Miracle-dz/UniFAR-FTUs huggingface dataset:
Fucoxanthin, a Marine Carotenoid, Reverses Scopolamine-Induced Cognitive Impairments in Mice and Inhibits Acetylcholinesterase in Vitro

Fucoxanthin, a natural carotenoid abundant in edible brown seaweeds, has been shown to possess anti-cancer, anti-oxidant, anti-obesity and anti-diabetic effects. In this study, we report for the first time that fucoxanthin effectively protects against scopolamine-induced cognitive impairments in mice. In addition, fucoxanthin significantly reversed the scopolamine-induced increase of acetylcholinesterase (AChE) activity and decreased both choline acetyltransferase activity and brain-derived neurotrophic factor (BDNF) expression. Using an in vitro AChE activity assay, we discovered that fucoxanthin directly inhibits AChE with an IC50 value of 81.2 μM. Molecular docking analysis suggests that fucoxanthin likely interacts with the peripheral anionic site within AChE, which is in accordance with enzymatic activity results showing that fucoxanthin inhibits AChE in a non-competitive manner. Based on our current findings, we anticipate that fucoxanthin might exhibit great therapeutic efficacy for the treatment of Alzheimer’s disease by acting on multiple targets, including inhibiting AChE and increasing BDNF expression.
Automatic Detection of Dominance and Expected Interest

Social Signal Processing is an emergent area of research that focuses on the analysis of social constructs. Dominance and interest are two of these social constructs. Dominance refers to the level of influence a person has in a conversation. Interest, when referred in terms of group interactions, can be defined as the degree of engagement that the members of a group collectively display during their interaction. In this paper, we argue that only using behavioral motion information, we are able to predict the interest of observers when looking at face-to-face interactions as well as the dominant people. First, we propose a simple set of movement-based features from body, face, and mouth activity in order to define a higher set of interaction indicators. The considered indicators are manually annotated by observers. Based on the opinions obtained, we define an automatic binary dominance detection problem and a multiclass interest quantification problem. Error-Correcting Output Codes framework is used to learn to rank the perceived observer's interest in face-to-face interactions meanwhile Adaboost is used to solve the dominant detection problem. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers in both dominance and interest detection problems.
Downlink resource allocation under channel uncertainties for device-to-device communication

For the downlink device-to-device (D2D) communication underlaying cellular networks, we study the resource allocation policy which jointly optimizes the resource block (RB) assignment and the powers of D2D users and cellular users. Aiming to improve the sum-rate of D2D subsystem and ensure the quality of service (QoS) of the cellular networks, a nominal optimization problem is formulated. Furthermore, considering the uncertainty in channel state information (CSI), a worst-case robust optimization problem with ellipsoid uncertainty set is investigated. After transforming the non-convex problem into a convex subproblem, we obtain the closed-form solution of the subproblem by using Lagrangian dual method and Karush-Kuhn-Tucker (KKT) conditions. And the optimal solution of the original problem is obtained by one dimensional search algorithm. The simulation results verify the advantages of the proposed resource allocation policy and illustrate the effect of the robustness in D2D communication system.
Trade Integration and Trade Imbalances in the European Union: A Network Perspective

We study the ever more integrated and ever more unbalanced trade relationships between European countries. To better capture the complexity of economic networks, we propose two global measures that assess the trade integration and the trade imbalances of the European countries. These measures are the network (or indirect) counterparts to traditional (or direct) measures such as the trade-to-GDP (Gross Domestic Product) and trade deficit-to-GDP ratios. Our indirect tools account for the European inter-country trade structure and follow (i) a decomposition of the global trade flow into elementary flows that highlight the long-range dependencies between exporting and importing economies and (ii) the commute-time distance for trade integration, which measures the impact of a perturbation in the economy of a country on another country, possibly through intermediate partners by domino effect. Our application addresses the impact of the launch of the Euro. We find that the indirect imbalance measures better identify the countries ultimately bearing deficits and surpluses, by neutralizing the impact of trade transit countries, such as the Netherlands. Among others, we find that ultimate surpluses of Germany are quite concentrated in only three partners. We also show that for some countries, the direct and indirect measures of trade integration diverge, thereby revealing that these countries (e.g. Greece and Portugal) trade to a smaller extent with countries considered as central in the European Union network.
Spatial Fuzzy C Means PET Image Segmentation of Neurodegenerative Disorder

Nuclear image has emerged as a promising research work in medical field. Images from different modality meet its own challenge. Positron Emission Tomography (PET) image may help to precisely localize disease to assist in planning the right treatment for each case and saving valuable time. In this paper, a novel approach of Spatial Fuzzy C-Means (PET-SFCM) clustering algorithm is introduced on PET scan image datasets. The proposed algorithm is incorporated the spatial neighborhood information with traditional FCM and updating the objective function of each cluster. This algorithm is implemented and tested on huge data collection of patients with brain neuro degenerative disorder such as Alzheimer’s disease. It has demonstrated its effectiveness by testing it for real world patient data sets. Experimental results are compared with conventional FCM and K-Means clustering algorithm. The performance of the PET-SFCM provides satisfactory results compared with other two algorithms.
A Dynamic Modelling Strategy for Bayesian Computer Model Emulation

Computer model evaluation studies build statistical models of deterministic simulation-based predictions of field data to then assess and criticize the computer model and suggest refinements. Computer models are often expensive computationally: statistical models that adequately emulate their key features can be very much more efficient. Gaussian process models are often used as emulators, but the resulting computations lack the ability to scale to higher-dimensional, time-dependent or functional outputs. For some such problems, especially for contexts of time series outputs, building emulators using dynamic linear models provides a computationally attractive alternative as well as a flexible modelling approach capable of emulating a broad range of stochastic structures underlying the input-output simulations. We describe this here, combining Bayesian multivariate dynamic linear models with Gaussian process modelling in an effective manner, and illustrate the approach with data from a hydrological simulation model. The general strategy will be useful for other computer model evaluation studies with time series or functional outputs.
Tyche Project: A Context Aware Self-Organization Middleware for Ubiquitous Environment

Complexity in ubiquitous environments is an important problem that leads to high implementation and management costs. In some cases, the complexity of the environments' systems are so important that it become impossible for a single human to understand or manage them. The software self-organization simplifies this complexity by reducing the user manipulations and reasoning on the ubiquitous environments' system during software management and organization. This paper presents the self-organization middleware model and architecture, as well as results coming from tests and validation scenarios carried out on the middleware implementation.
Near lossless image compression by local packing of histogram

In this paper a low complexity algorithm is proposed for near lossless compression of images. The reconstructed near lossless image can differ from the original one within a pixelwise error tolerance. This property is used to convert the histogram of the original image, by the proposed algorithm, to a new histogram which is proved to have minimum entropy. Hence, a new image is formed which has minimum entropy and high spatial correlation among its pixels and can efficiently be compressed. Simulation results show the effectiveness of this compression algorithm.
Indexing spatio-temporal trajectories with Chebyshev polynomials

In this paper, we attempt to approximate and index a d- dimensional (d ≥ 1) spatio-temporal trajectory with a low order continuous polynomial. There are many possible ways to choose the polynomial, including (continuous)Fourier transforms, splines, non-linear regressino, etc. Some of these possiblities have indeed been studied beofre. We hypothesize that one of the best possibilities is the polynomial that minimizes the maximum deviation from the true value, which is called the minimax polynomial. Minimax approximation is particularly meaningful for indexing because in a branch-and-bound search (i.e., for finding nearest neighbours), the smaller the maximum deviation, the more pruning opportunities there exist. However, in general, among all the polynomials of the same degree, the optimal minimax polynomial is very hard to compute. However, it has been shown thta the Chebyshev approximation is almost identical to the optimal minimax polynomial, and is easy to compute [16]. Thus, in this paper, we explore how to use the Chebyshev polynomials as a basis for approximating and indexing d-dimenstional trajectories.The key analytic result of this paper is the Lower Bounding Lemma. that is, we show that the Euclidean distance between two d-dimensional trajectories is lower bounded by the weighted Euclidean distance between the two vectors of Chebyshev coefficients. this lemma is not trivial to show, and it ensures that indexing with Chebyshev cofficients aedmits no false negatives. To complement that analystic result, we conducted comprehensive experimental evaluation with real and generated 1-dimensional to 4-dimensional data sets. We compared the proposed schem with the Adaptive Piecewise Constant Approximation (APCA) scheme. Our preliminary results indicate that in all situations we tested, Chebyshev indexing dominates APCA in pruning power, I/O and CPU costs.
Improved annular mode variability in a global atmospheric general circulation model with 16 km horizontal resolution

In an attempt to assess the benefit of resolving the subsynoptic to mesoscale processes, the spatial and temporal characteristics of the annular modes (AMs), in particular those related to the troposphere‐stratosphere interaction, are evaluated for moderate and high horizontal resolution simulations with the European Centre for Medium‐Range Weather Forecast Integrated Forecast System global atmospheric general circulation model in comparison with the reanalysis. Notably, the performance with the high horizontal resolution (T1279 truncation, ~16 km) version of the model is relatively more skillful than the moderate resolution (T159 truncation, ~125 km) on most metrics examined, including the variance of the AMs at different seasons of the year, the intrinsic e‐folding time scales of the AMs, and the downward influence from the stratosphere to troposphere in the AMs. Moreover, the summer Southern Annular Mode is more persistent in the high resolution and projected to respond in a greater magnitude to climate change forcing than the moderate resolution.
Applied to Human Centered Interaction Analysis

Emotional speech characterization is an important issue for the understanding of interaction. This article discusses the time-scale analysis problem in feature extraction for emotional speech processing. We describe a computational framework for combining segmental and supra-segmental features for emotional speech detection. The statistical fusion is based on the estimation of local a posteriori class probabilities and the overall decision employs weighting factors directly related to the duration of the individual speech segments. This strategy is applied to a real-world application: detection of Italian motherese in authentic and longitudinal parent-infant interaction at home. The results suggest that short- and long-term infor- mation, respectively, represented by the short-term spec- trum and the prosody parameters (fundamental frequency and energy) provide a robust and efficient time-scale analysis. A similar fusion methodology is also investigated by the use of a phonetic-specific characterization process. This strategy is motivated by the fact that there are varia- tions across emotional states at the phoneme level. A time- scale based on both vowels and consonants is proposed and it provides a relevant and discriminant feature space for acted emotion recognition. The experimental results on two different databases Berlin (German) and Aholab (Basque) show that the best performance are obtained by our pho- neme-dependent approach. These findings demonstrate the relevance of taking into account phoneme dependency (vowels/consonants) for emotional speech characterization.
Desmosomal proteins, including desmoglein 3, serve as novel negative markers for epidermal stem cell-containing population of keratinocytes

No single method has been universally adopted for identifying and isolating epidermal stem/progenitor cells, and the emergence of new markers of stem cell populations is worth exploring. Here we report, for the first time, that clusters of basal keratinocytes at the tips of the rete ridges in human palm, previously recognised as a major repository of stem cells, had very low levels of desmoplakin protein and mRNA expression, compared with cells at the sides of the ridges or above the dermal papillae. We found that in populations of palm keratinocytes, selected by their ability to adhere rapidly to type IV collagen, there were significantly reduced levels of desmoplakin and other major desmosome proteins. We then showed that a low desmoglein 3 (Dsg3) expression on the cell surface could be used to enrich for a cell population with high clonogenecity, colony forming efficiency and enhanced proliferative potential, but with a low ability to form the abortive clones, compared with populations with a higher Dsg3 expression. Moreover, stringent sorting of populations showing both β1 integrin-bright and Dsg3-dull expression enabled even further enrichment of a population containing the putative epidermal stem cells. These findings provide the basis for a new strategy for epidermal stem/progenitor cell enrichment, and encourage further study of the role of desmosomes in stem cell biology.
State of The Art and Hot Aspects in Cloud Data Storage Security SICS technical report T 2013 : 01

Along with the evolution of cloud computing and cloud storage towards maturity, researchers have analyzed an increasing range of cloud computing security aspects, data security being an important topic in this area. In this paper, we examine the state of the art in cloud storage security through an overview of selected peer reviewed publications. We address the question of defining cloud storage security and its different aspects, as well as enumerate the main vectors of attack on cloud storage. The reviewed papers present techniques for key management and controlled disclosure of encrypted data in cloud storage, while novel ideas regarding secure operations on encrypted data and methods for protection of data in fully virtualized environments provide a glimpse of the toolbox available for securing cloud storage. Finally, new challenges such as emergent government regulation call for solutions to problems that did not receive enough attention in earlier stages of cloud computing, such as for example geographical location of data. The methods presented in the papers selected for this review represent only a small fraction of the wide research effort within cloud storage security. Nevertheless, they serve as an indication of the diversity of problems that are being addressed.
Diabetes distress scale-17 implementation among patients with diabetes: Turkish validity and reliability study.

OBJECTIVE To prove reliability and validity of the Turkish version of diabetes distress scale. METHODS The cross-sectional study was conducted at Tepecik Training and Research Hospital, Izmir, Turkey, from July 2011 to February 2012, and comprised diabetics whose ages ranged from 25 to 86 years who voluntarily participated in the study. Descriptive statistics, exploratory and confirmatory factor analyses were used for reliability and validity evaluations. SPSS 21 was used for data analysis. RESULTS Of the 165 patients, 87(52.7%) were women and 78(47.3%) were men. Overall, 32(19.4%) patients had normal weight, 52(31.5%) were overweight, 74(44.8%) were obese and 7(4.2%) were morbid obese. Mean age was 53.69±11.39 years. Mean score was 50.87±11.53 points (range: 23 to 84). As for distress distribution, 15(9.1%) patients had no problem (0-34 points), 141(85.5%) moderate (35-68 points) and 9(5.5%) had high (>68 points). Fasting blood glucose was higher than 200 mg/dl in 22(13.2%) patients. CONCLUSIONS Turkish diabetes distress scale-17 was found to be valid and reliable and had satisfying fit-index parameters.
Localization of forgeries in MPEG-2 video through GOP size and DQ analysis

This work addresses forgery localization in MPEG-2 compressed videos. The proposed method is based on the analysis of Double Quantization (DQ) traces in frames that were encoded twice as intra (i.e., I-frames). Employing a state-of-the-art method, such frames are located in the video under analysis by estimating the size of the Group Of Pictures (GOP) that was used in the first compression; then, the DQ analysis is devised for the MPEG-2 encoding scheme and applied to frames that were intra-coded in both the first and second compression. In such a way, regions that were manipulated between the two encodings are detected. Compared to existing methods based on double quantization analysis, the proposed scheme makes forgery localization possible on a wider range of settings.
Information Filtering on Micro-blogging Services

Micro-blogging is an emerging form of communication and became very popular in recent years. Micro-blogging services allow users to publish updates as short text messages that are broadcasted to the followers of users in real-time. Twitter is currently the most popular micro-blogging service. It is a rich and real-time information source and a good way to discover interesting content or to follow recent developments. However, the service is fairly simple, and rely on the concept of following other users. With the lack of classification or filtering tools, the user receives all messages posted by the users she follows. In most cases, the user receive a noisy stream of updates. In this paper, an information filtering system for Twitter is introduced. The system focuses on one kind of feeds on Twitter: Lists which are a manually selected group of users on Twitter. List feeds tend to be focused on specific topics, however it is still noisy due to irrelevant messages. Therefore, we propose an online filtering system, which extracts the niche topics in a list, filtering out irrelevant messages. To classify messages as relevant or irrelevant, next to text-based features, we utilize the social network of Twitter and different aspects of messages such as the temporal properties and the links included in the text. We evaluate our approach on a labeled dataset of lists and with the help of these novel features, we achieve accuracies between 85% and 95%. Finally, we present the online prototype of the system.
Service-centric traffic engineering and cache orchestration in an ICN-enabled network

Large service providers (SP) who own their computing, storage, and networking resources have the potential to jointly manage their resources to achieve better quality of service and lower cost. Information-centric networking (ICN) exploits in-network caching resource, by unique identification of content objects. Orchestrating cache and networking resources in a coordinated way is the key to the success of ICN. Traffic engineering (TE), which allocates networking resources to services, face new challenges in the presence of geographically- replicated services and in-network cache in ICN. Some previous works [1] [2] [3] have addressed this by jointly optimizing content placement (allocating storage resources) and TE. However, jointly managing storage and networking resources at the content level is not scalable because of the huge number of contents and nontrivial amount of overhead to distribute content information and optimize content caching and routing at small time scale. In this study, we solve the TE and cache orchestration (CO) problems in an ICN-enabled network at the service level, in which no specific information about individual contents is needed. So, the proposed optimization is both scalable and easily implementable. Specifically, we formulate two simple but effective linear programs to solve the TE problem at the service level for scenarios with and without caching. We also propose a general network-flow model to solve the service-level CO problem. Simulation results show that our proposed TE and CO schemes can significantly reduce network cost and increase cache hit ratio, with the constraints of satisfying service-level agreements (SLA).
Visual Debugging Techniques for Reactive Data Visualization

Interaction is critical to effective visualization, but can be difficult to author and debug due to dependencies among input events, program state, and visual output. Recent advances leverage reactive semantics to support declarative design and avoid the “spaghetti code” of imperative event handlers. While reactive programming improves many aspects of development, textual specifications still fail to convey the complex runtime dynamics. In response, we contribute a set of visual debugging techniques to reveal the runtime behavior of reactive visualizations. A timeline view records input events and dynamic variable updates, allowing designers to replay and inspect the propagation of values step‐by‐step. On‐demand annotations overlay the output visualization to expose relevant state and scale mappings in‐situ. Dynamic tables visualize how backing datasets change over time. To evaluate the effectiveness of these techniques, we study how first‐time Vega users debug interactions in faulty, unfamiliar specifications; with no prior knowledge, participants were able to accurately trace errors through the specification.
The pre-ligand binding assembly domain: a potential target of inhibition of tumour necrosis factor receptor function

Signalling by the tumour necrosis factor receptors (TNFR) is thought to be mediated by the binding of the trimeric ligand TNF to three monomeric subunits of the receptor. This ligand induced trimerisation model of TNFR signalling is mainly supported by crystallographic data of the p60 TNFR-1 and TNFβ complex in which the trimeric ligand interdigitates between the individual receptor chains and prevents the receptor subunits from interacting with each other. Recently, a domain NH2-terminal to the ligand binding domain in the extracellular region of p60 TNFR-1, p80 TNFR-2 and Fas was identified that mediates receptor self association before ligand binding. This pre-ligand binding assembly domain or PLAD is critical for assembly of functional receptor complexes on the cell surface and may provide a potential target in the design of future novel therapeutics against diseases mediated by members of the TNFR family of receptors.