Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapamycin Response in Tumorigenic and Non-Tumorigenic Hepatic Cell Lines

Background The mTOR inhibitor rapamycin has anti-tumor activity across a variety of human cancers, including hepatocellular carcinoma. However, resistance to its growth inhibitory effects is common. We hypothesized that hepatic cell lines with varying rapamycin responsiveness would show common characteristics accounting for resistance to the drug. Methodology/Principal Findings We profiled a total of 13 cell lines for rapamycin-induced growth inhibition. The non-tumorigenic rat liver epithelial cell line WB-F344 was highly sensitive while the tumorigenic WB311 cell line, originally derived from the WB-F344 line, was highly resistant. The other 11 cell lines showed a wide range of sensitivities. Rapamycin induced inhibition of cyclin E–dependent kinase activity in some cell lines, but the ability to do so did not correlate with sensitivity. Inhibition of cyclin E–dependent kinase activity was related to incorporation of p27Kip1 into cyclin E–containing complexes in some but not all cell lines. Similarly, sensitivity of global protein synthesis to rapamycin did not correlate with its anti-proliferative effect. However, rapamycin potently inhibited phosphorylation of two key substrates, ribosomal protein S6 and 4E-BP1, in all cases, indicating that the locus of rapamycin resistance was downstream from inhibition of mTOR Complex 1. Microarray analysis did not disclose a unifying mechanism for rapamycin resistance, although the glycolytic pathway was downregulated in all four cell lines studied. Conclusions/Significance We conclude that the mechanisms of rapamycin resistance in hepatic cells involve alterations of signaling downstream from mTOR and that the mechanisms are highly heterogeneous, thus predicting that maintaining or promoting sensitivity will be highly challenging.
Some Completeness Results for Compactly Surjective Subgroups

Suppose κk,Z(ξ̄) ∼ ‖L ‖. M. Williams’s construction of unconditionally solvable sets was a milestone in Euclidean analysis. We show that F ⊃ −1. Next, in this context, the results of [24] are highly relevant. Recent developments in symbolic operator theory [24] have raised the question of whether Z < j.
MultiPARTES: Multicore Virtualization for Mixed-Criticality Systems

Modern embedded applications typically integrate a multitude of functionalities with potentially different criticality levels into a single system. Without appropriate preconditions, the integration of mixed-criticality subsystems can lead to a significant and potentially unacceptable increase of engineering and certification costs. A promising solution is to incorporate mechanisms that establish multiple partitions with strict temporal and spatial separation between the individual partitions. In this approach, subsystems with different levels of criticality can be placed in different partitions and can be verified and validated in isolation. The MultiPARTES FP7 project aims at supporting mixed-criticality integration for embedded systems based on virtualization techniques for heterogeneous multicore processors. A major outcome of the project is the MultiPARTES XtratuM, an open source hyper visor designed as a generic virtualization layer for heterogeneous multicore. MultiPARTES evaluates the developed technology through selected use cases from the offshore wind power, space, visual surveillance, and automotive domains. The impact of MultiPARTES on the targeted domains will be also discussed. In a number of ongoing research initiatives (e.g., RECOMP, ARAMIS, MultiPARTES, CERTAINTY) mixed-criticality integration is considered in multicore processors. Key challenges are the combination of software virtualization and hardware segregation and the extension of partitioning mechanisms to jointly address significant non-functional requirements (e.g., time, energy and power budgets, adaptivity, reliability, safety, security, volume, weight, etc.) along with development and certification methodology.
Topological black holes in anti-de Sitter space

We consider a class of black hole solutions to Einstein's equations in d dimensions with a negative cosmological constant. These solutions have the property that the horizon is a (d - 2)-dimensional Einstein manifold of positive, zero or negative curvature. The mass, temperature and entropy are calculated. Using the correspondence with conformal field theory, the phase structure of the solutions is examined, and used to determine the correct mass dependence of the Bekenstein-Hawking entropy.
Flexible ADCs for wireless mobile radios

The evolution of wireless mobile devices calls for "all- in-one-device", which support a large number of wireless standards and allows wireless connectivity and roaming. This creates the need for adaptive circuits that are able to reconfigure themselves to trade-off power dissipation for performance, depending on the wireless standard to be supported and the required quality of service. Therefore, the key features of such mobile devices are flexibility and adaptability. This paper presents the ADCs suitable for mobile radios, keeping focus on flexible architectures. Finally, a programmable ADCs array, which can digitize signal bands from cellular to UWB is presented.
The Anatomy of the Role of Morphological Awareness in Chinese Character Learning: The Mediation of Vocabulary and Semantic Radical Knowledge and the Moderation of Morpheme Family Size

ABSTRACT In the present study, the mediating roles of syllable awareness, orthographic knowledge, and vocabulary skills and the moderating role of morpheme family size in the association between morphological awareness and Chinese character reading were investigated with 176 second-grade Hong Kong Chinese children. In the path analyses, the results indicated significant partial mediating effects of semantic radical function knowledge and vocabulary skills but not of syllable awareness and pseudocharacter identification. A significant moderating effect of morpheme family size was also observed. Children with relatively poor compounding structure awareness performed much more poorly in reading Chinese characters that had a small family size. The mediating and moderating effects documented in the present study can help us understand the mechanism involved in the relationship between morphological awareness and Chinese character reading and provide evidence as a basis for modifying current instruction strategies for improving Chinese children’s reading performance.
The Production-Side Approach to Estimating Embodied Technological Change

We estimate the rate of embodied technological change directly from plant-level manufacturing data on current output and input choices along with histories on their vintages of equipment investment. Our estimates range between 8 and 17 percent for the typical U.S. manufacturing plant during the years 1972-1996. Any number in this range is substantially larger than is conventionally accepted with some important implications. First, the role of investment- specific technological change as an engine of growth is even larger than previously estimated. Second, existing producer durable price indices do not adequately account for quality change. As a result, measured capital stock growth is biased. Third, if accurate, the Hulten and Wykoff (1981) economic depreciation rates may primarily reflect obsolescence.
1 BlueBot : Asset Tracking via Robotic Location Crawling

LIMITED DISTRIBUTION NOTICE: This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g. , payment of royalties). Copies may be requested from IBM T. J. Abstract: Asset tracking – knowing what you have and where it is located – is essential for the smooth operation of many enterprises. From manufacturers, distributors, and retailers of consumer goods, to government departments, enterprises of all kinds are gearing up to use RFID technology to increase the visibility of goods and assets within their supply chain and on their premises. However, RFID technology alone lacks the capability to track the location of items once they are moved within a facility. This paper presents a prototype automatic location sensing system that combines RFID technology and off-the-shelf Wi-Fi based continuous positioning technology for asset tracking in indoor environments. The system employs a robot, with an attached RFID reader, which periodically crawls the space, associating items it detects with its own location determined with the Wi-Fi positioning system. We propose three algorithms that combine the detected tag's reading with previous samples to compute its location. Our experiments have shown that our positioning algorithms can bring a two to three fold improvement on the raw accuracy provided by the positioning technology.
Profiling the Maltese Entrepreneur : A model and preliminary findings

This study explored the relevance of specific factors that influence the development of entrepreneurs in the Maltese setting. The literature focused on exogenous factors such as family, education, culture, institutional support, as well as the political and economic climate. A model based on the literature was drawn and its applicability and generalizability amongst entrepreneurs in Malta was examined. The experiences of ten participants were studied through the use of in-depth interviews. The results showed generally that these factors are important in the formation and experiences of the Maltese entrepreneur. However, the relevance of each factor needs to be understood after a consideration of the appropriate realities of a micro-state like Malta.
Optimal virtual slice composition toward multi-tenancy over hybrid OCS/OPS data center networks

Multi-tenancy is a key feature of modern data centers. It allows for the existence of multiple independent virtual infrastructures, called virtual slices, on top of the same physical infrastructure, each of them specially tailored to the tenants' needs. In such a scenario, an optimal mapping of the virtual slices plays a capital role toward an efficient utilization of the data center network resources, potentially saving costs for the data center owner. However, due to the increasing trend of bringing optics to data center networks, specific virtual slice mapping mechanisms accounting for the particularities of the optical medium (e.g., wavelength continuity constraint) have to be investigated. For this, we present an integer linear programming (ILP) model for optimally mapping a set of virtual slices from different tenants in a hybrid optical circuit switching (OCS)/optical packet switching (OPS) data center network with the aim to minimize the necessary optical transponders to be equipped in the network. Additionally, we also present a lightweight heuristic for the cases in which the ILP model scalability is compromised. The benefits of the proposals are highlighted by benchmarking them against a pure OCS solution through extensive simulations.
Impact of hypoxia on heart rate variability based on sample entropy

In this study, changes in heart rate variability (H RV) induced by exposure to hypoxia were evaluate in healthy male. The sample entropy as a non-linear me thod of HRV analysis was used; it’s a powerful way to analyze biological system. Our aim was to investiga te the influence of stepwise hypoxia on HRV using sample entropy, we tested nine healthy yellow males (age=35± 5) at 3600m, 4000m, 4400m, 4800m during rest, these were a part of the acclimation t est. An increase in sample entropy at high altitude compared to 1000m was established in this research. The parameters showed similar tendencies during different altitude, but not so obvious. The changin g of sample entropy in every day was analyzed. Our results indicate that hypoxia exerts an influence o n HRV; it also suggested that acute exposure to normobaric hypoxia induces increases in sympathetic vasomotor activity and cardiac sympathetic dominance resulting in an increased heart rate.
On discretization of tori of compact simple Lie groups

Three types of numerical data are provided for simple Lie groups of any type and rank. These data are indispensable for Fourier-like expansions of multidimensional digital data into finite series of C- or S-functions on the fundamental domain F of the underlying Lie group G. Firstly, we determine the number |FM| of points in F from the lattice P∨M, which is the refinement of the dual weight lattice P∨ of G by a positive integer M. Secondly, we find the lowest set ΛM of dominant weights, specifying the maximal set of C- and S-functions that are pairwise orthogonal on the point set FM. Finally, we describe an efficient algorithm for finding, on the maximal torus of G, the number of conjugate points to every point of FM. Discrete C- and S-transforms, together with their continuous interpolations, are presented in full generality.
Commercializing technological research and skills: drivers from European technology institutes

Europe, a world-player in research and technology development, is lacking the ability to turn scientific research and skills into economic and social wealth through innovation. Technology Institutes (TIs) are potential catalysts and exist as an interface between science and industry. TIs have a potentially powerful influence over economic and technological development through their contributions to industrial innovation. Therefore, understanding TIs’ drivers of success is of great value. This study aims to analyse the most commercially successful European TIs to identify common drivers and best practices. The analysis focuses on drivers associated with customers, commercial staff, research strategy, S&T Policies, impact, and external factors. Some key drivers and best practices identified are: the inclusion of future needs of industry in research strategy; commercialization as a corporate-level activity, and specialized management.
ENERGY EFFICIENT SLEEP SCHEDULING WITH QOS CONSIDERATION IN NEXT GENERATION WIRELESS NETWORKS

Next generation wireless networks are probably the most promising technology for next generation mobile communications. For mobile applications, continuous communications at the User Equipment’s (UEs) over a long period of time, imposing stringent requirements on power saving. To manage power consumption, has defined the Discontinuous Reception (DRX) mechanism to allow UEs to turn off their radio interfaces and go to sleep in various patterns. Existing literature has paid much attention to evaluate the performance of DRX; however, how to tune DRX parameters to optimize energy cost is still left open. This work addresses the optimization problem of the DRX mechanism, by asking how to minimize the wake-up periods of the UEs while guarantee their QoS, especially on the aspects of traffic bit-rate, packet delay, and packet loss rate in mobile applications. Efficient schemes to optimize DRX parameters and schedule UEs' packets at the evolved NodeB (eNB) are proposed. The key idea of these schemes is to analyses and balances the impacts between QoS parameters and DRX configurations. Simulation results show that scheme can fully satisfy QoS requirements of the UEs while save considerable energy, compared to the existing schemes IndexTerms—Long Term Evolution-Advanced(LTE-Advanced), Discontinuous Reception/Transmission (DRX/DTX), Power Saving, Quality of Service, Sleep Scheduling.
Inference of Conversation Partners by Cooperative Acoustic Sensing in Smartphone Networks

A lot of personal daily contexts and activities may be inferred by analyzing acoustic signals in vicinity. Conversations play an important role in one's social communications. In this work, we consider the inference of conversation partners via acoustic sensing conducted by a group of smartphones in vicinity. By considering the continuity and overlap of speeches, we propose novel inference methods to identify conversational relationships among co-located users. In our system, each smartphone individually processes the acoustic data to understand its owner's talking turns and emotions. Via direct wireless communications, smartphones then cooperatively conduct the inference to retrieve conversational groups. Compared to existing work, which only exploits peer-to-peer conversational relationships, our approach is able to capture group conversational relationships in a more real-time manner. A prototype on Android smartphones is demonstrated to verify the feasibility of our approach. We also collect conversation data from movie clips and real life with 2 to 14 speakers to validate our result, which shows promising performance.
Adaptive Statistical Iterative Reconstruction-Applied Ultra-Low-Dose CT with Radiography-Comparable Radiation Dose: Usefulness for Lung Nodule Detection

Objective To assess the performance of adaptive statistical iterative reconstruction (ASIR)-applied ultra-low-dose CT (ULDCT) in detecting small lung nodules. Materials and Methods Thirty patients underwent both ULDCT and standard dose CT (SCT). After determining the reference standard nodules, five observers, blinded to the reference standard reading results, independently evaluated SCT and both subsets of ASIR- and filtered back projection (FBP)-driven ULDCT images. Data assessed by observers were compared statistically. Results Converted effective doses in SCT and ULDCT were 2.81 ± 0.92 and 0.17 ± 0.02 mSv, respectively. A total of 114 lung nodules were detected on SCT as a standard reference. There was no statistically significant difference in sensitivity between ASIR-driven ULDCT and SCT for three out of the five observers (p = 0.678, 0.735, < 0.01, 0.038, and < 0.868 for observers 1, 2, 3, 4, and 5, respectively). The sensitivity of FBP-driven ULDCT was significantly lower than that of ASIR-driven ULDCT in three out of the five observers (p < 0.01 for three observers, and p = 0.064 and 0.146 for two observers). In jackknife alternative free-response receiver operating characteristic analysis, the mean values of figure-of-merit (FOM) for FBP, ASIR-driven ULDCT, and SCT were 0.682, 0.772, and 0.821, respectively, and there were no significant differences in FOM values between ASIR-driven ULDCT and SCT (p = 0.11), but the FOM value of FBP-driven ULDCT was significantly lower than that of ASIR-driven ULDCT and SCT (p = 0.01 and 0.00). Conclusion Adaptive statistical iterative reconstruction-driven ULDCT delivering a radiation dose of only 0.17 mSv offers acceptable sensitivity in nodule detection compared with SCT and has better performance than FBP-driven ULDCT.
Pop, heavy metal and the blues: secondary analysis of persistent organic pollutants (POP), heavy metals and depressive symptoms in the NHANES National Epidemiological Survey

Objectives Persistent environmental pollutants, including heavy metals and persistent organic pollutants (POPs), have a ubiquitous presence. Many of these pollutants affect neurobiological processes, either accidentally or by design. The aim of this study was to explore the associations between assayed measures of POPs and heavy metals and depressive symptoms. We hypothesised that higher levels of pollutants and metals would be associated with depressive symptoms. Setting National Health and Nutrition Examination Survey (NHANES). Participants A total of 15 140 eligible people were included across the three examined waves of NHANES. Primary and secondary outcome measures Depressive symptoms were assessed using the nine-item version of the Patient Health Questionnaire (PHQ-9), using a cut-off point of 9/10 as likely depression cases. Organic pollutants and heavy metals, including cadmium, lead and mercury, as well as polyfluorinated compounds (PFCs), pesticides, phenols and phthalates, were measured in blood or urine. Results Higher cadmium was positively associated with depression (adjusted Prevalence Ratios (PR)=1.48, 95% CI 1.16 to 1.90). Higher levels of mercury were negatively associated with depression (adjusted PR=0.62, 95% CI 0.50 to 0.78), and mercury was associated with increased fish consumption (n=5500, r=0.366, p<0.001). In addition, several PFCs (perfluorooctanoic acid, perfluorohexane sulfonic acid, perfluorodecanoic acid and perfluorononanoic acid) were negatively associated with the prevalence of depression. Conclusions Cadmium was associated with an increased likelihood of depression. Contrary to hypotheses, many of persistent environmental pollutants were not associated or negatively associated with depression. While the inverse association between mercury and depressive symptoms may be explained by a protective role for fish consumption, the negative associations with other pollutants remains unclear. This exploratory study suggests the need for further investigation of the role of various agents and classes of agents in the pathophysiology of depression.
HUMAN ACCURACY ANAYLSIS ON THE AMAZON MECHANICAL TURK

Platforms such as the Amazon Mechanical Turk (AMT) make it easy and cheap to gather human input for machine learning problems. The flipside, however, is that some human contributors can be confused or inaccurate. The challenge then becomes to extract trustworthy information from the answers of more or less accurate contributors. Image-Net is an effort to organize images collected online into hierarchal synonym sets (synsets), such as dog or jigsaw puzzle. Image-Net then employs AMT turkers to label whether the images it has gathered are actually typical representatives of the synset they were grouped under. Since the turker labels are not 100% accurate, Image-Net currently determines whether an image is correctly labeled by majority vote. In this project, we consider machine learning algorithms that could enable us to identify the more accurate turkers, and to recognize more typical representatives of synsets using the available AMT votes. First, we present an unsupervised EM algorithm that assigns accuracies to individual turkers and assumes that their votes are Bernoulli-distributed. Second, we present a supervised algorithm that identifies a few dozen key images, which we correctly label, and then uses this information to identify trustworthy turkers. The supervised approach produced some very promising results, especially in synsets with ambiguous definitions, such as bishop (the chess piece); the unsupervised results were a bit more mixed. In both our approaches, we make the following assumptions: first, we assume that each of our voters has a consistent accuracy level; second, we assume that the majority is more often correct than not. We then attempt to identify turkers who vote with the majority most often, infer that they are our best labelers, and follow these trusted labellers in ambiguous cases.