Miracle-dz/UniFAR-FTUs huggingface dataset:
Modulation of the Tumor Microvasculature by Phosphoinositide-3 Kinase Inhibition Increases Doxorubicin Delivery In Vivo

Purpose: Because effective drug delivery is often limited by inadequate vasculature within the tumor, the ability to modulate the tumor microenvironment is one strategy that may achieve better drug distribution. We have previously shown that treatment of mice bearing tumors with phosphoinositide-3 kinase (PI3K) inhibitors alters vascular structure in a manner analogous to vascular normalization and results in increased perfusion of the tumor. On the basis of that result, we asked whether inhibition of PI3K would improve chemotherapy delivery. Experimental Design: Mice with xenografts using the cell line SQ20B bearing a hypoxia marker or MMTV-neu transgenic mice with spontaneous breast tumors were treated with the class I PI3K inhibitor GDC-0941. The tumor vasculature was evaluated by Doppler ultrasound, and histology. The delivery of doxorubicin was assessed using whole animal fluorescence, distribution on histologic sections, high-performance liquid chromatography on tumor lysates, and tumor growth delay. Results: Treatment with GDC-0941 led to approximately three-fold increases in perfusion, substantially reduced hypoxia and vascular normalization by histology. Significantly increased amounts of doxorubicin were delivered to the tumors correlating with synergistic tumor growth delay. The GDC-0941 itself had no effect on tumor growth. Conclusion: Inhibition of PI3K led to vascular normalization and improved delivery of a chemotherapeutic agent. This study highlights the importance of the microvascular effects of some novel oncogenic signaling inhibitors and the need to take those changes into account in the design of clinical trials many of which use combinations of chemotherapeutic agents. Clin Cancer Res; 18(1); 161–9. ©2011 AACR.
A Hybrid Algorithm for Solving Two-Part Division Problem in Network Community Detection

Network representation is a convenient and intuitive abstraction for analyzing the massive interacting data. Some topological characteristics of the network have been found in the past decade, and community structure is the typical one of them. Community detection has become a hot topic in complex network analysis. In the paper, a hybrid algorithm is presented for solving such problem. At first, we take the max-degree node as the "core" node. Then, the diffusing operation is per-formed on it to produce two preliminary partitions. Subsequently, an EO-based adjustment step is used for generating the final partitioning with high quality. In addition, four real-world networks are used for validating the efficiency and effectiveness of our hybrid algorithm. The experimental results show that the proposed hybrid algorithm is a promising solution for solving the community detection problem both in precision and efficiency.
Spatially Adaptive Image Denoising using Undecimated Directionlet Transform

paper a novel de-noising method based on directionlet transform and on sub band adaptive Bayesian threshold is presented. The denoising scheme used in wavelet domain has been extended to the directionlet domain to make the image features to concentrate on fewer coefficients so that more effective thresholding is possible. Here the directionality of the spatially segmented image is first computed using a parameter called directional variance for selecting the optimum direction for decomposing the image using undecimated directionlet transform. The decomposed images with directional energy are used for threshold computation using Bayes scheme. This threshold is then applied to the sub- bands except the LLL subband. The threshold corrected sub- bands with the unprocessed first sub-band are given as input to the inverse directionlet algorithm for getting the de-noised image. Experimental results show that the proposed method outperforms the standard wavelet-based denoising methods in terms of perceptual and numerical estimates.
Relay Placement for Physical Layer Security: A Secure Connection Perspective

This work studies the problem of secure connection in cooperative wireless communication with two relay strategies, decode-and-forward (DF) and randomize-and-forward (RF). The four-node scenario and cellular scenario are considered. For the typical four-node (source, destination, relay, and eavesdropper) scenario, we derive the optimal power allocation for the DF strategy and find that the RF strategy is always better than the DF to enhance secure connection. In cellular networks, we show that without relay, it is difficult to establish secure connections from the base station to the cell edge users. The effect of relay placement for the cell edge users is demonstrated by simulation. For both scenarios, we find that the benefit of relay transmission increases when path loss becomes severer.
Extraction of Adverse Drug Effects from Medical Case Reports

A sheer amount of information about adverse effects of drugs are published in medical case reports that pose major challenges for drug safety experts to perform timely monitoring. Efﬁcient strategies for identiﬁcation and extraction of information about adverse drug effects from free-text resources are needed to support pharmacovigilance research and decision making. Therefore, this work focusses on the adaptation of a machine learning-based relation extraction system for the identiﬁcation and extraction of drug-related adverse effects from MEDLINE case reports. It relies on a high quality corpus that was manually annotated, using ontology-driven methodology. Qualitative evaluation of the system show robust results.
Optimal Hydrothermal Energy Generation for Ghana

Power production and distribution in Ghana is ever more becoming erratic and expensive, both for the power producer and the consumer. It is in this regard that an investigation of hydrothermal power generation scheduling is undertaken for a major power producer in the country. The goal of the study was to determine an optimal power production schedule that meets daily load demands at minimum cost of production and also ascertain the marginal cost of producing electricity per day and therefore tariff rate. The problem was formulated as Mixed Integer Linear Programming (MILP) and the resulting model tested using real data obtained from a major power producer in Ghana. The test results show that daily load demands could be met at a minimum cost. Furthermore, the marginal cost of producing power obtained from the dual of the MILP model provided insight into the appropriate Tariff that is reasonable for the power producer to charge consumers.
A Single Mode High-Index Core Bragg Fiber

In this paper, we have presented a single-mode high-index Bragg fiber having two zero dispersion wavelengths one at 813 nm with a dispersion slope of 0.2745 ps/nm2 km and another one at 1550 nm. The Bragg fiber has a high-index core made up of silica and is consisting of alternating layers of air and silica. Due to its low intrinsic losses (<1 dB/km), the fiber is suitable for telecom applications in visible range and nontelecom applications due to its ultraflat anomalous dispersion.
$Ka/Ku$ -Band pHEMT Gilbert Mixers With Polyphase and Coupled-Line Quadrature Generators

In this paper, three kinds of Kalpha/Ku-band Gilbert mixers are demonstrated using 0.15- mum AlGaAs/InGaAs pseudomorphic high electron mobility transistor technology. Thanks to the semiinsulating GaAs substrate, microwave passive components have a low-loss feature, and polyphase filters work up to higher frequencies. Highly accurate tantalum-nitride thin-film resistors utilized in polyphase filters result in perfect quadrature operation. Therefore, our proposed single-sideband up-converter operates at 15 GHz with a 63-dB sideband rejection ratio, and another 34-GHz in-phase/quadrature (I/Q) subharmonic down-converter reaches <0.4-dB magnitude and < 1deg phase errors. More than 50-dB local oscillator (LO) leakage suppression is achieved in the I/Q subharmonic mixer. On the other hand, a 40-GHz stacked-LO subharmonic mixer with a novel compensation technique is also proposed and demonstrated to improve LO speed and reduce the amount of transistors, as compared to previous work.
Multi-modal biometrics fusion: beyond optimal weighting

The multivariate polynomials model provides an effective way to describe complex nonlinear input-output relationships as it is tractable for optimization, sensitivity analysis, and prediction of confidence intervals. However, for high dimensional and high order problems, multivariate polynomial regression becomes impractical due to its prohibitive number of product terms. This is especially true for the case of a full interaction model. In this paper, we propose a reduced multivariate polynomials model to circumvent the dimensionality problem with some compromise in the approximation capability. When applied to multi-modal biometrics fusion, this mode! is demonstrated to improve the combined classification performance in terms of classification accuracy.
Seven years on: insights from children’s developing perspectives in interpreting the wisdom of the biblical creation narrative

Seven years ago, in 2004–2005, research was carried out into children’s perspectives as they encounter sacred text. Seven years on, these same children and young people were contacted with a similar interview and the results are offered. In order to comment on the meaning-making interpretative process that children employ at different key stages of development, the insights of hermeneutic phenomenology and of developmental psychology are used detailing their former stage and seven years later. At each of the British educational Key Stages 2, 3 and 4, children’s perspectives of their world views and scriptural understanding are given alongside hermeneutical commentary.
Deutsch, Doyc or Doitsch? Chatters as Languagers – The Case of a German–Turkish Chat Room

Abstract The paper looks at the process of online-languaging of adolescents with a migratory and ethnic Turkish background in a diasporic chat room. The multiple resources and voices used are analysed. One particular example is looked at more thoroughly to illustrate the process of languaging and how it is legitimised by the chatters themselves. Finally, it is briefly looked at the commitment involved in the languaging and why this process is rather one-sided German-inclined.
Adaptive Discrete-Rate MIMO Communications with Rate-Compatible LDPC Codes

By using rate-compatible (RC) low density parity-check (LDPC) codes with adaptive modulation, we propose an adaptive, discrete-rate multiple-input multiple-output (MIMO) communications system. Given the high spectral efficiency of MIMO and the flexibility of an incremental redundancy (IR) protocol, combined with adaptive coding and modulation (ACM), the designed communications system is capable of achieving high data rates, for a low amount of overhead. A novel ACM power- and bit-allocation protocol is proposed to implement this system. We adapt the existing water-filling algorithm (WFA) to the discrete and finite bit rate constraints inherent in any communications system. This constrained WFA is shown to significantly improve the throughput performance of the communications system, over the case where a regular WFA is used. The results given in this paper show that the combination of IR and ACM with MIMO creates a wireless communications system that can easily adapt to channel fluctuations and provide high-data rates.
Monetary Assessment of Battery Life on Smartphones

Research claims that users value the battery life of their smartphones, but no study to date has attempted to quantify battery value and how this value changes according to users' current context and needs. Previous work has quantified the monetary value that smartphone users place on their data (e.g., location), but not on battery life. Here we present a field study and methodology for systematically measuring the monetary value of smartphone battery life, using a reverse second-price sealed-bid auction protocol. Our results show that the prices for the first and last 10% battery segments differ substantially. Our findings also quantify the tradeoffs that users consider in relation to battery, and provide a monetary model that can be used to measure the value of apps and enable fair ad-hoc sharing of smartphone resources.
Route Planning for Unmanned Air Vehicles with Multiple Missions Using an Evolutionary Algorithm

In this paper, the route-planning problem for unmanned air vehicles with multiple missions is studied with the proposal of a novel evolutionary route planner. In the new planner, the individual candidates are evaluated with respect to the workspace. Therefore the computation of the configuration space is avoided. With digital terrain elevation data, our approach can find a near-optimal route that can increase the surviving probability efficiently. By using a problem-specific representation of candidate solutions and genetic operators, our algorithm can take into account different kinds of mission constraints and generate the solutions in real-time.
Q methodology.

Q methodology is more than just a technique for assessing attitudes. It is an approach which has subjectivity as its central focus and aims to allow researchers to present the views and perspectives of different people without insisting on the 'superior', more 'objective' status of the researcher's own construction of reality ( 1 ). It is a way of examining the world from the internal standpoint of an individual.
PTEN Protein Loss by Immunostaining: Analytic Validation and Prognostic Indicator for a High Risk Surgical Cohort of Prostate Cancer Patients

Purpose: Analytically validated assays to interrogate biomarker status in clinical samples are crucial for personalized medicine. PTEN is a tumor suppressor commonly inactivated in prostate cancer that has been mechanistically linked to disease aggressiveness. Though deletion of PTEN, as detected by cumbersome FISH spot counting assays, is associated with poor prognosis, few studies have validated immunohistochemistry (IHC) assays to determine whether loss of PTEN protein is associated with unfavorable disease. Experimental Design: PTEN IHC was validated by employing formalin fixed and paraffin-embedded isogenic human cell lines containing or lacking intact PTEN alleles. PTEN IHC was 100% sensitive and 97.8% specific for detecting genomic alterations in 58 additional cell lines. PTEN protein loss was then assessed on 376 prostate tumor samples, and PTEN FISH or high resolution single nucleotide polymorphism microarray analysis was done on a subset of these cases. Results: PTEN protein loss, as assessed as a dichotomous IHC variable, was highly reproducible, correlated strongly with adverse pathologic features (e.g., Gleason score and pathologic stage), detected between 75% and 86% of cases with PTEN genomic loss, and was found at times in the absence of apparent genomic loss. In a cohort of 217 high risk surgically treated patients, PTEN protein loss was associated with decreased time to metastasis. Conclusion: These studies validate a simple method to interrogate PTEN status in clinical specimens and support the utility of this test in future multicenter studies, clinical trials, and ultimately perhaps for routine clinical care. Clin Cancer Res; 17(20); 6563–73. ©2011 AACR.
Reducing Mutual Coupling of Closely Spaced Microstrip MIMO Antennas for WLAN Application

An efficient mutual coupling reduction method is introduced in this letter for extremely closely placed dual-element microstrip antennas positioned on a finite-sized ground plane for WLAN MIMO application at 5.8 GHz. High isolation can be achieved through a simple slot structure on the ground between the microstrip antennas. The position, length, and width of the slot have been optimized for maximizing the isolation. It is found that more than 40 dB isolation can be achieved between two parallel microstrip antennas sharing a common ground plane. The space distance of these antennas is 17.5 mm ≈ 0. 33λ0 from element center to center (side by side of 1.6 mm ≈ 0.031λ0) when the ground plane size is 0. 85λ0 × 0. 55λ0. Along with this letter, several prototypes were fabricated, and their performances measured to validate the obtained IE3D moment method-based simulation results.
Differentially Private Moving Object Database Publication in Location Tracking Service

Location tracking applications which receives frequent updates of a moving object's position, collect numerous moving objects' location data. Public transit agencies can make use of tracking data to optimize traffic control strategies. While improper use of trajectory data could cause individuals' privacy leakage. However, existing privacy-preserving techniques are unable to provide sufficient privacy protection. In this paper, we propose a data-dependent differentially private sanitization algorithm to publish moving object database. Moreover, we make use of a set of real-world constraints to conduct constraint inference, which can boost the utility of the published data. At last, we experimentally evaluate the utility of the sanitized data in terms of range-count queries, results show high utility and efficiency of our proposal.
EXACT SOLUTIONS IN OPEN BOSONIC STRING FIELD THEORY AND MARGINAL DEFORMATION IN CFT

In this paper we continue our study of the exact solution in open bosonic string field theory. We present new solution in the string field theory defined on the background corresponding to the boundary conformal field theory describing D25-brane. Then we will study the fluctuation modes around this solution and we determine their basic properties from the linearized equation of motion of the string field theory defined above the classical solution.
Promoting Activity in Geriatric Rehabilitation: A Randomized Controlled Trial of Accelerometry

Background Low activity levels in inpatient rehabilitation are associated with adverse outcomes. The study aimed to test whether activity levels can be increased by the provision of monitored activity data to patients and clinicians in the context of explicit goal setting. Methods A randomized controlled trial in three sites in Australia included 255 inpatients aged 60 and older who had a rehabilitation goal to become ambulant. The primary outcome was patients’ walking time measured by accelerometers during the rehabilitation admission. Walking times from accelerometry were made available daily to treating therapists and intervention participants to motivate patients to improve incidental activity levels and reach set goals. For the control group, ‘usual care’ was followed, including the setting of mobility goals; however, for this group, neither staff nor patients received data on walking times to aid the setting of daily walking time targets. Results The median daily walking time in the intervention group increased from 10.3 minutes at baseline to 32.1 minutes at day 28, compared with an increase from 9.5 to 26.5 minutes per day in the control group. Subjects in the intervention group had significantly higher non-therapy walking time by about 7 minutes [mean (95% CI): 24.6 (21.7, 27.4)] compared to those in the control group [mean(95% CI): 17.3 (14.4, 20.3)] (p = 0.001). Conclusions Daily feedback to patients and therapists using an accelerometer increased walking times during rehabilitation admissions. The results of this study suggest objective monitoring of activity levels could provide clinicians with information on clinically important, mobility-related activities to assist goal setting. Trial Registration Australian New Zealand Clinical Trials Registry ACTRN12611000034932 http://www.ANZCTR. org.au/
Les théories migratoires contemporaines au prisme des textes fondateurs

This article aims to explore the development of contemporary migration theories as reflected in some twenty founding texts brought together for the first time in a single volume (Piche V., 2013, Les theories de la migration, INED). Each text marks a major advance in the understanding of migration, its causes and its effects. Together, they bear witness to the emergence of theories which, after initially focusing on micro-individual approaches centred on cost-benefit analysis, gradually move on to incorporate macro-structural factors. Analysis of migration networks is a key component of explanatory frameworks and of studies to determine the effects of migration on economic development. A gendered approach to migration decision-making rounds off this analysis. The analytical framework developed here presents migration as a multifactorial and multidimensional phenomenon combining three main dimensions: origin and destination; micro, meso, macro and global analysis levels; economic, social and political aspects. Far from competing, these approaches each provide specific new insights. They must all be considered when seeking to explain migration or to assess migration policy.
Learning to Walk through Imitation

Programming a humanoid robot to walk is a challenging problem in robotics. Traditional approaches rely heavily on prior knowledge of the robot's physical parameters to devise sophisticated control algorithms for generating a stable gait. In this paper, we provide, to our knowledge, the first demonstration that a humanoid robot can learn to walk directly by imitating a human gait obtained from motion capture (mocap) data. Training using human motion capture is an intuitive and flexible approach to programming a robot but direct usage of mocap data usually results in dynamically unstable motion. Furthermore, optimization using mocap data in the humanoid full-body joint-space is typically intractable. We propose a new modelfree approach to tractable imitation-based learning in humanoids. We represent kinematic information from human motion capture in a low dimensional subspace and map motor commands in this lowdimensional space to sensory feedback to learn a predictive dynamic model. This model is used within an optimization framework to estimate optimal motor commands that satisfy the initial kinematic constraints as best as possible while at the same time generating dynamically stable motion. We demonstrate the viability of our approach by providing examples of dynamically stable walking learned from mocap data using both a simulator and a real humanoid robot.
A factorization perspective for learning representations in reinforcement learning

Reinforcement learning is a general formalism for sequential decision-making, with recent algorithm development focusing on function approximation to handle large state spaces and high-dimensional, high-velocity (sensor) data. The success of function approximators, however, hinges on the quality of the data representation. In this work, we explore representation learning within batch reinforcement learning, with a focus on making the assumptions on the representation explicit and making the learning problem amenable to principled optimization techniques. We specify a reinforcement learning objective for value function learning that facilitates the addition of a regularized matrix factorization objective to specify the desired class of representations. The resulting joint optimization over the representation and value function parameters enables us to take advantages of recent advances in unsupervised learning and presents a general yet simple formalism for learning representations in reinforcement learning.