Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-Wide Scan for Japanese Familial Intracranial Aneurysms: Linkage to Several Chromosomal Regions

Background—Genetic factors have an important role in the pathogenesis of intracranial aneurysm (IA). The results of previous studies have suggested several loci. Methods and Results—From 29 IA families with ≥3 individuals affected by IA, we used nonparametric (model-free) methods for linkage analyses, using GENEHUNTER and Merlin software. Genome-wide linkage analyses revealed 3 regions on chromosomes 17cen (maximum nonparametric logarithm of the odds score [MNS] = 3.00, nominal P=0.001), 19q13 (MNS=2.15, nominal P=0.020), and Xp22 (MNS=2.16, nominal P=0.019). We tested 4 candidate genes in these regions: the microfibril-associated protein 4 gene (MFAP4) and the promoter polymorphism of the inducible nitric oxide synthase gene (NOS2A) on chromosome 17cen, the epsilon genotypes of the apolipoprotein E gene (APOE) on chromosome 19q13, and the angiotensin I converting enzyme 2 gene (ACE2) on chromosome Xp22. Associations of their polymorphisms with IA were evaluated by a case-control study (100 cases: 29 probands from IA families and 71 unrelated subjects with IAs, 100 unrelated control subjects [unaffected members with IAs and absence of family history of IAs]). However, the case-control study showed that none of the polymorphisms of the examined genes had associations with IA. Conclusions—A genome-wide scan in 29 Japanese families with a high degree of familial clustering revealed 1 suggestive linkage region on chromosome 17cen and 2 potentially interesting regions on chromosomes 19q13 and Xp22. These regions were consistent with previous findings in various populations.
Genetic Programming in Agent-Based Arti cial Markets: Simulations and Analysis

In this paper, we propose a new architecture to study arti cial stock markets. This architecture rests on a mechanism called \school" which is a procedure to map the phenotype to the genotype or, in plain English, to uncover the secret of success. We propose an agent-based model of \school", and consider school as an evolving population driven by single-population GP (SGP). The architecture also takes into consideration traders' search behavior. By simulated annealing, traders' search density can be connected to psychological factors, such as peer pressure or economic factors such as the standard of living. This market architecture was then implemented in a standard arti cial stock market. Our econometric study of the resultant arti cial time series evidences that the return series is independently and identically distributed (iid), and hence supports the e cient market hypothesis (EMH). What is interesting though is that this iid series was generated by traders, who do not believe in the EMH at all. In fact, our study indicates that many of our traders were able to nd useful signals quite often from business school, even though these signals were short-lived.
Validity of Fuzzy Clustering Using Entropy Regularization

We introduce in this paper a new formulation of the regularized fuzzy c-means (FCM) algorithm which allows us to find automatically the actual number of clusters. The approach is based on the minimization of an objective function which mixes, via a particular parameter, a classical FCM term and a new entropy regularizer. The main contribution of the method is the introduction of a new exponential form of the fuzzy memberships which ensures the consistency of their bounds and makes it possible to interpret the mixing parameter as the variance (or scale) of the clusters. This variance closely related to the number of clusters, provides us with an intuitive and an easy to set parameter. We will discuss the proposed approach from the regularization point-of-view and we will demonstrate its validity both analytically and experimentally. We will show an extension of the method to nonlinearly separable data. Finally, we will illustrate preliminary results both on simple toy examples as well as database categorization problems
Combining Parametric and Non-parametric Methods for Predicting Consumer Choice Using Supermarket Scanner Data

It is important for national chain stores to be able to customize their prices in individual stores to adapt to the neighborhood demand. A lot of stores collect scanner data which can be used to determine the price distribution over products which optimizes profits, and yet often this data resource is under-utilized. In previous research both parametric (such as linear regression) and non-parametric (such as artificial neural networks) models have been successfully used for prediction. We propose to approach this problem from a different angle and using both parametric and non-parametric methods to investigate ways of combining the lower-level models into a high-level model so as to achieve a better predictor than either of the simpler methods by itself. We will work with the data provided by a supermarket chain which would like to be able to price more strategically the various products in its local stores. The scanner data is for the category Chilled Juices and consists of store-level weekly reports of prices and quantities sold for the 14 products in the category. The data was assembled for two years from 100 individual stores of the supermarket chain. Our research will produce a method able to predict for a given category of products at a store level the consumer demand of the products from their prices. Previous research indicates that micro-marketing pricing strategies can increase gross profit margins by 4% to 10%, which after administrative and operating costs are subtracted would translate into an increase of operating profit margins by 33% to 83%. This research will provide a valuable tool for marketers concerned with predicting consumer choice. The new models will also generalize to other Machine Learning applications where both methods are currently used.
A genetic screen in zebrafish identifies cilia genes as a principal cause of cystic kidney

Polycystic kidney disease (PKD) is a common human genetic illness. It is characterized by the formation of multiple kidney cysts that are thought to result from over-proliferation of epithelial cells. Zebrafish larvae can also develop kidney cysts. In an insertional mutagenesis screen in zebrafish, we identified 12 genes that can cause cysts in the glomerular-tubular region when mutated and we cloned 10 of these genes. Two of these genes, vhnf1 (tcf2) and pkd2, are already associated with human cystic kidney diseases. Recently, defects in primary cilia have been linked to PKD. Strikingly, three out of the 10 genes cloned in this screen are homologues of Chlamydomonas genes that encode components of intraflagellar transport (IFT) particles involved in cilia formation. Mutation in a fourth blocks ciliary assembly by an unknown mechanism. These results provide compelling support for the connection between cilia and cystogenesis. Our results also suggest that lesions in genes involved in cilia formation and function are the predominant cause of cystic kidney disease, and that the genes identified here are excellent candidates for novel human PKD genes.
A GMM approximation with merge and split for nonlinear non-Gaussian tracking

In this paper, we present a recursive estimation algorithm for nonlinear non-Gaussian tracking systems based on an adaptive Gaussian mixture technique. This estimation cannot be efficiently performed for nonlinear non-Gaussian systems because of the complex representation of the state density. To alleviate this complexity, approximation techniques based on Gaussian mixtures are used. An adaptive mixture approximation method is used based on optimal minimization of a least squares error function between the true density and the corresponding approximation mixture. The state Gaussian mixture is propagated over time through prediction and update steps. Results in comparison with other methods show the efficiency of the proposed algorithm.
Mining Frequent Trajectory Pattern Co-ordinates in Spatial-Temporal databases using Apriori Algorithm

Frequent pattern mining has been an emerging and active field in data mining research for over a decade. Abundant literature has been emerged from this research and tremendous progress has been made in numerous research frontiers. This article, provide an application of the modified Apriori algorithm in coordinate sets of trajectories to find the frequent trajectory coordinates. In this algorithm additional steps are added to prune the coordinate sets generated so that to reduce the unnecessary search time and space. This sequential pattern mining method is quite simple in nature but complex to implement. In this paper, we propose an efficient modified Apriori Algorithm for mining the frequent trajectory patterns in a spatial–temporal database. This paper explains the basics of data origination, database structure to hold the coordinate datasets and the implementation of the algorithm with the object oriented programming language by an illustration. It can be applied to interesting game domains to find the frequent trajectory of an object shot by a player which follows a trajectory path. Keywords--Spatial Data mining, Frequent pattern mining, Trajectory pattern mining and Association mining.
Poisson Sphere Distributions

We present a method for efficiently generating Poisson sphere distributions, the three-dimensional equivalent of Poisson disk distributions. Our method is able to generate arbitrarily large Poisson sphere distributions in real time, and allows local evaluation of the generated distributions. The method we present is based on cubes with colored corners, called corner cubes, an extension of corner tiles. We generalize tiling algorithms and construction methods from corner tiles to corner cubes. Poisson sphere distributions have several useful applications in computer graphics, such as geometry instancing and a three-dimensional procedural object distribution function.
Toxin YafQ Reduces Escherichia coli Growth at Low Temperatures

Toxin/antitoxin (TA) systems reduce metabolism under stress; for example, toxin YafQ of the YafQ/DinJ Escherichia coli TA system reduces growth by cleaving transcripts with in-frame 5’-AAA-G/A-3’ sites, and antitoxin DinJ is a global regulator that represses its locus as well as controls levels of the stationary sigma factor RpoS. Here we investigated the influence on cell growth at various temperatures and found that deletion of the antitoxin gene, dinJ, resulted in both reduced metabolism and slower growth at 18°C but not at 37°C. The reduction in growth could be complemented by producing DinJ from a plasmid. Using a transposon screen to reverse the effect of the absence of DinJ, two mutations were found that inactivated the toxin YafQ; hence, the toxin caused the slower growth only at low temperatures rather than DinJ acting as a global regulator. Corroborating this result, a clean deletion of yafQ in the ΔdinJ ΔKmR strain restored both metabolism and growth at 18°C. In addition, production of YafQ was more toxic at 18°C compared to 37°C. Furthermore, by overproducing all the E. coli proteins, the global transcription repressor Mlc was found that counteracts YafQ toxicity only at 18°C. Therefore, YafQ is more effective at reducing metabolism at low temperatures, and Mlc is its putative target.
Nanocrafter: Design and Evaluation of a DNA Nanotechnology Game

In this paper we present the design and preliminary evaluation of a new scientiﬁc discovery game , Nanocrafter. The intent of Nanocrafter is to be a citizen science platform for the discovery of novel nanoscale devices built out of self-assembling strands of DNA. The game uses DNA strand displacement rules as its basic mechanics, allowing players to construct and simulate such devices. The aim is to have a simulation accurate enough that promising devices built in the game could be synthesized and tested in the wetlab. Semi-weekly challenges deﬁned by open-ended text prompts and rated by the community encourage players to build towards a particular type of device. Nanocrafter was released online and has run a number of challenges. We discuss the design of the game in terms of visualizations, interactions, scoring, and introductory levels, and present an evaluation of preliminary results, analyzing several devices created by players using the state-of-the-art simulator Visual DSD.
Laboratory Evaluation of Boric Acid-Sugar Solutions as Baits for Management of German Cockroach Infestations

Abstract Boric acid dust has a long history as an insecticide in urban pest management, and it has been shown to be an effective alternative to conventional neurotoxic insecticides. However, dust formulations require specialized equipment and are difficult to apply, whereas gel and paste formulations contain large amounts of boric acid and tend to be less efficacious than other insecticide baits. The purpose of this study was to evaluate the effectiveness of borate solutions as baits against the German cockroach. Several borate-sugar combinations were evaluated in choice and no-choice assays in the laboratory. Mortality was recorded for 15 d and expressed as lethal time90, the time taken to kill 90% of the cockroaches. Results showed that boric acid was more effective than sodium tetraborate or disodium octaborate tetrahydrate and that aqueous solutions containing mixtures of 0.5–2% boric acid and any of several inexpensive sugars, including fructose, glucose, maltose, and sucrose as a phagostimulant, at molar concentrations of 0.05–1.0, can provide rapid and effective kill of German cockroaches, Blattella germanica (L.).
Canonicalization of graph database records using similarity measures

Information extraction and crawling from the Web have been increasingly common, yet raw data are often noisy and redundant due to heterogeneous sources. Although much work has focused on duplicate records detection, there is little investigation in providing a uniform, standard result from the duplicates to users, which we refer to as a canonical result, and the process is referred to record canonicalization. In this paper, we focus on the situation of imperfect and duplicate documents on the Web, and propose a preprocessing method of graph canonicalization. We first formalize the problem of graph records canonicalization, and then we propose three possible solutions in order. Upon the framework, we implement graph selection canonicalization, which aims to construct a canonical graph by selecting the central graph among records. Experiment results demonstrate its performance in representing real world entities.
A novel method to summarize and retrieve text documents using text feature extraction based on ontology

Data retrieval is a key process of acquiring information as per requirement. Now days, the necessity of proper information has increased. The most basic tools which provide this service are browser. It traverses the data as per user's query and gives the search results of all related information. Hence, it becomes a time consuming process to find required information. In this paper, the focus is done over content based data mining using ontology and text feature extraction. Content based data mining process focuses on domain of the data. Ontology, itself is a domain based data set information system that will help to achieve required data retrieval in a more appropriate way. The proposed systemuses k means clustering algorithm for creation of flat clusters. Flat clusters are the primary classification or clusters of data that are used for various further processing. For more appropriate data retrieval, this system uses text feature extraction algorithm. This algorithm will help to reduce the noisy data from data sets. A noise free data will help to perform better data retrieval process.
A New Estimation Algorithm for the GNSS-R Interference Pattern Technique: The Segmented Maximum-Likelihood

During the last years, Global Navigation Satellite Systems (GNSS) reflectometry (GNSS-R) receivers have proven in several field experiments that GNSS signals reflected and scattered from the Earth’s surface can be used in passive remote sensing applications. In ground based GNSS-R receivers, when the line-of-sight (LOS) signal coherently combines at then antenna with the reflected signal, the measured signal-to-noise ratio (SNR) will slowly fluctuate with the change of the GNSS satellite elevation. The Interference Pattern Technique (IPT) was proposed in order to use these SNR fluctuations to infer the distance between the antenna and the ground, as well as some of the geophysical properties of the nearby reflecting surface. The IPT implies little or no modification on the GNSS receiver, but it usually requires fairly long observation periods leading to a poor spatial resolution. Using the existing models, it is possible to express the reflected signal amplitude as a function of the height of the receiver antenna, the surface relative permittivity, and a surface roughness coefficient. Using the output of the receiver’s prompt correlators, we propose a new computationally efficient optimization algorithm, the segmented maximum likelihood (SML), that makes use of the particular properties of the likelihood/cost function under consideration to obtain the maximum likelihood estimator (MLE). We also show, by computing the estimator’s root-mean-square error and comparing it with the Cramer-Rao lower bound, how the obtained estimator can be efficient even for relatively short observation times.
A survey of the existing shadow detection techniques

Shadows are formed when light from the source is obstructed by an object. Shadows hide the information of the underlying objects which will lead to wrong interpretations during image processing. Hence shadow detection has become a vital pre-processing step in digital image processing as most of the images acquired are contaminated with shadows. The shadows are found more in urban images where the surface elevation changes dramatically thus increasing the complexity of the scenes. In this paper we discuss the various techniques that have been used for shadow detection for both still images and moving objects.
The effects of salience on saccadic target selection

Two experiments were conducted to investigate the effects of saliency on saccadic target selection as a function of time. Participants were required to make a speeded saccade towards a target defined by a unique orientation presented concurrently with multiple nontargets and one distractor. Target and distractor were equally salient within the orientation dimension but varied in saliency in the colour dimension. Within the colour dimension, the target presented could be more, equally, or less salient than the distractor. The results showed that saliency played a large role early during processing while no effects of saliency were found in later processing. Results are discussed in terms of models on visual selection.
LIN28A facilitates the transformation of human neural stem cells and promotes glioblastoma tumorigenesis through a pro-invasive genetic program

The cellular reprogramming factor LIN28A promotes tumorigenicity in cancers arising outside the central nervous system, but its role in brain tumors is unknown. We detected LIN28A protein in a subset of human gliomas observed higher expression in glioblastoma (GBM) than in lower grade tumors. Knockdown of LIN28A using lentiviral shRNA in GBM cell lines inhibited their invasion, growth and clonogenicity. Expression of LIN28A in GBM cell lines increased the number and size of orthotopic xenograft tumors. LIN28A expression also enhanced the invasiveness of GBM cells in vitro and in vivo. Increasing LIN28A was associated with down-regulation of tumor suppressing microRNAs let-7b and let-7g and up-regulation of the chromatin modifying protein HMGA2. The increase in tumor cell aggressiveness in vivo and in vitro was accompanied by an upregulation of pro-invasive gene expression, including SNAI1. To further investigate the oncogenic potential of LIN28A, we infected hNSC with lentiviruses encoding LIN28A together with dominant negative R248W-TP53, constitutively active KRAS and hTERT. Resulting subclones proliferated at an increased rate and formed invasive GBM-like tumors in orthotopic xenografts in immunodeficient mice. Similar to LIN28A-transduced GBM neurosphere lines, hNSC-derived tumor cells showed increased expression of HMGA2. Taken together, these data suggest a role for LIN28A in high grade gliomas and illustrate an HMGA2-associated, pro-invasive program that can be activated in GBM by LIN28A-mediated suppression of let-7 microRNAs.
Evolutionary algorithms for classification of malware families through different network behaviors

The staggering increase of malware families and their diversity poses a significant threat and creates a compelling need for automatic classification techniques. In this paper, we first analyze the role of network behavior as a powerful technique to automatically classify malware families and their polymorphic variants. Afterwards, we present a framework to efficiently classify malware families by modeling their different network behaviors (such as HTTP, SMTP, UDP, and TCP). We propose protocol-aware and state-space modeling schemes to extract features from malware network behaviors. We analyze the applicability of various evolutionary and non-evolutionary algorithms for our malware family classification framework. To evaluate our framework, we collected a real-world dataset of $6,000$ unique and active malware samples belonging to 20 different malware families. We provide a detailed analysis of network behaviors exhibited by these prevalent malware families. The results of our experiments shows that evolutionary algorithms, like sUpervised Classifier System (UCS), can effectively classify malware families through different network behaviors in real-time. To the best of our knowledge, the current work is the first malware classification framework based on evolutionary classifier that uses different network behaviors.
The Path from Foster Care to Permanence: Does Proximity Outweigh Stability?

This thesis investigates the relationship between foster care placement settings and discharges. Placement settings are where foster children live: foster homes, group homes, etc. There may be one or several placements for any individual child. In the interest of stability, federal funding to states depends in part on low numbers of placement moves. Federal reviews, however, do not consider whether the placement settings resemble permanent family life (foster homes compared to congregate care) or the direction of placement moves. Competing risks regression was used to analyze time to discharge data of foster children in Georgia. Discharges (competing risks) were compared based on the number and the direction of placement moves. Children with movement patterns that favored placements similar to permanent family life were found to have higher probabilities of discharges to safe permanence. This thesis promotes “proximity to permanence” as an important, but often overlooked, consideration in foster care placements. INDEX WORDS: Adoption, Competing risks regression, Discharge, Foster care, Permanence, Placement, Reunification THE PATH FROM FOSTER CARE TO PERMANENCE: DOES PROXIMITY OUTWEIGH STABILITY?