Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of SCN Connectivity and the Circadian Control of Arousal: A Diminishing Role for Humoral Factors?

The suprachiasmatic nucleus (SCN) is part of a wake-promoting circuit comprising the dorsomedial hypothalamus (DMH) and locus coeruleus (LC). Although widely considered a “master clock,” the SCN of adult rats is also sensitive to feedback regarding an animal's behavioral state. Interestingly, in rats at postnatal day (P)2, repeated arousing stimulation does not increase neural activation in the SCN, despite doing so in the LC and DMH. Here we show that, by P8, the SCN is activated by arousing stimulation and that selective destruction of LC terminals with DSP-4 blocks this activational effect. We next show that bidirectional projections among the SCN, DMH, and LC are nearly absent at P2 but present at P8. Despite the relative lack of SCN connectivity with downstream structures at P2, day-night differences in sleep-wake activity are observed, suggesting that the SCN modulates behavior at this age via humoral factors. To test this hypothesis, we lesioned the SCN at P1 and recorded sleep-wake behavior at P2: Day-night differences in sleep and wake were eliminated. We next performed precollicular transections at P2 and P8 that isolate the SCN and DMH from the brainstem and found that day-night differences in sleep-wake behavior were retained at P2 but eliminated at P8. Finally, the SCN or DMH was lesioned at P8: When recorded at P21, rats with either lesion exhibited similarly fragmented wake bouts and no evidence of circadian modulation of wakefulness. These results suggest an age-related decline in the SCN's humoral influence on sleep-wake behavior that coincides with the emergence of bidirectional connectivity among the SCN, DMH, and LC.
Shannon's Secrecy System With Informed Receivers and its Application to Systematic Coding for Wiretapped Channels

Shannon's secrecy system is studied in a setting, where both the legitimate decoder and the wiretapper have access to side information sequences correlated to the source, but the wiretapper receives both the coded information and the side information via channels that are more noisy than the respective channels of the legitimate decoder, which in turn, also shares a secret key with the encoder. A single-letter characterization is provided for the achievable region in the space of five figures of merit: the equivocation at the wiretapper, the key rate, the distortion of the source reconstruction at the legitimate receiver, the bandwidth expansion factor of the coded channels, and the average transmission cost (generalized power). Beyond the fact that this is an extension of earlier studies, it also provides a framework for studying fundamental performance limits of systematic codes in the presence of a wiretap channel. The best achievable performance of systematic codes is then compared to that of a general code in several respects, and a few examples are given.
Spectral Risk Measures with an Application to Futures Clearinghouse Variation Margin Requirements

This paper applies an AR(1)-GARCH (1, 1) process to detail the conditional distributions of the return distributions for the S&P500, FT100, DAX, Hang Seng, and Nikkei225 futures contracts. It then uses the conditional distribution for these contracts to estimate spectral risk measures, which are coherent risk measures that reflect a user's risk-aversion function. It compares these to more familiar VaR and Expected Shortfall (ES) measures of risk, and also compares the precision and discusses the relative usefulness of each of these risk measures in setting variation margins that incorporate time-varying market conditions. The goodness of fit of the model is confirmed by a variety of backtests.
Mitigating Radio Irregularity Impact: An RSSI Calibration Method for Range-Free Localization in Sensor Networks

Range-Free algorithms, appealing to people for their cost-efficiency, suffer from the precision problem. Some methods try to combine received signal strength indication (RSSI) with range-free localization algorithms to improve the accuracy, but RSSI is sensitive to the radio irregularity. Based on the well known RIM model, we present a new method of RSSI calibration, namely MRIRC, to mitigate the impact of radio irregularity. MRIRC divides nodes within a continuous angle into groups with the same level of RSSI deviation. By doing this, given an irregular deviation input, MRIRC can get a maximum angle (worst case), which guarantees that the nodes in the same group are in the same level of radio irregularity, thereby improving the accuracy of the distance estimations. We conduct simulations for large-scale sensor networks, and the results show that MRIRC achieves superior performance over the other two typical Range-Free algorithms.
Engineering the Processive Run Length of the Kinesin Motor

Conventional kinesin is a highly processive molecular motor that takes several hundred steps per encounter with a microtubule. Processive motility is believed to result from the coordinated, hand-over-hand motion of the two heads of the kinesin dimer, but the specific factors that determine kinesin's run length (distance traveled per microtubule encounter) are not known. Here, we show that the neck coiled-coil, a structure adjacent to the motor domain, plays an important role in governing the run length. By adding positive charge to the neck coiled-coil, we have created ultra-processive kinesin mutants that have fourfold longer run lengths than the wild-type motor, but that have normal ATPase activity and motor velocity. Conversely, adding negative charge on the neck coiled-coil decreases the run length. The gain in processivity can be suppressed by either proteolytic cleavage of tubulin's negatively charged COOH terminus or by high salt concentrations. Therefore, modulation of processivity by the neck coiled-coil appears to involve an electrostatic tethering interaction with the COOH terminus of tubulin. The ability to readily increase kinesin processivity by mutation, taken together with the strong sequence conservation of the neck coiled-coil, suggests that evolutionary pressures may limit kinesin's run length to optimize its in vivo function.
Carnosic acid sensitized TRAIL-mediated apoptosis through down-regulation of c-FLIP and Bcl-2 expression at the post translational levels and CHOP-dependent up-regulation of DR5, Bim, and PUMA expression in human carcinoma caki cells

Carnosic acid is a phenolic diterpene from rosmarinus officinalis, and has multiple functions, such as anti-inflammatory, anti-viral, and anti-tumor activity. In this study, we examined whether carnosic acid could sensitize TRAIL-mediated apoptosis in human renal carcinoma Caki cells. We found that carnosic acid markedly induced TRAIL-mediated apoptosis in human renal carcinoma (Caki, ACHN, and A498), and human hepatocellular carcinoma (SK-HEP-1), and human breast carcinoma (MDA-MB-231) cells, but not normal cells (TMCK-1 and HSF). Carnosic acid induced down-regulation of c-FLIP and Bcl-2 expression at the post-translational levels, and the over-expression of c-FLIP and Bcl-2 markedly blocked carnosic acid-induced TRAIL sensitization. Furthermore, carnosic acid induced death receptor (DR)5, Bcl-2 interacting mediator of cell death (Bim), and p53 up-regulated modulator of apoptosis (PUMA) expression at the transcriptional levels via CCAAT/enhancer-binding protein-homologous protein (CHOP). Down-regulation of CHOP expression by siRNA inhibited DR5, Bim, and PUMA expression, and attenuated carnosic acid plus TRAIL-induced apoptosis. Taken together, our study demonstrates that carnosic acid enhances sensitization against TRAIL-mediated apoptosis through the down-regulation of c-FLIP and Bcl-2 expression, and up-regulation of ER stress-mediated DR5, Bim, and PUMA expression at the transcriptional levels.
Collaborative Recommendation of Photo-Taking Geolocations

We apply collaborative recommendation algorithms to photography in order to produce personalized suggestions for locations in the geocoordinate space where mobile users can take photos. We base our work on a collection of 3 million geotagged, publicly-available Flickr.com digital photos on which we applied a series of steps: first, unique locations are identified by discretizing the continuous latitude and longitude geocoordinates into geographic virtual bins; second, implicit feedback is calculated in a user x location matrix using normalized frequency; and third, missing feedback values are imputed through four different algorithms (one memory-based and three model-based). Our results show that two of the model-based algorithms produced the best RMSE and that the RMSE is sensitive to increasing hash bin size.
Analogical projection in pattern perception

This paper proposes a perceptually motivated method for solving proportional analogy problems involving sequential patterns. Our method is based on an algebraic model of pattern perception that determines the gestalt structure of sequential patterns. The gestalts of sequential patterns are represented as algebraic terms in the model. An analogical relation between sequential patterns appearing in proportional analogy is then formalized as a mapping between algebras that generate terms representing the gestalts of these patterns. Based on this formalism, an algorithm is proposed that solves proportional analogy problems: given three sequential patterns, the algorithm computes a fourth pattern such that the resulting two pairs of patterns are perceived as having an identical analogical relation.
Artificial maxwell fisheye lens design and synthesis with using metasurface structure

In this paper, we use a periodic structure to produce a Maxwell's fisheye lens and investigate some important information such as power, efficiency, etc. At Chapter 1, we describe some introduction about the surface plasmon polariton (SPP), frequency selective surface (FSS), concentration power by using lens. At Chapter 2, we describe some theorem about we need to use like Transmission line modal, high impedance surface, periodic structure lens. At Chapter 3, we describe our design and we show some simulation results about the surface wave Maxwell's fisheye lens. At chapter 4, we discuss our results about the fisheye lens. At chapter 5, we will draw the conclusions.
Three longitudinal impact evaluations of continuing library education : Participant satisfaction, program effects, and future participation

Many practitioners of continuing library education (CLE) are concerned with demonstrating its utility for improving participants' knowledge and on-the-job performance. Such demonstrated program effects can be useful in obtaining grant support for programs or for promoting future self-sustaining programs. Many continuing library educators, however, have not had extensive training in educational evaluations. Practitioners may find some of the procedures reported here useful in their own program evaluations, especially with audiences for whom future programs will be developed. This article reports the results of three CLE workshop evaluations that measured participant satisfaction. knowledge and performance gains, and intentions to participate in a future program by the workshop's sponsor. The relationships among these three areas of interest are also reported. The evaluations also investigated reasons for non-participation in the workshops, an often-neglected aspect of program evaluation.
Towards building a scholarly big data platform: Challenges, lessons and opportunities

We introduce a Big Data platform that provides various services for harvesting scholarly information and enabling efficient scholarly applications. The core architecture of the platform is built on a secured private cloud, crawls data using a scholarly focused crawler that leverages a dynamic scheduler, processes by utilizing a map reduce based crawl-extraction-ingestion (CEI) workflow, and is stored in distributed repositories and databases. Services such as scholarly data harvesting, information extraction, and user information and log data analytics are integrated into the platform and provided by an OAI and RESTful API. We also introduce a set of scholarly applications built on top of this platform including citation recommendation and collaborator discovery.
Amyloidogenic processing of the Alzheimer β-amyloid precursor protein depends on lipid rafts

Formation of senile plaques containing the β-amyloid peptide (Aβ) derived from the amyloid precursor protein (APP) is an invariant feature of Alzheimer's disease (AD). APP is cleaved either by β-secretase or by α-secretase to initiate amyloidogenic (release of Aβ) or nonamyloidogenic processing of APP, respectively. A key to understanding AD is to unravel how access of these enzymes to APP is regulated. Here, we demonstrate that lipid rafts are critically involved in regulating Aβ generation. Reducing cholesterol levels in N2a cells decreased Aβ production. APP and the β-site APP cleavage enzyme (BACE1) could be induced to copatch at the plasma membrane upon cross-linking with antibodies and to segregate away from nonraft markers. Antibody cross-linking dramatically increased production of Aβ in a cholesterol-dependent manner. Aβ generation was dependent on endocytosis and was reduced after expression of the dynamin mutant K44A and the Rab5 GTPase-activating protein, RN-tre. This inhibition could be overcome by antibody cross-linking. These observations suggest the existence of two APP pools. Although APP inside raft clusters seems to be cleaved by β-secretase, APP outside rafts undergoes cleavage by α-secretase. Thus, access of α- and β-secretase to APP, and therefore Aβ generation, may be determined by dynamic interactions of APP with lipid rafts.
The Association between Carbohydrate-Rich Foods and Risk of Cardiovascular Disease Is Not Modified by Genetic Susceptibility to Dyslipidemia as Determined by 80 Validated Variants

Background It is still unclear whether carbohydrate consumption is associated with cardiovascular disease (CVD) risk. Genetic susceptibility might modify the associations between dietary intakes and disease risk. Objectives The aim was to examine the association between the consumption of carbohydrate-rich foods (vegetables, fruits and berries, juice, potatoes, whole grains, refined grains, cookies and cakes, sugar and sweets, and sugar-sweetened beverages) and the risk of incident ischemic CVD (iCVD; coronary events and ischemic stroke), and whether these associations differ depending on genetic susceptibility to dyslipidemia. Methods Among 26,445 individuals (44–74 years; 62% females) from the Malmö Diet and Cancer Study cohort, 2,921 experienced an iCVD event during a mean follow-up time of 14 years. At baseline, dietary data were collected using a modified diet history method, and clinical risk factors were measured in 4,535 subjects. We combined 80 validated genetic variants associated with triglycerides and HDL-C or LDL-C, into genetic risk scores and examined the interactions between dietary intakes and genetic risk scores on the incidence of iCVD. Results Subjects in the highest intake quintile for whole grains had a 13% (95% CI: 3–23%; p-trend: 0.002) lower risk for iCVD compared to the lowest quintile. A higher consumption of foods rich in added sugar (sugar and sweets, and sugar-sweetened beverages) had a significant cross-sectional association with higher triglyceride concentrations and lower HDL-C concentrations. A stronger positive association between a high consumption of sugar and sweets on iCVD risk was observed among those with low genetic risk score for triglycerides (p-interaction=0.05). Conclusion In this prospective cohort study that examined food sources of carbohydrates, individuals with a high consumption of whole grains had a decreased risk of iCVD. No convincing evidence of an interaction between genetic susceptibility for dyslipidemia, measured as genetic risk scores of dyslipidemia-associated variants, and the consumption of carbohydrate-rich foods on iCVD risk was observed.
Implementing Trust-to-Trust with Customer Edge Switching

A Network Address Translator allows hosts in a private address space to communicate with servers in the public Internet. There is no accepted solution for an arbitrary host from the public IP network to initiate communication with a host in a private address space although attempts have been made to create one. This paper proposes the replace NATs with a more comprehensive concept we call Customer Edge Switching (CES). Customer edge switching assumes connection state on the trust boundary between the user and the core networks. The connection state is managed by implicit signaling. The state gives means for the private network operator to apply elaborate access control to packet flows arriving from the Internet to the private network. CES is a way of moving from the end-to-end principle to the trust-to-trust principle advocated by Dave Clark.
Human occupancy recognition with multivariate ambient sensors

With advancement in sensors and Internet of Things, gathering spatiotemporal information from one's surroundings has become easier, to an extent that we can start to use sensor data to infer indoor occupancy patterns. This paper aims to identify which ambient sensor is the most dominant in recognising human presence. Four different types of off-the-shelf sensors from two manufacturers were deployed to ensure that we could collect the following data reliably: illumination, temperature, humidity, levels of carbon dioxide, pressure and sound from within one staff office. Motion, power consumption, door opening and closing data and annotations from a self-developed mobile app were also collected as ground truth. We present our methods to preprocess the data and compute the number of people in the room with different classifiers, and identify sensors with strong and weak correlations. We explain our methodology for integrating large amounts of sensor data, discuss our experiments and findings in relation to the binary occupancy of a single person office, providing a baseline for recognising human occupancy.
A polynomial algorithm for a bi-criteria cyclic scheduling problem.

The minimization of the amount of initial tokens in a Weighted Timed Event Graph (in short WTEG) under throughput constraint is a crucial problem in industrial area such as the design of manufacturing systems or embedded systems. In this paper, an efficient polynomial algorithm is developed for the minimization of the overall places capacities with a maximum throughput. It is proved optimal for a particular sub-classes of WTEG and is leading to a 2-approximation solution in the general case.
Simulation-based evaluation of susceptibility distortion correction methods in diffusion MRI for connectivity analysis

Connectivity analysis on diffusion MRI data of the whole-brain suffers from distortions caused by the standard echo-planar imaging acquisition strategies. These images show characteristic geometrical deformations and signal destruction that are an important drawback limiting the success of tractography algorithms. Several retrospective correction techniques are readily available. In this work, we use a digital phantom designed for the evaluation of connectivity pipelines. We subject the phantom to a “theoretically correct” and plausible deformation that resembles the artifact under investigation. We correct data back, with three standard methodologies (namely fieldmap-based, reversed encoding-based, and registration-based). Finally, we rank the methods based on their geometrical accuracy, the dropout compensation, and their impact on the resulting connectivity matrices.
From people to entities: typed search in the enterprise and the web

The exponential growth of digital information available in Enterprises and on the Web creates the need for search tools that can respond to the most sophisticated informational needs. Retrieving relevant documents is not enough anymore and finding entities rather than textual resources provides great support to the user both on the Web and in Enterprises. Many user tasks would be simplified if Search Engines would support typed search, and return entities instead of just Web pages. For example, an executive who tries to solve a problem needs to find people in the company who are knowledgeable about a certain topic. Aggregation of information spread over different documents is a key aspect in this process. Finding experts is a problem mostly considered in the Enterprise setting where teams for new projects need to be built and problems need to be solved by the right persons. In the first part of the thesis, we propose a model for expert finding based on the well consolidated vector space model for Information Retrieval and investigate its effectiveness. We can define Entity Retrieval by generalizing the expert finding problem to any entity. In Entity Retrieval the goal is to rank entities according to their relevance to a query (e.g., "Countries where I can pay in Euro"); the set of entities to be ranked is assumed to be loosely defined by a generic category, given in the query itself (e.g., countries), or by some example entities (e.g., Italy, Germany, France). In the second part of the thesis, we investigate different methods based on Semantic Web and Natural Language Processing techniques for solving these tasks both in Wikipedia and, generally, on the Web. Evaluation is a critical aspect of Information Retrieval. We contributed to the field of Information Retrieval evaluation by organizing an evaluation initiative for Entity Retrieval. Opinions and other relevant information about entities can be provided by different sources in different contexts. News articles report about events where entities are involved. In such setting the temporal dimension is critical as news stories develop over time and new entities appear in the story and others are not relevant anymore. In the third part of this thesis, we study the problem of Entity Retrieval for news applications and the importance of the news trail history (i.e., past related articles) to determine the relevant entities in current articles. We also study opinion evolution about entities. In the last years, the blogosphere has become a vital part of the Web, covering a variety of different points of view and opinions on political and event-related topics such as immigration, election campaigns, or economic developments. We propose a method for automatically extracting public opinion about specific entities from the blogosphere. Available online at http://www.gianlucademartini. net/research/phd/.
Energy efficient WiFi-based fingerprinting for indoor positioning with smartphones

The paper presents an energy efficient WiFi-based indoor positioning algorithm, based on the probabilistic fingerprinting method, suited to be used over smartphone platforms. The work proposes a simple algebraic approach aimed at reducing the computational and energy loads of the probabilistic fingerprinting, which is employed to carry out the position of a smartphone on the basis of the captured WiFi Access Points signal strengths in an indoor area. The presented solution does not apply any kind of approximation with respect to the traditional approach, so avoiding accuracy detriment. The idea is to factoring out the parts of the probabilistic fingerprint formulae that can be computed a-priori, so reducing the computational burden of the positioning process. The paper also highlights the effectiveness of the proposed solution by evaluating the energy saved in comparison with the traditional approach. The obtained results, collected by testing three different smartphones, show that our approach allows saving a significant quantity of energy so increasing the smartphones battery lifetime.