Miracle-dz/UniFAR-FTUs huggingface dataset:
DISORDER / SETTING Question 1 : What is the specific clinical disorder to be studied ? Question 2 : What are the clinical findings defining this disorder ?

Question 1: What is the specific clinical disorder to be studied? Question 2: What are the clinical findings defining this disorder? Question 3: What is the clinical setting in which the test is to be performed? Question 4: What DNA test(s) are associated with this disorder? Question 5: Are preliminary screening questions employed? Question 6: Is it a stand-alone test or is it one of a series of tests? Question 7: If it is part of a series of screening tests, are all tests performed in all instances (parallel) or are some tests performed only on the basis of other results (series)? Summary • Excluding non-melanoma skin cancer, breast cancer is the most common form of cancer and is the second most common cause of cancer deaths in women � 250,000 women will be diagnosed each year � 39,400 women will die � 809,000 person-years of life will be lost • 5 to10 percent of breast cancer cases are associated with an autosomal pattern of inheritance, and one of the causes is known to be mutations in the BRCA1/2 genes • BRCA1/2 mutations are also associated with ovarian cancer, and for this reason, breast cancer and ovarian cancer need to be considered together • Women identified with a BRCA1/2 mutation have a predisposition to developing ovarian cancer and/or early onset breast cancer The primary clinical disorder being studied in this report is breast cancer in women. However, since this report focuses on testing for mutations in the genes BRCA1 (breast cancer gene 1) and BRCA2 (breast cancer gene 2) that predispose women to both breast and ovarian cancer, ovarian cancer will also be reviewed. Excluding non-melanoma skin cancers, breast cancer is the most common form of cancer among women in the United States. The American Cancer Society estimates that in 2002 about 203,500 new cases of invasive breast cancer and 54,300 cases of in situ breast cancer will be diagnosed among women in the United States. (2002) It is estimated that 39,400 women will die of breast cancer this year, ranking it second among cancer deaths in women, exceeded only by lung cancer. Although not as common as breast cancer, ovarian cancer accounts for nearly 4 percent of all cancers among women (23,400 diagnosed cases) and is estimated by the American Cancer Society to cause 13,900 deaths in 2002. Ovarian cancer has the highest mortality rate of all reproductive system …
Oleic acid-induced lung injury: thin-section CT evaluation in dogs.

PURPOSE To validate lung attenuation measurements for quantifying extravascular lung water in oleic acid-induced pulmonary edema, compare subjective assessment with attenuation measurements, and compare this permeability-type pulmonary edema with hydrostatic-type pulmonary edema. MATERIALS AND METHODS Thin-section computed tomography (CT) and pulmonary hemodynamic examinations were performed sequentially in six dogs before and after intravenous administration of 0.08 mg of oleic acid per kilogram of body weight. Extravascular lung water and pulmonary capillary pressure were measured. Results were compared with those reported in a canine model of hydrostatic edema. RESULTS Oleic acid induced a progressive increase in extravascular lung water without a change in capillary pressure, which indicated pure permeability-type edema. Ground-glass opacification was detected as soon as extravascular lung water increased. Lung attenuation was highly correlated to extravascular lung water (r = 0.76, P<.001), as in hydrostatic edema, but was characterized by an almost absent gravitational gradient. CONCLUSION Thin-section CT is sensitive for early detection and quantification of oleic acid-induced pulmonary edema in a canine model. Different from early canine hydrostatic edema, which is characterized by a gravitational gradient, early oleic acid-induced pulmonary edema in a supine dog is characterized by nearly homogeneous distribution, except for ventral sparing.
TCP ex machina: computer-generated congestion control

This paper describes a new approach to end-to-end congestion control on a multi-user network. Rather than manually formulate each endpoint's reaction to congestion signals, as in traditional protocols, we developed a program called Remy that generates congestion-control algorithms to run at the endpoints. In this approach, the protocol designer specifies their prior knowledge or assumptions about the network and an objective that the algorithm will try to achieve, e.g., high throughput and low queueing delay. Remy then produces a distributed algorithm---the control rules for the independent endpoints---that tries to achieve this objective. In simulations with ns-2, Remy-generated algorithms outperformed human-designed end-to-end techniques, including TCP Cubic, Compound, and Vegas. In many cases, Remy's algorithms also outperformed methods that require intrusive in-network changes, including XCP and Cubic-over-sfqCoDel (stochastic fair queueing with CoDel for active queue management). Remy can generate algorithms both for networks where some parameters are known tightly a priori, e.g. datacenters, and for networks where prior knowledge is less precise, such as cellular networks. We characterize the sensitivity of the resulting performance to the specificity of the prior knowledge, and the consequences when real-world conditions contradict the assumptions supplied at design-time.
A Microfabricated 1-D Metamaterial Unit Cell Matched From DC to Millimeter-Waves

A silicon-integrated one-dimensional CRLH-TL metamaterial unit cell is presented. Ultra broadband behavior is achieved by implementing the previously proposed `lattice network' CRLH-TL unit cell in a monolayer host transmission line, as required for simple micromachining fabrication. Measurements demonstrate a so-called balanced composite right/left-handed behavior from 5 to 35 GHz, with a transition zero-phase frequency at 20 GHz. The lower measured frequency of 5 GHz is limited by the on-wafer calibration but the very principle of the `lattice network' CRLH-TL guaranties its operation down to dc.
Targeted deletion of the murine corneodesmosin gene delineates its essential role in skin and hair physiology

Controlled proteolytic degradation of specialized junctional structures, corneodesmosomes, by epidermal proteases is an essential process for physiological desquamation of the skin. Corneodesmosin (CDSN) is an extracellular component of corneodesmosomes and, although considerable debate still exists, genetic studies have suggested that the CDSN gene in the major psoriasis-susceptibility locus (PSORS1) may be responsible for susceptibility to psoriasis, a human skin disorder characterized by excessive growth and aberrant differentiation of keratinocytes. CDSN is also expressed in the inner root sheath of hair follicles, and a heterozygous nonsense mutation of the CDSN gene in humans is associated with scalp-specific hair loss of poorly defined etiology. Here, we have investigated the pathogenetic roles of CDSN loss of function in the development of skin diseases by generating a mouse strain with targeted deletion of the Cdsn gene. Cdsn-deficient mouse skin showed detachment of the stratum corneum from the underlying granular layer and/or detachment within the upper granular layers due to the disrupted integrity of the corneodesmosomes. When grafted onto immunodeficient mice, Cdsn-deficient skin showed rapid hair loss together with epidermal abnormalities resembling psoriasis. These results underscore the essential roles of CDSN in hair physiology and suggest functional relevance of CDSN gene polymorphisms to psoriasis susceptibility.
MedLDA: maximum margin supervised topic models

A supervised topic model can use side information such as ratings or labels associated with documents or images to discover more predictive low dimensional topical representations of the data. However, existing supervised topic models predominantly employ likelihood-driven objective functions for learning and inference, leaving the popular and potentially powerful max-margin principle unexploited for seeking predictive representations of data and more discriminative topic bases for the corpus. In this paper, we propose the maximum entropy discrimination latent Dirichlet allocation (MedLDA) model, which integrates the mechanism behind the max-margin prediction models (e.g., SVMs) with the mechanism behind the hierarchical Bayesian topic models (e.g., LDA) under a unified constrained optimization framework, and yields latent topical representations that are more discriminative and more suitable for prediction tasks such as document classification or regression. The principle underlying the MedLDA formalism is quite general and can be applied for jointly max-margin and maximum likelihood learning of directed or undirected topic models when supervising side information is available. Efficient variational methods for posterior inference and parameter estimation are derived and extensive empirical studies on several real data sets are also provided. Our experimental results demonstrate qualitatively and quantitatively that MedLDA could: 1) discover sparse and highly discriminative topical representations; 2) achieve state of the art prediction performance; and 3) be more efficient than existing supervised topic models, especially for classification.
Practical Guidance for Implementing Predictive Biomarkers into Early Phase Clinical Studies

The recent U.S. Food and Drug Administration (FDA) coapprovals of several therapeutic compounds and their companion diagnostic devices (FDA News Release, 2011, 2013) to identify patients who would benefit from treatment have led to considerable interest in incorporating predictive biomarkers in clinical studies. Yet, the translation of predictive biomarkers poses unique technical, logistic, and regulatory challenges that need to be addressed by a multidisciplinary team including discovery scientists, clinicians, biomarker experts, regulatory personnel, and assay developers. These issues can be placed into four broad categories: sample collection, assay validation, sample analysis, and regulatory requirements. In this paper, we provide a primer for drug development teams who are eager to implement a predictive patient segmentation marker into an early clinical trial in a way that facilitates subsequent development of a companion diagnostic. Using examples of nucleic acid-based assays, we briefly review common issues encountered when translating a biomarker to the clinic but focus primarily on key practical issues that should be considered by clinical teams when planning to use a biomarker to balance arms of a study or to determine eligibility for a clinical study.
HERSCHEL-ATLAS: A BINARY HyLIRG PINPOINTING A CLUSTER OF STARBURSTING PROTOELLIPTICALS

Panchromatic observations of the best candidate hyperluminous infrared galaxies from the widest Herschel extragalactic imaging survey have led to the discovery of at least four intrinsically luminous z = 2.41 galaxies across an ≈100 kpc region—a cluster of starbursting protoellipticals. Via subarcsecond interferometric imaging we have measured accurate gas and star formation surface densities. The two brightest galaxies span ∼3 kpc FWHM in submillimeter/radio continuum and CO J = 4–3, and double that in CO J = 1–0. The broad CO line is due partly to the multitude of constituent galaxies and partly to large rotational velocities in two counter-rotating gas disks—a scenario predicted to lead to the most intense starbursts, which will therefore come in pairs. The disks have Mdyn of several × 1011 M☉, and gas fractions of ∼40%. Velocity dispersions are modest so the disks are unstable, potentially on scales commensurate with their radii: these galaxies are undergoing extreme bursts of star formation, not confined to their nuclei, at close to the Eddington limit. Their specific star formation rates place them ≳ 5 × above the main sequence, which supposedly comprises large gas disks like these. Their high star formation efficiencies are difficult to reconcile with a simple volumetric star formation law. N-body and dark matter simulations suggest that this system is the progenitor of a B(inary)-type ≈1014.6-M☉ cluster.
Learning hidden unit contributions for unsupervised speaker adaptation of neural network acoustic models

This paper proposes a simple yet effective model-based neural network speaker adaptation technique that learns speaker-specific hidden unit contributions given adaptation data, without requiring any form of speaker-adaptive training, or labelled adaptation data. An additional amplitude parameter is defined for each hidden unit; the amplitude parameters are tied for each speaker, and are learned using unsupervised adaptation. We conducted experiments on the TED talks data, as used in the International Workshop on Spoken Language Translation (IWSLT) evaluations. Our results indicate that the approach can reduce word error rates on standard IWSLT test sets by about 8-15% relative compared to unadapted systems, with a further reduction of 4-6% relative when combined with feature-space maximum likelihood linear regression (fMLLR). The approach can be employed in most existing feed-forward neural network architectures, and we report results using various hidden unit activation functions: sigmoid, maxout, and rectifying linear units (ReLU).
The space of density states in geometrical quantum mechanics

We present a geometrical description of the space of density states of a quantum system of finite dimension. After presenting a brief summary of the geometrical formulation of Quantum Mechanics, we proceed to describe the space of density states $\D(\Hil)$ from a geometrical perspective identifying the stratification associated to the natural $GL(\Hil)$--action on $\D(\Hil)$ and some of its properties. We apply this construction to the cases of quantum systems of two and three levels.
Effects of Isoflurane Anesthesia on Ensemble Patterns of Ca2+ Activity in Mouse V1: Reduced Direction Selectivity Independent of Increased Correlations in Cellular Activity

Anesthesia affects brain activity at the molecular, neuronal and network level, but it is not well-understood how tuning properties of sensory neurons and network connectivity change under its influence. Using in vivo two-photon calcium imaging we matched neuron identity across episodes of wakefulness and anesthesia in the same mouse and recorded spontaneous and visually evoked activity patterns of neuronal ensembles in these two states. Correlations in spontaneous patterns of calcium activity between pairs of neurons were increased under anesthesia. While orientation selectivity remained unaffected by anesthesia, this treatment reduced direction selectivity, which was attributable to an increased response to the null-direction. As compared to anesthesia, populations of V1 neurons coded more mutual information on opposite stimulus directions during wakefulness, whereas information on stimulus orientation differences was lower. Increases in correlations of calcium activity during visual stimulation were correlated with poorer population coding, which raised the hypothesis that the anesthesia-induced increase in correlations may be causal to degrading directional coding. Visual stimulation under anesthesia, however, decorrelated ongoing activity patterns to a level comparable to wakefulness. Because visual stimulation thus appears to ‘break’ the strength of pairwise correlations normally found in spontaneous activity under anesthesia, the changes in correlational structure cannot explain the awake-anesthesia difference in direction coding. The population-wide decrease in coding for stimulus direction thus occurs independently of anesthesia-induced increments in correlations of spontaneous activity.
Anti-inflammatory and anti-oxidative effects of the green tea polyphenol epigallocatechin gallate in human corneal epithelial cells

Purpose To determine the anti-inflammatory and anti-oxidant effects of epigallocatechin gallate (EGCG), the major polyphenol component of green tea, in human corneal epithelial cells (HCEpiC). Methods HCEpiC were challenged with interleukin-1β (IL-1β) for 18 h or hyperosmolarity (440 mOsm) for 24 h. Luminex technology was used to determine the effects of EGCG (0.3 – 30 µM) on IL-1β- or hyperosmolar-induced cytokine release into the medium. Cell metabolic activity was measured using the alamarBlue assay. Effects of EGCG on mitogen-activated protein kinase (MAPK) phosphorylation were determined by cell-based enzyme-linked immunosorbent assay (ELISA) and western blotting. Effects of EGCG on nuclear factor kappa B (NFκB) and activator protein-1 (AP-1) transcriptional activity were assessed by reporter gene assay. The effects of EGCG on glucose oxidase (GO)-induced reactive oxygen species (ROS) production was determined using the ROS probe CM-H2DCFDA. Results Treatment of HCEpiC with 1 ng/ml IL-1β for 18 h significantly increased release of the cytokines/chemokines granulocyte colony-stimulating factor (G-CSF), granulocyte-macrophage colony-stimulating factor (GM-CSF), interleukin-6 (IL-6), interleukin-8 (IL-8), and monocyte chemotactic protein-1 (MCP-1), while hyperosmolarity-induced release of IL-6 and MCP-1. When cells were treated with IL-1β and EGCG or hyperosmolarity and EGCG there was a dose-dependent reduction in release of these cytokines/chemokines, with significant inhibition observed at 3–30 µM. There was no effect of EGCG on cell metabolic activity at any of the doses tested (0.3–30 µM). EGCG significantly inhibited phosphorylation of the MAPKs p38 and c-Jun N-terminal kinase (JNK), and NFκB and AP-1 transcriptional activities. There was a significant dose-dependent decrease in GO-induced ROS levels after treatment of HCEpiC with EGCG. Conclusions EGCG acts as an anti-inflammatory and anti-oxidant agent in HCEpiC and therefore may have therapeutic potential for ocular inflammatory conditions such as dry eye.
Analysis of relationship between target visual cognition difficulties and gaze movements in visual search task

In this paper, we experimentally examine the relationship between visual cognition difficulty and target-tracking eye movements, which recorded during moving target cognition. Generally, such eye movements are observed when humans perceive a moving object and they vary widely due to many factors, such as target shape, backgrounds, illumination conditions, and so on. Several systems have been proposed for estimating human cognition based on gaze movements. However, since most of them employ simple thresholding techniques to classify the states of cognition, their classification performance remain insufficient. This research clarifies the relationship between multiple visual conditions and target-tracking eye movements to enhance the classification performance. We found that we can address a variety of target and background factors from the perspective of visual cognition difficulty for the targets. Observed gaze movement properties and the difficulty of target visual cognition have a linear relationship, suggesting the possibility of more precise estimation of human's target cognition based on them.
A modified PSO algorithm for remote sensing image template matching.

Image template matching is essential in image analysis and computer vision tasks. Cross-correlation algorithms are often used in practice, but they are sensitive to nonlinear changes in image intensity and random noise, and are computationally expensive. In this paper, we propose a template-matching algorithm based on a modified particle swarm optimization (PSO) procedure with a mutual information (MI) similarity measure. The influence of MI on the performance of template matching, calculated by different histogram bins, is analyzed first. A modified PSO method (CRI-PSO) is then presented. The proposed algorithm is tested with remote sensing imagery from different sensors and for different seasons. Our experimental results indicate that the proposed approach is robust in practical scenarios and outperforms the standard PSO , multi-start PSO , and cross-correlation algorithms in accuracy and efficiency with our test data. The proposed method can be used for position estimation of aircraft, object recognition, and image retrieval.
Molecular Characterization of E-Type Prostanoid Receptor 4 (EP4) from Ayu (Plecoglossus altivelis) and Its Functional Analysis in the Monocytes/Macrophages

Prostaglandin E2 (PGE2) plays an important role in a broad spectrum of physiological and pathological processes by interacting with E-type prostanoid receptors (EPs). EP4 is one of four EP subtypes known to mediate the immune response in mammalian monocytes/macrophages. However, the precise function and characteristics of EP4 in fish remain unclear. In this study, we characterized a novel EP4-like (PaEP4L) gene from ayu, Plecoglossus altivelis. The cDNA sequence of PaEP4L is 2781 nucleotides (nts) in length, encoding a polypeptide of 459 amino acid residues with a calculated molecular weight of 51.17 kDa. Sequence comparison and phylogenetic tree analysis showed that PaEP4L shared 76% amino acid identity with that of the Atlantic salmon (Salmo salar). PaEP4L mRNA was detected by real-time quantitative PCR (QPCR) in all tested tissues and head kidney-derived monocytes/macrophages (MO/MФ). It varied greatly in liver, spleen and MO/MФ upon Vibrio anguillarum infection. Western blot analysis revealed a significant increase of PaEP4L in cell homogenates from ayu MO/MФ upon V. anguillarum infection. Moreover, anti-PaEP4L IgG reversed the down-regulation of interleukin 1β (IL-1β) and tumor necrosis factor α (TNF-α) mRNA expression as well as phagocytosis in ayu MO/MФ caused by PGE2. There were no significant differences in the respiratory burst response between PGE2 treated and untreated cells. We further found that cAMP mediated PGE2/PaEP4L signal in ayu MO/MФ. In conclusion, our results indicate that PaEP4L mediates PGE2 effects on ayu MO/MФ function, revealing that EP4 also plays a role in the modulation of cells of the fish’s innate immune system.
A Security Architecture for Data Aggregation and Access Control in Smart Grids

We propose an integrated architecture for smart grids, that supports data aggregation and access control. Data can be aggregated by home area network, building area network and neighboring area network in such a way that the privacy of customers is protected. We use homomorphic encryption technique to achieve this. The consumer data that is collected is sent to the substations where it is monitored by remote terminal units (RTU). The proposed access control mechanism gives selective access to consumer data stored in data repositories and used by different smart grid users. Users can be maintenance units, utility centers, pricing estimator units or analyzing and prediction groups. We solve this problem of access control using cryptographic technique of attribute-based encryption. RTUs and users have attributes and cryptographic keys distributed by several key distribution centers (KDC). RTUs send data encrypted under a set of attributes. Users can decrypt information provided they have valid attributes. The access control scheme is distributed in nature and does not rely on a single KDC to distribute keys. Bobba \emph{et al.} \cite{BKAA09} proposed an access control scheme, which relies on a centralized KDC and is thus prone to single-point failure. The other requirement is that the KDC has to be online, during data transfer which is not required in our scheme. Our access control scheme is collusion resistant, meaning that users cannot collude and gain access to data, when they are not authorized to access. We theoretically analyze our schemes and show that the computation overheads are low enough to be carried out in smart grids. To the best of our knowledge, ours is the first work on smart grids, which integrates these two important security components (privacy preserving data aggregation and access control) and presents an overall security architecture in smart grids.