conventional Stroke Rehabilitation in A Teaching Hospital in Malaysia

Med J Malaysia Vol 70 No 1 February 2015 SUMMARY Introduction: The lack of evidence that proves the benefit of traditional and complementary medicines (T&CM) in treating chronic medical conditions does not deter its usage among patients worldwide. Prevalence of usage among post-stroke patients in Malaysia especially is unknown. This study aims to determine the prevalence, practice and perception of T&CM use among stroke survivors attending an outpatient rehabilitation program in a teaching hospital.
Through wall imaging: Historical perspective and future directions

Through wall imaging is highly desirable for police, fire and rescue, first responder, and military applications. The ultimate desire of such system is to provide detailed information in areas that cannot be seen using conventional measures. Borrowing from successes in geological and medical imaging environments, researchers are applying radio frequency (RF) and other sensing modes to penetrate wall materials and make intelligent decisions about the contents of rooms and buildings. For this application, they are many propagation differences that provide unique challenges that must be addressed to make through wall penetration sensors operationally viable. This paper outlines the historical context of early research as well as providing new directions for future research in this exciting interplay between electromagnetic propagation, signal processing, and knowledge-based reasoning algorithms.
Is Priming in Fragment Completion Based on a " Traceless " Memory System ?

Four experiments investigated the relation between successive tests of primed fragment completion with the use of the same or different fragments on successive tests. The results were contrasted with successive tests of fragment cued recall using identical study/test stimuli and differing from the fragment completion task only in terms of instructions. Performance on successive tests of primed fragment completion was dependent if the same fragments were represented on both tests. There was no reliable dependence when different fragments were used on the two fragment completion tests. In contrast, when fragment cued recall instructions were given, successive tests with both same and different fragments yielded clear dependence. The results are interpreted as evidence that performance in the fragment completion task does not depend upon a specific cognitive unit or memory trace, whereas in episodic cued recall it does. This suggestion is elaborated in terms of an integration of the transfer-appropriate procedural approach with the idea of a traceless memory system that supports priming in tasks such as fragment completion.