Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual Role of Heat Shock Proteins as Regulators of Apoptosis and Innate Immunity

Stress or heat shock proteins (HSPs) 70 and 90 are powerful chaperones whose expression is induced in response to a wide variety of physiological and environmental insults. These proteins have different functions depending on their intracellular or extracellular location. Intracellular HSPs have a protective function. They allow the cells to survive potentially lethal conditions. The cytoprotective functions of HSPs can largely be explained by their anti-apoptotic properties. HSP70 and HSP90 can directly interact with different proteins of the tightly regulated programmed cell death machinery and thereby block the apoptotic process at distinct key points. In cancer cells, where the expression of HSP70 and/or HSP90 is frequently abnormally high, they participate in oncogenesis and in resistance to chemotherapy. Therefore, the inhibition of HSPs has become an interesting strategy in cancer therapy. In contrast to intracellular HSPs, extracellularly located or membrane-bound HSPs mediate immunological functions. They can elicit an immune response providing a link between innate and adaptive immune systems. In cancer, most immunotherapeutical approaches based on extracellular HSPs exploit their carrier function for immunogenic peptides. This review will focus on the roles of HSP70 and HSP90 in apoptosis and in innate immunity and how these functions are being exploited in cancer therapy.
CRM1 inhibitor S109 suppresses cell proliferation and induces cell cycle arrest in renal cancer cells

Abnormal localization of tumor suppressor proteins is a common feature of renal cancer. Nuclear export of these tumor suppressor proteins is mediated by chromosome region maintenance-1 (CRM1). Here, we investigated the antitumor eff ects of a novel reversible inhibitor of CRM1 on renal cancer cells. We found that S109 inhibits the CRM1-mediated nuclear export of RanBP1 and reduces protein levels of CRM1. Furthermore, the inhibitory eff ect of S109 on CRM1 is reversible. Our data demonstrated that S109 signifi cantly inhibits proliferation and colony formation of renal cancer cells. Cell cycle assay showed that S109 induced G1-phase arrest, followed by the reduction of Cyclin D1 and increased expression of p53 and p21. We also found that S109 induces nuclear accumulation of tumor suppressor proteins, Foxo1 and p27. Most importantly, mutation of CRM1 at Cys528 position abolished the eff ects of S109. Taken together, our results indicate that CRM1 is a therapeutic target in renal cancer and the novel reversible CRM1 inhibitor S109 can act as a promising candidate for renal cancer therapy.
Kernel Algorithms Distribution Embeddings in Reproducing Kernel Hilbert Spaces

The “kernel trick” is well established as a means of constructing nonlinear algorithms from linear ones, by transferring the linear algorithms to a high dimensional feature space: specifically, a reproducing kernel Hilbert space (RKHS). Recently, it has become clear that a potentially more far reaching use of kernels is as a linear way of dealing with higher order statistics, by embedding probability distributions in a suitable RKHS. These representations allow us to painlessly represent high order properties of distributions, and to compare distributions in a nonparametric setting.
Combining real options and multiattribute decision analysis to define the favourable

IT infrastructure implementation projects have to deal with many uncertainties and risks. A favourable implementation strategy therefore needs to reduce uncertainties and risks with regard to the IT infrastructure investment. The complexity in these type of decisions relates to the fact that different types of uncertainties and risks and quantitative and qualitative information need to be incorporated in a sound, transparent and pragmatic way. In this paper a decision making model for defining a favourable implementation strategy is introduced. The approach integrates the utility based evidential reasoning approach for multiattribute decision analysis (MADA) with real options analysis. Using real data from a public organization, the analysis framework is used to guide an investment decision for a large multi-staged, cost reduction IT infrastructure implementation. We compare the presented framework with the net present value analysis, the real options analysis, and the MADA framework. The paper concludes on how the presented approach can be used to improve the evaluation of IT infrastructure implementation strategy decisions and discusses the implications for further research.
Neuronal movement detector in the model of simulated mammalian visual system

A simple model of mammalian visual system was created. This structure was simulated by means of several hundred Hodgkin-Huxley neurons. We investigated signal processing properties of the model. Energetic function – a concept taken from information theory was applied to the analysis of Primary Visual Cortex dynamics. Discussion of such method’s efficiency in movement detection is presented in some detail.
Score Following Using Spectral Analysis and Hidden Markov Models

This paper presents an approach to score following. The real-time alignment of a performance with a score is obtained through the use of a hidden Markov model. The model works on two levels. The lower level compares the features of the incoming signal with the expected ones. Groups of states of the lower level are embedded in states at the higher level, which are used to model the performance by taking into account the possible errors a performer may make. The performer's position on the score is computed through a decoding technique alternative to classic Viterbi decoding. A novel technique for the training of hidden Markov models is proposed.
Temperature Structure in Abell 1367

We study the temperature structure of the rich cluster of galaxies Abell 1367 using two independent methods of correcting for the ASCA point-spread function. The results of the two methods are in excellent agreement and give solid evidence of a strong and localized shock in the intracluster medium. Our analysis suggests that we are observing the merger of two subclusters. We find that the lower luminosity subcluster, located to the northwest, has a higher temperature of 4.2 ± 0.3 keV, compared to the more luminous southeast subcluster, whose temperature is 3.2 ± 0.1 keV. An analysis of the ROSAT surface brightness profiles of both subclusters is also presented. The data agree well with predictions from numerical simulations of mergers between subcluster-sized masses, in particular with early stages of the merger prior to the first core passage.
Magnetization transfer imaging and proton MR spectroscopy in the evaluation of axonal injury: correlation with clinical outcome after traumatic brain injury.

BACKGROUND AND PURPOSE Current imaging does not permit quantification of neural injury after traumatic brain injury (TBI) and therefore limits both the development of new treatments and the appropriate counseling of patients concerning prognosis. We evaluated the utility of magnetization transfer ratio (MTR) and proton MR spectroscopy in identifying patients with neuronal injury after TBI. METHODS Thirty patients with TBI (21-77 years old; mean age, 42 years; admission Glasgow Coma Scale (GOS) scores 3-15; mean score, 11) were studied on a 1.5-T system with magnetization transfer imaging and MR spectroscopy of the splenium. Magnetization transfer imaging was also performed in the brain stem in all patients, and other areas of the brain were sampled in one patient. The splenium of the corpus callosum and brain stem were studied because these are often affected by diffuse axonal injury. Scans were obtained 2 to 1129 days after injury (median, 41 days). MTR was considered abnormal if it was more than 2 SD below normal. Proton MR spectroscopy was used to calculate the N-acetylaspartate (NAA)/creatine (Cr) ratio. GOS was determined at least 3 months after injury. RESULTS In 10 patients with a GOS of 1 to 4, the mean NAA/Cr was 1.24 +/- 0.28; two of these patients had abnormal MTR in normal-appearing white matter (NAWM). In 20 patients with a GOS of 5, the mean NAA/Cr was 1.53 +/- 0.37 (P < .05); four of these patients had abnormal MTR in NAWM. MTR abnormalities in NAWM were identified in six patients, but these changes did not correlate with GOS or MR spectroscopy changes. CONCLUSION MTR and MR spectroscopy can quantify damage after TBI, and NAA levels may be a sensitive indicator of the neuronal damage that results in a worse clinical outcome.
Path selection methods with multiple constraints in service-guaranteed WDM networks

We propose a new approach to constraint-based path selection for dynamic routing and wavelength allocation in optical networks based on WDM. Our approach considers service-specific path quality attributes, such as physical layer impairments, reliability, policy, and traffic conditions, and uses a flooding-based transfer of path information messages from source to destination to find multiple feasible paths. It is fully decentralized, as it uses local network state information. To better understand how multiple constraints impact the efficiency of wavelength routing, and consequently provision the service guarantees, we specifically focus on electronic regenerators that, while being widely considered as the basic building blocks for optical switching nodes, are likely to impose conflicting constraints on routing. For example, electronic regenerators extend the optical reach and could perform wavelength shifting, but also induce impairments, such as delays and operational costs. The question for constraint-based routing is how to account for these conflicting effects. To validate the network modeling, a wide range of networking scenarios are simulated, such as ring, mesh and interconnections of all-optical networks with electronic gateways. For all these scenarios, our approach is shown to efficiently accommodate multiple, conflicting routing metrics related to different services and network architectures.
Machine learning for modeling Dutch pronunciation variation

This paper describes the use of rule induction techniques for the automatic extraction of phonemic knowledge and rules from pairs of pronunciation lexicons. This extracted knowledge allows the adaptation of speech processing systems to regional variants of a language. As a case study, we apply the approach to Northern Dutch and Flemish (the variant of Dutch spoken in Flanders, a part of Belgium) , based on Celex and Fonilex, pronunciation lexicons for Northern Dutch and Flemish, respectively. In our study, we compare two rule induction techniques, TransformationBased Error-Driven Learning (TBEDL) (Brill, 1995) and C5.0 (Quinlan, 1993), and evaluate the extracted knowledge quantitatively (accuracy) and qualitatively (linguistic relevance of the rules). We conclude that, whereas classication-based rule induction with C5.0 is more accurate, the transformation rules learned with TBEDL can be more easily interpreted.
SPEECH ENHANCEMENT USING NEAR-FIELD SUPERDIRECTIVITY WITH AN ADAPTIVE SIDELOBE CANCELER AND POST-FILTER

This paper describes a new microphone array technique and investigates its effectiveness for speech enhancement. A system structure consisting of a fixed near-field superdirective beamformer and an adaptive sidelobe canceling path is proposed (NFSD-ASC). The effect of adding a post-filter is also examined. The system is evaluated in terms of speech quality measures in the context of a computer workstation in an office environment. The speaker is located directly in front of the computer monitor at a distance of 60 cm and the array is designed to fit across the top of a standard 17 inch monitor. The experiments show that the array is effective in both decreasing the noise level and the amount of signal distortion when compared with standard near-field superdirectivity and the generalised sidelobe canceler.
Real-time human tracking by detection based on HOG and particle filter

In this paper, a novel approach is proposed to achieve the multi-human tracking in video surveillance system using a combination of tracking by detection method. First of all, a modified foreground objects detection method is applied to extract the foreground blobs thus achieving the regions of interest. On the second hand, the HOG features together with searching strategies are used to initialize the trackers of humans and the trackers are utilized for the particle filter tracking. Through the tracker initialization, the problems that one blob might contain several people could be overcome. Moreover, for the tracking aspect, we further utilize the data from the foreground detection that a color-edgetexture histogram is used by calculating the local binary pattern of the edge of the foreground objects which could have a good performance in describing the shape and texture of the objects. Finally, occlusion solutions strategies are applied in order to overcome the occlusion problems during tracking. Experimental results on different data sets have proved that our method has better performance and good real-time ability.
Metric decomposition , smooth measures , and clustering

In recent years, randomized decompositions have become a fundamental tool in the design of algorithms on metric spaces. A randomized decomposition is a distribution over partitions of a metric space satisfying certain properties. Two main types of decompositions have emerged: The “padded” decomposition, see e.g. [20, 15, 9, 19], and the “separating” decomposition, e.g. [2, 3, 5, 14]. Here, we show that if a metric space admits the former type of decomposition, then it also admits the latter. This result froms a nontrivial transformation of distributions. Using this technique, we give new approximation algorithms for the 0-extension problem when the input metric (T, d) on terminals admits such a decomposition. For instance, we achieve an O(1) approximation when the metric on terminals is planar or a subset of R under any norm. We then introduce two additional techniques. The first is based on a powerful theorem about the existence of “well-behaved” probability measures on metric spaces. The second yields improved randomized decompositions for n-point subsets of Lp, 1 < p < 2, by utilizing probabilistic techniques of Marcus and Pisier based on p-stable random variables. Both yield improved approximation algorithms for important classes of metric spaces.
ANKK1, TTC12, and NCAM1 polymorphisms and heroin dependence: importance of considering drug exposure.

CONTEXT The genetic contribution to liability for opioid dependence is well established; identification of the responsible genes has proved challenging. OBJECTIVE To examine association of 1430 candidate gene single-nucleotide polymorphisms (SNPs) with heroin dependence, reporting here only the 71 SNPs in the chromosome 11 gene cluster (NCAM1, TTC12, ANKK1, DRD2) that include the strongest observed associations. DESIGN Case-control genetic association study that included 2 control groups (lacking an established optimal control group). SETTING Semistructured psychiatric interviews. PARTICIPANTS A total of 1459 Australian cases ascertained from opioid replacement therapy clinics, 531 neighborhood controls ascertained from economically disadvantaged areas near opioid replacement therapy clinics, and 1495 unrelated Australian Twin Registry controls not dependent on alcohol or illicit drugs selected from a twin and family sample. MAIN OUTCOME MEASURE Lifetime heroin dependence. RESULTS Comparison of cases with Australian Twin Registry controls found minimal evidence of association for all chromosome 11 cluster SNPs (P ≥ .01); a similar comparison with neighborhood controls revealed greater differences (P ≥ 1.8 × 10(-4)). Comparing cases (n = 1459) with the subgroup of neighborhood controls not dependent on illicit drugs (n = 340), 3 SNPs were significantly associated (correcting for multiple testing): ANKK1 SNP rs877138 (most strongly associated; odds ratio = 1.59; 95% CI, 1.32-1.92; P = 9.7 × 10(-7)), ANKK1 SNP rs4938013, and TTC12 SNP rs7130431. A similar pattern of association was observed when comparing illicit drug-dependent (n = 191) and nondependent (n = 340) neighborhood controls, suggesting that liability likely extends to nonopioid illicit drug dependence. Aggregate heroin dependence risk associated with 2 SNPs, rs877138 and rs4492854 (located in NCAM1), varied more than 4-fold (P = 2.7 × 10(-9) for the risk-associated linear trend). CONCLUSIONS Our results provide further evidence of association for chromosome 11 gene cluster SNPs with substance dependence, including extension of liability to illicit drug dependence. Our findings highlight the necessity of considering drug exposure history when selecting control groups for genetic investigations of illicit drug dependence.
Is tree diversity an important driver for phosphorus and nitrogen acquisition of a young tropical plantation ?

Many tropical plantations in Central America are monocultures of fast growing, mostly exotic species such as a teak, eucalypts and pines. This has been perceived as a problem for ecosystem stability, pest control, local biodiversity and long-term nutrient availability. In our study, we followed the effects of increasing tree diversity (1, 3 and 6 native species) on aboveground nitrogen (N) and phosphorus (P) pools in a young experimental biodiversity plantation (central Panama) over two subsequent years. Our results show a positive but not consistent net effect of biodiversity on the N and P pools, mainly explained by the complementarity effect. N and P use efficiencies strongly varied among the investigated tree species and the species richness gradient. Anacardium excelsum and Luehea seemannii were associated with higher N and P use efficiencies while Hura crepitans and Tabebuia rosea were less efficient in aboveground biomass production per unit N or P. Tree species tended to have lower P use efficiencies in the intermediate diversity level compared to monocultures and six-species mixtures. Although the environmental conditions explained a large part of the variation in the N and P pools (58%) in our experiment, we argue that incorporating tree mixtures in the management can bring additional benefits and improve tree growth and nutrient uptake as compared to the monocultures. © 2010 Elsevier B.V. All rights reserved.
An Automated Bacterial Colony Counting System

Bacterial colony enumeration is an essential tool for many widely used biomedical assays. However, bacterial colony enumerating is a low throughput, time consuming and labor intensive process since there might exist hundreds or thousands of colonies on a Petri dish, and the counting process is often manually performed by well-trained technicians. In this paper, we introduce a fully automatic yet cost-effective bacterial colony counter. Our proposed method can recognize chromatic and achromatic images and thus can deal with both color and clear medium. In addition, our proposed method can also accept general digital camera images as its input. The whole process includes detecting dish/plate regions, identifying colonies, separating aggregated colonies, and finally reporting consistent and accurate counting results. Our proposed counter has a promising performance in terms of both precision and recall, and is flexible and efficient in terms of labor- and time- savings.
A novel network architecture for data center

To meet the requirements for a scalable, agile and efficient data center, we present DNet, a dual-network structure composed of a primary network and a secondary network. DNet is based on layer 2 fabric, which is considered more agile than layer 3 fabric in data center design. We compress ARP request and move it from the primary network to a secondary one, and we propose a DNet source routing protocol (DSR) for the primary network. We evaluate that, with these characteristics, DNet is enabled to scale to support huge data centers with up to 131K hosts. Besides, DSR simplifies switch design, and decreases the cost and delay.
Dynamics of acorn production by five species of Southern Appalachian oaks

The management implications of fluctuations in acorn crop size underscore the need to better understand their patterns, causal factors, and predict4bility (both within a year and long term). Acorn yield has a demonstrable influence on the population dynamics of many wildlife species, both game (Eiler et al. 1989, Wentworth et al. 1992) and nongame (Hannon et al. 1987, Koenig and Mumme 1987, Smith and Scarlett 1987, Elkinton et al. 1996, Wolff 1996, McShea 2000). Wolff . (1996) suggests that acorns function as a "keystone" resource in forest community dynamics, by influencing smail mammal prey populations. Indeed, acorn· crop size has a far-reaching influence on ecosystems. "'''hite-footed mouse (Peromyscus leucopus) populations, which are directly influenced by acorn crop size, affect gypsy moth (Lymantria dispar) populations (E:lkintonet al. 1996) and even the prevalence of Lyme disease (Jones et al. 1998). Also, oak regeneration has been shown to increase following large acorn crops (Marquis et al. 1976), although a host ofother factors influence seedling establishment and success (Loftis and McGee 1993). The ability to predict the size of future acorn crops (Sork et aI. 1993, Koenig, Mumme, et al. 1994) and to estimate current-year production (e.g., Koenig, Knops, et al. 1994, Whitehead 1969, 1980, Graves 1980, Sharp 1958, Christisen and Kearby 1984) has received considerable attention by forest managers and researchers because of its importance to 'wildlife and forest regeneration. This chapter examines temporal patterns of acorn production within and among five species of southern Appalachian oaks. The data en-
Exploring the Role of Future Perspective in Predicting Turkish University Students’ Beliefs About Global Climate Change

Abstract Recent studies indicate that limited understanding about causes and its potential impacts of climate change and fault beliefs by people across different countries of the world including Turkey is a real challenge. Acceptance of climate change as a real threat, believing its existence, and knowing causes and consequences are very significant for climate change adaptation and mitigation. Therefore, exploring underlying factors shaping or affecting beliefs of people is needed for designing educational interventions for a change in individuals’ attitudes and behaviours. The main purpose of the present study was to explore how and to what extent future time perspective, perceived knowledge about global climate change, and environmental attitudes explain the university students’ beliefs about occurrence, causes and consequences of GCC. A quantitative research was conducted with the participation of one thousand, five hundred and eighty undergraduate students (n=1580) of METU and the data was gathered through Future Perspective Related Beliefs about Global Climate Change Scale. The study findings suggested that ecocentric attitude and perceived knowledge are mainly two influential factors for the undergraduate students’ beliefs about global climate change. Future time perspective’s contribution although low, was found to be significant in beliefs about GCC. The results of this study would shed light to evaluate and improve educational programs and curriculum in higher education, and can be a guide because Turkish literature does not serve any research that seek students’ future time perspective related to global climate change.