Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of synthetic photostable retinoids induces novel limb and facial phenotypes during chick embryogenesis in vivo

We have recently developed a range of synthetic retinoid analogues which include the compounds EC23 and EC19. They are stable on exposure to light and are predicted to be resistant to the normal metabolic processes involved in the inactivation of retinoids in vivo. Based on the position of the terminal carboxylic acid groups in the compounds we suggest that EC23 is a structural analogue of all‐trans retinoic acid (ATRA), and EC19 is an analogue of 13‐cis retinoic acid. Their effects on the differentiation of pluripotent stem cells has been previously described in vitro and are consistent with this hypothesis. We present herein the first description of the effects of these molecules in vivo. Retinoids were applied to the anterior limb buds of chicken embryos in ovo via ion‐exchange beads. We found that retinoid EC23 produces effects on the wing digits similar to ATRA, but does so at two orders of magnitude lower concentration. When larger quantities of EC23 are applied, a novel phenotype is obtained involving production of multiple digit 1s on the anterior limb. This corresponds to differential effects of ATRA and EC23 on sonic hedgehog (shh) expression in the developing limb bud. With EC23 application we also find digit 1 phenotypes similar to thumb duplications described in the clinical literature. EC23 and ATRA are shown to have effects on the entire proximal–distal axis of the limb, including hitherto undescribed effects on the scapula. This includes suppression of expression of the scapula marker Pax1. EC23 also produces effects similar to those of ATRA on the developing face, producing reductions of the upper beak at concentrations two orders of magnitude lower than ATRA. In contrast, EC19, which is structurally very similar to EC23, has novel, less severe effects on the face and rarely alters limb development. EC19 and ATRA are effective at similar concentrations. These results further demonstrate the ability of retinoids to influence embryonic development. Moreover, EC23 represents a useful new tool to investigate developmental processes and probe the mechanisms underlying congenital abnormalities in vertebrates including man.
Learning and using language via recursive pragmatic reasoning about other agents

Language users are remarkably good at making inferences about speakers' intentions in context, and children learning their native language also display substantial skill in acquiring the meanings of unknown words. These two cases are deeply related: Language users invent new terms in conversation, and language learners learn the literal meanings of words based on their pragmatic inferences about how those words are used. While pragmatic inference and word learning have both been independently characterized in probabilistic terms, no current work unifies these two. We describe a model in which language learners assume that they jointly approximate a shared, external lexicon and reason recursively about the goals of others in using this lexicon. This model captures phenomena in word learning and pragmatic inference; it additionally leads to insights about the emergence of communicative systems in conversation and the mechanisms by which pragmatic inferences become incorporated into word meanings.
Quantification of injury-related EEG signal changes using distance measures

Novel indicators based on distance measures are developed and compared to quantify changes in electroencephalogram signal resulting from hypoxic-asphyxic injury. An injury index is derived based on the measures. The Itakura distance-based index is found to have the highest correlation with the long-term outcome as measured by the neurological deficit scores.
Analysis of one-bit quantized ZF precoding for the multiuser massive MIMO downlink

We present a mathematical analysis of linear precoders for downlink multiuser massive MIMO. In the scenario we consider, one-bit digital-to-analog converters are employed at the basestation antennas for mitigating power consumption and hardware complexity. Using the Bussgang theorem, a probability-of-error analysis for a general precoder is presented. The special case of zero-forcing (ZF) precoders is considered and simplified error expressions are derived for asymptotic scenarios. Our analysis illustrates that the probability of error for the quantized ZF precoder depends primarily on the ratio of the number of antennas to the number of users. Our simulations also show that the quantized ZF precoder outperforms the high complexity ML encoder for low-to medium-SNRs.
Discovering Valuable items from Massive Data

Suppose there is a large collection of items, each with an associated cost and an inherent utility that is revealed only once we commit to selecting it. Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value? This task generalizes several important problems such as multi-arm bandits, active search and the knapsack problem. We present an algorithm, GP-SELECT, which utilizes prior knowledge about similarity between items, expressed as a kernel function. GP-SELECT uses Gaussian process prediction to balance exploration (estimating the unknown value of items) and exploitation (selecting items of high value). We extend GP-SELECT to be able to discover sets that simultaneously have high utility and are diverse. Our preference for diversity can be specified as an arbitrary monotone submodular function that quantifies the diminishing returns obtained when selecting similar items. Furthermore, we exploit the structure of the model updates to achieve an order of magnitude (up to 40X) speedup in our experiments without resorting to approximations. We provide strong guarantees on the performance of GP-SELECT and apply it to three real-world case studies of industrial relevance: (1) Refreshing a repository of prices in a Global Distribution System for the travel industry, (2) Identifying diverse, binding-affine peptides in a vaccine design task and (3) Maximizing clicks in a web-scale recommender system by recommending items to users.
An Automatic Framework for Segmentation and Digital Inpainting of 2D Frontal Face Images

Nowadays applications that use face images as input for people identification have been very common. In general, the input image must be preprocessed in order to fit some normalization and quality criteria. In this paper, we propose a computational framework composed of digital image quality computation, segmentation of the damaged regions on face images by statistical decision, morphological operators and image restoration by inpainting techniques. Additionally, in this work we propose a new method for digital inpainting that considers as relevant information for such restoration the neighboring pixel intensities as well as prior information extracted from an image database. To assess the efficiency of the computational framework proposed, we have used 2D face images from public databases. The results show that our inpainting method performs similarly to the traditional ones on quasi-homogeneous regions and gives better results when the damaged areas includes complex image patterns.
Crude Oil and Gasoline Prices: an Asymmetric Relationship? Spot Crude Oil Prices and Retail Gasoline Prices

consumption of petroleum products, and its price is the most visible among these products. As such, changes in gasoline prices are always under public scrutiny. Many claim to observe an asymmetric relationship between gasoline and oil prices — specifically that gasoline prices respond more quickly when oil prices are rising than when oil prices are falling (Figure 1). President George Bush gave these concerns official weight during the Gulf War when he asked the oil companies to show restraint in raising prices for their products. Much of the previous research provides econometric support for public claims of asymmetry in the movements of gasoline and crude oil prices. all found some evidence of an asymmetric response in U.S. gasoline markets. Norman and Shin (1991) found a symmetric response in U.S. gasoline markets. Of these studies, one of the most comprehensive and compelling is that of Borenstein, Cameron, and Gilbert (1997), hereafter identified as BCG. They use a series of bivariate error-correction models to test for asymmetry in price movements in each of the various stages in the production and distribution of gasoline from the crude oil price through the refinery to the retail pump, using weekly and biweekly data from 1986 to 1992. They find strong and pervasive evidence of asymmetry. As Shin (1992) has argued, however, the periodicity of the data, the sample period of estimation, and the model specification may have affected the results obtained in previous studies. To explore these issues, we extend the work of BCG by using several different model Many claim to observe an asymmetric relationship between gasoline and oil prices…that gasoline prices respond more quickly when oil prices are rising than when oil prices are falling.
A Cognitive System for Human Manipulation Action Understanding

This paper describes the architecture of a cognitive system that interprets human manipulation actions from perceptual information (image and depth data) and consists of perceptual modules and reasoning modules that are in interaction with each other. The contributions of our work are along two core problems at the heart of action understanding: a. ) the grounding of relevant information about actions in perception (the perception action integration problem), and b. ) the organization of perceptual and high-level symbolic information for interpreting the actions (the sequencing problem). At the high level, actions are represented with the Manipulation Action Grammar, a context-free grammar and associated parsing algorithms, which organizes actions as a sequence of sub-events. Each sub-event is described by the hand, movements and the objects and tools involved, and the relevant information about these quantities is obtained from biological-inspired perception modules. These modules track the hands and objects and recognize the hand grasp, objects and actions using attention, segmentation, and feature description. Experiments on a new dataset of manipulation actions show that our system can successfully extract the relevant visual information and semantic representation. This representation could further be used by the cognitive agent for reasoning, prediction, and planning.
Self-bias modulates saccadic control

We present novel data on the role of attention in eliciting enhanced processing of stimuli associated with self. Participants were required to make pro- or anti-saccades according to whether learned shape–label pairings matched or mismatched. When stimuli matched participants were required to make an anti-saccade, and when the stimuli mismatched a pro-saccade was required. We found that anti-saccades were difficult to make to stimuli associated with self when compared to stimuli associated with a friend and a stranger. In contrast, anti-saccades to friend-stimuli were easier to make than anti-saccades to stranger-stimuli. In addition, a correct anti-saccade to a self-associated stimulus disrupted subsequent pro-saccade trials, relative to when the preceding anti-saccade was made to other stimuli. The data indicate that self-associated stimuli provide a strong cue for explicit shifts of attention to them, and that correct anti-saccades to such stimuli demand high levels of inhibition (which carries over to subsequent pro-saccade trials). The self exerts an automatic draw on attention.
Universal rewriting in constrained memories

A constrained memory is a storage device whose elements change their states under some constraints. A typical example is flash memories, in which cell levels are easy to increase but hard to decrease. In a general rewriting model, the stored data changes with some pattern determined by the application. In a constrained memory, an appropriate representation is needed for the stored data to enable efficient rewriting.
A Network of Immediate Early Gene Products Propagates Subtle Differences in Mitogen-Activated Protein Kinase Signal Amplitude and Duration

ABSTRACT The strength and duration of mitogen-activated protein kinase (MAPK) signaling have been shown to regulate cell fate in different cell types. In this study, a general mechanism is described that explains how subtle differences in signaling kinetics are translated into a specific biological outcome. In fibroblasts, the expression of immediate early gene (IEG)-encoded Fos, Jun, Myc, and early growth response gene 1 (Egr-1) transcription factors is significantly extended by sustained extracellular signal-regulated kinase 1 and 2 (ERK1 and -2) signaling. Several of these proteins contain functional docking site for ERK, FXFP (DEF) domains that serve to locally concentrate the active kinase, thus showing that they can function as ERK sensors. Sustained ERK signaling regulates the posttranslational modifications of these IEG-encoded sensors, which contributes to their sustained expression during the G1-S transition. DEF domain-containing sensors can also interpret the small changes in ERK signal strength that arise from less than a threefold reduction in agonist concentration. As a result, downstream target gene expression and cell cycle progression are significantly changed.
A Method for Visualization of Fine Retinal Vascular Pulsation Using Nonmydriatic Fundus Camera Synchronized with Electrocardiogram

Pulsatile changes in retinal vascular geometry over the cardiac cycle have clinical implication for diagnosis of ocular and systemic vascular diseases. In this study, we report a Vesselness Mapping of Retinal Image Sequence (VMRS) methodology to visualize the vessel pulsation and quantify the pulsatile motions in the cardiac cycle. Retinal images were recorded in an image sequence corresponding to 8 segments of the cardiac cycle using a nonmydriatic fundus camera (Canon CR45, Canon Inc., Japan) modified with ECG-synchronization. Individual cross-sectional vessel diameters were measured separately and the significance of the variations was tested statistically by repeated measures analysis of variance (ANOVA). The graders observed an improved quality of vessel pulsation on a wide region around the optic disk using the VMRS. Individual cross- sectional vessel diameter measurement after visualization of pulsatile motions resulted in the detection of more significant diameter change for both arterioles (3.3 μm, P = 0.001) and venules (6.6 μm, P < 0.001) compared to individual measurement without visualization of the pulsatile motions (all P values > 0.05), showing an increase of 2.1 μm and 4.7 μm for arterioles and venules, respectively.
Clustering Problems on Sliding Windows

We explore clustering problems in the streaming sliding window model in both general metric spaces and Euclidean space. We present the first polylogarithmic space O(1)-approximation to the metric k-median and metric k-means problems in the sliding window model, answering the main open problem posed by Babcock, Datar, Motwani and O'Callaghan [5], which has remained unanswered for over a decade. Our algorithm uses O(k3log6W) space and poly(k, log W) update time, where W is the window size. This is an exponential improvement on the space required by the technique due to Babcock, et al. We introduce a data structure that extends smooth histograms as introduced by Braverman and Ostrovsky [11] to operate on a broader class of functions. In particular, we show that using only polylogarithmic space we can maintain a summary of the current window from which we can construct an O(1)-approximate clustering solution. Merge-and-reduce is a generic method in computational geometry for adapting offline algorithms to the insertion-only streaming model. Several well-known coreset constructions are maintainable in the insertion-only streaming model using this method, including well-known coreset techniques for the k-median and k-means problems in both low-and high-dimensional Euclidean spaces [31, 15]. Previous work [27] has adapted coreset techniques to the insertion-deletion model, but translating them to the sliding window model has remained a challenge. We give the first algorithm that, given an insertion-only streaming coreset of space s (maintained using merge-and-reduce method), maintains this coreset in the sliding window model using O(s2e--2 log W) space. For clustering problems, our results constitute the first significant step towards resolving problem number 20 from the List of Open Problems in Sublinear Algorithms [39].
Inferring Continental Ancestry of Argentineans from Autosomal, Y‐Chromosomal and Mitochondrial DNA

We investigated the bio‐geographic ancestry of Argentineans, and quantified their genetic admixture, analyzing 246 unrelated male individuals from eight provinces of three Argentinean regions using ancestry‐sensitive DNA markers (ASDM) from autosomal, Y and mitochondrial chromosomes. Our results demonstrate that European, Native American and African ancestry components were detectable in the contemporary Argentineans, the amounts depending on the genetic system applied, exhibiting large inter‐individual heterogeneity. Argentineans carried a large fraction of European genetic heritage in their Y‐chromosomal (94.1%) and autosomal (78.5%) DNA, but their mitochondrial gene pool is mostly of Native American ancestry (53.7%); instead, African heritage was small in all three genetic systems (<4%). Population substructure in Argentina considering the eight sampled provinces was very small based on autosomal (0.92% of total variation was between provincial groups, p = 0.005) and mtDNA (1.77%, p = 0.005) data (none with NRY data), and all three genetic systems revealed no substructure when clustering the provinces into the three geographic regions to which they belong. The complex genetic ancestry picture detected in Argentineans underscores the need to apply ASDM from all three genetic systems to infer geographic origins and genetic admixture. This applies to all worldwide areas where people with different continental ancestry live geographically close together.
What they do in shadows: Twitter underground follower market

Internet users and businesses are increasingly using online social networks (OSN) to drive audience traffic and increase their popularity. In order to boost social presence, OSN users need to increase the visibility and reach of their online profile, like - Facebook likes, Twitter followers, Instagram comments and Yelp reviews. For example, an increase in Twitter followers not only improves the audience reach of the user but also boosts the perceived social reputation and popularity. This has led to a scope for an underground market that provides followers, likes, comments, etc. via a network of fraudulent and compromised accounts and various collusion techniques. In this paper, we landscape the underground markets that provide Twitter followers by studying their basic building blocks - merchants, customers and phony followers. We charecterize the services provided by merchants to understand their operational structure and market hierarchy. Twitter underground markets can operationalize using a premium monetary scheme or other incentivized freemium schemes. We find out that freemium market has an oligopoly structure with few merchants being the market leaders. We also show that merchant popularity does not have any correlation with the quality of service provided by the merchant to its customers. Our findings also shed light on the characteristics and quality of market customers and the phony followers provided by underground market. We draw comparison between legitimate users and phony followers, and find out key identifiers to separate such users. With the help of these differentiating features, we build a supervised learning model to predict suspicious following behaviour with an accuracy of 89.2%.
OASIS: Operational Access Sandboxes for Information Security

Android's permission system follows an "all or nothing" approach when installing an application. The end user has no way to know how the permissions are actually used by the application, and how the sensitive data flows during its execution. With this work we present OASIS (Operational Access Sandboxes for Information Security), a trusted component that allows developers to execute operations on sensitive data while keeping that data confidential. OASIS allows the end user to have full control over the data available to applications, and also grants policy based regulation of sensitive data flows. Moreover, our system can be deployed via a simple application installation, and does not require any modification to the stock Android OS.
Nearest neighbor prediction of pareto dominance using general regression neural networks

Conventional methods of determining Pareto dominance in multi-objective optimization evaluate and compare objective vectors of candidate solutions, but the computation and (or) experiment of evaluating objective vectors are overwhelmingly costly when computationally expensive multi-objective problems are involved. This study investigates a nearest neighbor prediction method of Pareto dominance using general regression neural networks (GRNN). The decision differential value (D-value) vector of two feasible solutions is used as the input of GRNN, and the objective D-value vector is used as the output. Under the supervision of sample candidate solutions, GRNN is used to predict objective D-value vectors between an observed solution and samples. For an observed candidate, the predicted objective D-Value vectors are used to find out the nearest neighbor samples in objective space. Experimental results show that the nearest neighbor prediction of Pareto dominance relationships using GRNN can obtain acceptable prediction accuracy. The proposed algorithm provides an effective method to relieve the curse of computation cost in computationally expensive multi-objective optimization problems, but without requirement for analytical models of objective functions.
Anticancer Activity of Indian Stingless Bee Propolis: An In Vitro Study

Indian stingless bee propolis has a complex chemical nature and is reported to possess various medicinal properties. In the present study, anticancer activity of the ethanolic extract of propolis (EEP) was explored by testing the cytotoxic and apoptotic effect in four different cancer cell lines, namely, MCF-7 (human breast cancer), HT-29 (human colon adenocarcinoma), Caco-2 (human epithelial colorectal adenocarcinoma), and B16F1 (murine melanoma), at different concentrations. Cytotoxicity was evaluated by MTT assay and Trypan blue dye exclusion assay. EEP at a concentration of 250 μg/mL exhibited ≥50% mortality in all cell lines tested (i.e., IC50 value). EEP revealed a concentration and time dependent cytotoxic effect. Apoptosis was estimated by differential staining (ethidium bromide/acridine orange) and TUNEL (deoxynucleotidyl transferase-dUTP nick end labeling) assay. Light microscopy and atomic force microscopy demonstrated morphological features of apoptosis in all the cell lines after treatment with 250 μg/mL EEP for 24 h. Thus, early onset of apoptosis is the reason for anticancer activity of Indian stingless bee propolis. Further, the antioxidant potential of Indian stingless bee propolis was demonstrated to substantiate its anticancer activity.