Miracle-dz/UniFAR-FTUs huggingface dataset:
Na+-K+-Cl− Cotransporter in Rat Focal Cerebral Ischemia

In cultured neurons, the authors previously demonstrated that the Na+-K+-Cl− cotransporter is significantly stimulated by elevated extracellular potassium and glutamate, which are important factors in cerebral ischemic damage. These findings led the authors to hypothesize that stimulation of the cotransporter after ischemia might result in Na+, K+, and Cl− influx, and might contribute to neuron damage. In the current study, the authors investigated such a role of the Na+-K+-Cl− cotransporter in focal cerebral ischemia. Cerebral ischemia was induced by 2-hour occlusion of the left middle cerebral artery (MCA) and 24-hour reperfusion in male spontaneously hypertensive rats (SHRs). Immunocytochemical staining and immunoblotting revealed an up-regulation of expression of the cotransporter protein in neurons in cortex at 24 hours of reperfusion. Artificial cerebral spinal fluid (aCSF) or 100 μmol/L bumetanide (a cotransporter inhibitor) in aCSF were continuously microdialyzed through a microdialysis probe into left cortices throughout 2-hour MCA occlusion and 24-hour reperfusion. Compared with the aCSF-treated group, infarction volume was significantly reduced in the bumetanide-treated group (25%, P < 0.05). In addition, brain water content in the bumetanide-treated brains was decreased by 70% (P < 0.05). These results strongly suggest that the Na+-K+-Cl− cotransporter may play an important role in cerebral ischemic neuronal damage.
Phylogenetic Identification and Functional Characterization of Orthologs and Paralogs across Human, Mouse, Fly, and Worm

Model organisms can serve the biological and medical community by enabling the study of conserved gene families and pathways in experimentally-tractable systems. Their use, however, hinges on the ability to reliably identify evolutionary orthologs and paralogs with high accuracy, which can be a great challenge at both small and large evolutionary distances. Here, we present a phylogenomics-based approach for the identification of orthologous and paralogous genes in human, mouse, fly, and worm, which forms the foundation of the comparative analyses of the modENCODE and mouse ENCODE projects. We study a median of 16,101 genes across 2 mammalian genomes (human, mouse), 12 Drosophila genomes, 5 Caenorhabditis genomes, and an outgroup yeast genome, and demonstrate that accurate inference of evolutionary relationships and events across these species must account for frequent gene-tree topology errors due to both incomplete lineage sorting and insufficient phylogenetic signal. Furthermore, we show that integration of two separate phylogenomic pipelines yields increased accuracy, suggesting that their sources of error are independent, and finally, we leverage the resulting annotation of homologous genes to study the functional impact of gene duplication and loss in the context of rich gene expression and functional genomic datasets of the modENCODE, mouse ENCODE, and human ENCODE projects.
Rhyme and Style Features for Musical Genre Classification by Song Lyrics

How individuals perceive music is influenced by many different factors. The audible part of a piece of music, its sound, does for sure contribute, but is only one aspect to be taken into account. Cultural information influences how we experience music, as does the songs’ text and its sound. Next to symbolic and audio based music information retrieval, which focus on the sound of music, song lyrics, may thus be used to improve classification or similarity ranking of music. Song lyrics exhibit specific properties different from traditional text documents – many lyrics are for example composed in rhyming verses, and may have different frequencies for certain parts-of-speech when compared to other text documents. Further, lyrics may use ‘slang’ language or differ greatly in the length and complexity of the language used, which can be measured by some statistical features such as word / verse length, and the amount of repetative text. In this paper, we present a novel set of features developed for textual analysis of song lyrics, and combine them with and compare them to classical bag-of-words indexing approaches. We present results for musical genre classification on a test collection in order to demonstrate our analysis.
Semantic Modeling of Digital Multimedia

The requirement for a commonly accepted efficient mapping between multimedia metadata standards and semantic web-ontology standards is a major issue recognized by semantic multimedia research community. Though there have been several attempts to translate MPEG-7 audio descriptions to ontology languages there is very little literature that addresses issues associated with streaming video contents. In this paper we outline our plan to develop a methodology and the corresponding software implementation of mapping techniques of MPEG21 video items. The novelty of our effort lies in the fact that we address the complexity of video content’s metadata descriptions and its integration with the well recognized ontological standard through transparent mapping from original XML to RDF description. The validity of the proposed method and implementation detail will be verified against the MOSAICA semantic framework and its use cases.
New evaluation method to detect physiological stress in fruit trees by airborne hyperspectral image spectroscopy

Nowadays airborne remote sensing data are increasingly used in precision agriculture. The fast space-time dependent localization of stresses in orchards, which allows for a more efficient application of horticultural technologies, could lead to improved sustainable precise management. The disadvantage of the near field multi and hyper spectroscopy is the spot sample taking, which can apply independently only for experimental survey in plantations. The traditional satellite images is optionally suitable for precision investigation because of the low spectral and ground resolution on field condition. The presented airborne hyperspectral image spectroscopy reduces above mentioned disadvantages and at the same time provides newer analyzing possibility to the user. In this paper we demonstrate the conditions of data base collection and some informative examination possibility. The estimating of the board band vegetation indices calculated from reflectance is well known in practice of the biomass stress examinations. In this method the N-dimension spectral data cube enables to calculate numerous special narrow band indexes and to evaluate maps. This paper aims at investigating the applied hyperspectral analysis for fruit tree stress detection. In our study, hyperspectral data were collected by an AISA DUAL hyperspectral image spectroscopy system, with high (0,5-1,5 m) ground resolution. The research focused on determining of leaves condition in different fruit plantations in the peach orchard near Siófok. Moreover the spectral reflectance analyses could provide more information about plant condition due to changes in the absorption of incident light in the visible and near infrared range of the spectrum. International Journal of Horticultural Science 2010, 16 (1): 37–40. Agroinform Publishing House, Budapest, Printed in Hungary ISSN 1585-0404
A Universal Decline Law of Classical Novae

We calculate many different nova light curves for a variety of white dwarf masses and chemical compositions, with the assumption that free-free emission from optically thin ejecta dominates the continuum flux. We show that all these light curves are homologous and a universal law can be derived by introducing a "time scaling factor. " The template light curve for the universal law has a slope of flux F ∝ t-1.75 in the middle part (from ~2 to ~6 mag below the optical maximum), but it declines more steeply, F ∝ t-3.5, in the later part (from ~6 to ~10 mag), where t is the time from the outburst in units of days. This break on the light curve is due to a quick decrease in the wind mass-loss rate. The nova evolutions are approximately scaled by the time of break. Once the time of break is observationally determined, we can derive the period of a UV burst phase, the duration of optically thick wind phase, and the turnoff date of hydrogen shell-burning. An empirical observational formula, t3 = (1.68 ± 0.08) t2 + (1.9 ± 1.5) days, is derived from the relation of F ∝ t-1.75, where t2 and t3 are the times in days during which a nova decays by 2 and 3 mag from the optical maximum, respectively. We have applied our template light curve model to the three well-observed novae, V1500 Cyg, V1668 Cyg, and V1974 Cyg. Our theoretical light curves show excellent agreement with the optical y and infrared J, H, K light curves. The continuum UV 1455 Å light curves observed with IUE are well reproduced. The turn-on and turn-off of supersoft X-ray observed with ROSAT are also explained simultaneously by our model. The WD mass is estimated, from the light curve fitting, to be MWD ≈ 1.15 M☉ for V1500 Cyg, MWD ≈ 0.95 M☉ for V1668 Cyg, and MWD ≈ 0.95-1.05 M☉ for V1974 Cyg, together with the appropriate chemical compositions of the ejecta.
Energy-Efficient Scheduling for Hybrid Tasks in Control Devices for the Internet of Things

In control devices for the Internet of Things (IoT), energy is one of the critical restriction factors. Dynamic voltage scaling (DVS) has been proved to be an effective method for reducing the energy consumption of processors. This paper proposes an energy-efficient scheduling algorithm for IoT control devices with hard real-time control tasks (HRCTs) and soft real-time tasks (SRTs). The main contribution of this paper includes two parts. First, it builds the Hybrid tasks with multi-subtasks of different function Weight (HoW) task model for IoT control devices. HoW describes the structure of HRCTs and SRTs, and their properties, e.g., deadlines, execution time, preemption properties, and energy-saving goals, etc. Second, it presents the Hybrid Tasks' Dynamic Voltage Scaling (HTDVS) algorithm. HTDVS first sets the slowdown factors of subtasks while meeting the different real-time requirements of HRCTs and SRTs, and then dynamically reclaims, reserves, and reuses the slack time of the subtasks to meet their ideal energy-saving goals. Experimental results show HTDVS can reduce energy consumption about 10%–80% while meeting the real-time requirements of HRCTs, HRCTs help to reduce the deadline miss ratio (DMR) of systems, and HTDVS has comparable performance with the greedy algorithm and is more favorable to keep the subtasks' ideal speeds.
6.2.2 Relative Cost

Thus, using (8) we receive that the competitive ratio c according to the absolute cost C is c = C(online) C(offline) = C on C off < 2 (9) Therefore, from (4) and (9) we receive that the competitive ratio of the online algorithm is c = min(1 + y 2d ; 2) 7 Discussion In this paper we have analyzed the problem of placing an application in a very large distributed system in a good location with high probability, using a small number of steps. We have shown an algorithm that achieves this goal, proved its behavior for a single application and simulated it for multiple applications, and showed that it exhibits a good competitive ratio. Also, we have presented a scalable two-step protocol for placing virtual servers near their clients, which is based on the application placement algorithms we discussed. Yet, there are some other aspects of this problem that are not addressed by this work. For example, a heuristic for making smart choices regarding when a process should move and when it should stay even if it found a better place might improve the average overall cost of an application. That is, even though we have shown a good competitive ratio, this measure only states that in the worst case our protocol is at most twice as bad as an oo-line optimal algorithm. However, it says nothing about the average or common behavior. Another issue is analyzing or simulating a situation in which multiple applications are allowed to simultaneously check the same location for its goodness number. Also, simulating the case when there are diierent weights to diierent applications, and providing a solution to the problem associated with it as presented in Section 4.1 is desirable. This includes combining our protocol with methods that can exchange two or more applications simultaneously , even if neither one would beneet if it where to move by itself. The challenge is to do so in a scalable way. Presumably, it will also have to rely on probabilistic methods. Finally, it would be highly valuable to integrate our protocol from Section 5 into symphony 7], and measure how well it works in a full scale system. Acknowledgements: We would like to thank Hadas Shachnai for helping us with the nal competitive analysis results. that the online and the optimal oo-line algorithms pay for this sequence of j steps is C 0 …
Phylodynamics and Human-Mediated Dispersal of a Zoonotic Virus

Understanding the role of humans in the dispersal of predominately animal pathogens is essential for their control. We used newly developed Bayesian phylogeographic methods to unravel the dynamics and determinants of the spread of dog rabies virus (RABV) in North Africa. Each of the countries studied exhibited largely disconnected spatial dynamics with major geo-political boundaries acting as barriers to gene flow. Road distances proved to be better predictors of the movement of dog RABV than accessibility or raw geographical distance, with occasional long distance and rapid spread within each of these countries. Using simulations that bridge phylodynamics and spatial epidemiology, we demonstrate that the contemporary viral distribution extends beyond that expected for RABV transmission in African dog populations. These results are strongly supportive of human-mediated dispersal, and demonstrate how an integrated phylogeographic approach will turn viral genetic data into a powerful asset for characterizing, predicting, and potentially controlling the spatial spread of pathogens.
The Relevance of Value Net Integrator and Shared Infrastructure Business Models in Managing Chronic Conditions

There is widespread support for chronic condition management (CCM) programs that require a multi-disciplinary, care-team approach. Implementation of such programs represents a paradigm shift in primary care service delivery and has significant resource implications for the general practice. Integral to the widespread uptake of care-team based CCM is information collection, storage and dissemination amongst the care-team members. This paper looks to ebusiness models for assistance in understanding the requirements of general practitioners (GPs) in providing multi-disciplinary team care to patients with chronic conditions. The role required of GPs in chronic condition management is compared to that of a value net integrator. The essential characteristics of value net integrators are identified and compared to those of GPs providing multi-disciplinary team care to patients with chronic conditions. It is further suggested that a shared infrastructure is required.
Comparing Human and Automated Evaluation of Open-Ended Student Responses to Questions of Evolution

Written responses can provide a wealth of data in understanding student reasoning on a topic. Yet they are time- and labor-intensive to score, requiring many instructors to forego them except as limited parts of summative assessments at the end of a unit or course. Recent developments in Machine Learning (ML) have produced computational methods of scoring written responses for the presence or absence of specific concepts. Here, we compare the scores from one particular ML program -- EvoGrader -- to human scoring of responses to structurally- and content-similar questions that are distinct from the ones the program was trained on. We find that there is substantial inter-rater reliability between the human and ML scoring. However, sufficient systematic differences remain between the human and ML scoring that we advise only using the ML scoring for formative, rather than summative, assessment of student reasoning.
Practical decoy state for quantum key distribution (15 pages)

Decoy states have recently been proposed as a useful method for substantially improving the performance of quantum key distribution (QKD). Here, we present a general theory of the decoy state protocol based on only two decoy states and one signal state. We perform optimization on the choice of intensities of the two decoy states and the signal state. Our result shows that a decoy state protocol with only two types of decoy states---the vacuum and a weak decoy state---asymptotically approaches the theoretical limit of the most general type of decoy state protocol (with an infinite number of decoy states). We also present a one-decoy-state protocol. Moreover, we provide estimations on the effects of statistical fluctuations and suggest that, even for long-distance (larger than 100 km) QKD, our two-decoy-state protocol can be implemented with only a few hours of experimental data. In conclusion, decoy state quantum key distribution is highly practical.
Adaptive Kalman Filtering Method to the Data Processing of GPS Deformation Monitoring

This paper introduced the principle of Kalman filtering and modeling methods, however, there existed some problems with the standard Kalman filtering. Combined with the characteristics of GPS deformation monitoring data, this paper improved the algorithm of the standard Kalman filtering and proposed the Adaptive Kalman filtering method. The authors took the data of GPS deformation monitoring as an example, carried out AKF method in the VB platform, and compared the treatment results with the original data. The results show that the AKF can effectively suppress the phenomenon of divergence emerged filtering with the systematic statistical properties of real-time dynamic estimation and make the results more stable and reasonable. The results show that the Adaptive Kalman Filter proposed in this paper is more effective than the traditional methods.
Evaluating the potential of Bayesian networks for desertification assessment in arid areas of Iran

Spatiotemporal complexities increase the uncertainty of the results of various desertification assessment models. We integrated the Mediterranean desertification and land‐use (MEDALUS) model with Bayesian networks (BNs) to develop a novel method for desertification assessment in central Iran. We first derived the current desertification status of the study area from empirical observations of actual, current desertification conditions and mapped the obtained status based on the criteria of the MEDALUS model. We then utilized BNs to produce three separate causal models for the soil, groundwater, and wind erosion indicators of the MEDALUS model. We then integrated all the related indicators and indices into a desertification assessment BNs model. After the reevaluation and sensitivity analysis of the model, we compared its results with those of the MEDALUS model. The obtained results highlighted soil condition and wind erosion as the main determinants of desertification in the study area. Considering the ability of BNs to accommodate uncertainty in the assessment process, these models can assist relevant authorities in making informed decisions. They can also be applied as decision support tools for adaptive management in fragile ecosystems of arid areas.
Automatic SAT-Compilation of Planning Problems

Recent work by Kautz et al., provides tantalizing evidence that large, classical planning problems may be efficiently solved by translating them into propositional satisfiability problems, using stochastic search techniques, and translating the resulting truth assignments back into plans for the original problems. We explore the space of such transformations, providing a simple framework that generates eight major encodings (generated by selecting one of four action representations and one of two frame axioms) and a number of subsidiary ones. We describe a fully-implemented compiler that can generate each of these encodings, and we test the compiler on a suite of STRIPS planning problems in order to determine which encodings have the best properties.
Development of the human brain: In vivo quantification of metabolite and water content with proton magnetic resonance spectroscopy

Cerebral metabolite concentrations and water content were measured by means of localized proton magnetic resonance spectroscopy in 50 children, while metabolite peak ratios in short echo time spectra were evaluated in 173 examinations. Normative curves for normal development were established for two cerebral locations. The current report presents the first study of absolute metabolite concentrations and T1‐and T2‐relaxation as a function of age. Myo‐inositol was found dominating the spectra at birth (12 mmoles/kg), while choline is responsible for the strongest peak in older infants (2.5 mmoles/kg). Creatine and N‐acetyl groups are at significantly lower concentrations in the neonate than in the adult (Cr: 6, NA: 5 mmoles/kg). NA and Cr are determined by gestational age, whereas the concentration of ml correlates best with postnatal age. Quantitative lH MRS is expected to be of particular value in diagnosis and monitoring of pathology in infants, since metabolite ratios are often misleading.
Boundary Behavior of Interior Point Algorithms in Linear Programming

This paper studies the boundary behavior of some interior point algorithms for linear programming. The algorithms considered are Karmarkar's projective rescaling algorithm, the linear rescaling algorithm which was proposed as a variation on Karmarkar's algorithm, and the logarithmic barrier technique. The study includes both the continuous trajectories of the vector fields induced by these algorithms and also the discrete orbits. It is shown that, although the algorithms are defined on the interior of the feasible polyhedron, they actually determine differentiable vector fields on the closed polyhedron. Conditions are given under which a vector field gives rise to trajectories that each visit the neighborhoods of all the vertices of the Klee-Minty cube. The linear rescaling algorithm satisfies these conditions. Thus, limits of such trajectories, obtained when a starting point is pushed to the boundary, may have an exponential number of breakpoints. It is shown that limits of projective rescaling trajectories may have a linear number of such breakpoints. However, projective rescaling trajectories may visit the neighborhoods of linearly many vertices. The behavior of the linear rescaling algorithm near vertices is analyzed. It is proved that all the trajectories have a unique asymptotic direction of convergence to the optimum.
VLSI design of fixed width 2's compliment multiplier

This paper focus on fixed width, parallel multiplier design in which 8 least significant columns of the partial product array are truncated. It takes the two n-bit numbers as input and generates a n-bit product as the output. Baugh-Wooley multiplier is preferred for 2's complement multiplication. In the design, three multiplication modules are used to generate the desired output. All these modules use the combinational blocks. The parallel operation reduces delay effectively. The high performance of the circuit is achieved by replacing the designing components with the more efficient one. The performance evaluation of 2's complement fixed width multiplier using Spartan-3, Spartan-6 and Virtex-5 families available from Xilinx is made. Simulation is done to check the functionality of the design.