Miracle-dz/UniFAR-FTUs huggingface dataset:
Shared RNA-binding sites for interacting members of the Drosophila ELAV family of neuronal proteins

The product of the Drosophila embryonic lethal abnormal visual system is a conserved protein (ELAV) necessary for normal neuronal differentiation and maintenance. It possesses three RNA-binding domains and is involved in the regulation of RNA metabolism. The long elav 3′-untranslated region (3′-UTR) is necessary for autoregulation. We used RNA-binding assays and in vitro selection to identify the ELAV best binding site in the elav 3′-UTR. This site resembles ELAV-binding sites identified previously in heterologous targets, both for its nucleotide sequence and its significant affinity for ELAV (Kd 40 nM). This finding supports our model that elav autoregulation depends upon direct interaction between ELAV and elav RNA. We narrowed down the best binding site to a 20 nt long sequence A(U5)A(U3)G(U2)A(U6) in an alternative 3′ exon. We propose and test a model in which the regulated use of this alternative 3′ exon is involved in normal elav regulation. Found in NEurons (FNE), another neuronal RNA-binding protein paralogous to ELAV, also binds this site. These observations provide a molecular basis for the in vivo interactions reported previously between elav and fne.
0 10 50 83 v 2 1 2 Se p 20 01 General relativistic analysis of peculiar velocities

We give a careful general relativistic and (1+3)-covariant analysis of cosmological peculiar velocities induced by matter density perturbations in the presence of a cosmological constant. In our quasi-Newtonian approach, constraint equations arise to maintain zero shear of the non-comoving fundamental worldlines which define a Newtonian-like frame, and these lead to the (1+3)-covariant dynamical equations, including a generalized Poisson-type equation. We investigate the relation between peculiar velocity and peculiar acceleration, finding the conditions under which they are aligned. In this case we find (1+3)-covariant relativistic generalizations of well-known Newtonian results. PACS numbers: 04.20. -q, 98.80. Hw, 98.65. Dx, 95.30. Sf gr-qc/0105083 Submitted to: Class. Quantum Grav.
Gene expression signature to improve prognosis prediction of stage II and III colorectal cancer.

PURPOSE This study aims to develop a robust gene expression classifier that can predict disease relapse in patients with early-stage colorectal cancer (CRC). PATIENTS AND METHODS Fresh frozen tumor tissue from 188 patients with stage I to IV CRC undergoing surgery was analyzed using Agilent 44K oligonucleotide arrays. Median follow-up time was 65.1 months, and the majority of patients (83.6%) did not receive adjuvant chemotherapy. A nearest mean classifier was developed using a cross-validation procedure to score all genes for their association with 5-year distant metastasis-free survival. RESULTS An optimal set of 18 genes was identified and used to construct a prognostic classifier (ColoPrint). The signature was validated on an independent set of 206 samples from patients with stage I, II, and III CRC. The signature classified 60% of patients as low risk and 40% as high risk. Five-year relapse-free survival rates were 87.6% (95% CI, 81.5% to 93.7%) and 67.2% (95% CI, 55.4% to 79.0%) for low- and high-risk patients, respectively, with a hazard ratio (HR) of 2.5 (95% CI, 1.33 to 4.73; P = .005). In multivariate analysis, the signature remained one of the most significant prognostic factors, with an HR of 2.69 (95% CI, 1.41 to 5.14; P = .003). In patients with stage II CRC, the signature had an HR of 3.34 (P = .017) and was superior to American Society of Clinical Oncology criteria in assessing the risk of cancer recurrence without prescreening for microsatellite instability (MSI). CONCLUSION ColoPrint significantly improves the prognostic accuracy of pathologic factors and MSI in patients with stage II and III CRC and facilitates the identification of patients with stage II disease who may be safely managed without chemotherapy.
Family Separateness’ and Connectedness’ Impact on Sibling Relationships, Stress and Depression during Emerging Adulthood

ii Vita . iv List of Tables . vi List of Figures . vii Chapter 1: Family Separateness’ and Connectedness’ Impact on Sibling Relationships, Stress and Depression during Emerging Adulthood . 1 Methods . 7 Results . 11 Discussion . 16 References . 22 Appendix A: Figures . 27 Appendix B: Tables . 31
Optimal control of admission to a multi-server queue with two arrival streams

The problem of finding an optimal admission policy to an M/M/c queue with one controlled and one uncontrolled arrival stream is addressed There are two streams of customers that are generated according to independent Poisson processes with constant arrival rates. The service time probability distribution is exponential and does not depend on the class of the customers. Upon arrival a class 1 customer may be admitted or rejected, while incoming class 2 customers are always admitted. A state-dependent reward is earned each time a new class 1 customer enters the system. When the discount factor is small, there exists a stationary admission policy of a threshold type that maximizes the expected total discounted reward over an infinite horizon. A similar result is also obtained when considering the long-run average reward criterion. The proof relies on a new device that consists of a partial construction of the solution of the dynamic programming equation. Applications arising from teletraffic analysis are proposed. >
Framework for Developing a Brief Interview to Understand Cyber Defense Work: An Experience Report

Cyber defense is increasingly important for the wellbeing of our economy and our national defense. Universities can help meet our growing cybersecurity needs by training the next generation of cyber defenders, and it is crucial that the curricula for such programs are designed to prepare students for the type of work that is performed in the field. Unfortunately, collecting data about cyber work is hindered in situations where cybersecurity professionals are uncomfortable with traditional human factors work analysis methods. Four potential constraints are 1) no naturalistic observations, 2) anonymity and safety, 3) short data collection time, and 4) no deep process questions. We developed a brief interview technique that allowed us to measure the importance of knowledge, skills, and abilities related to offensive and defensive cyber work. Based on our experience using this technique, it fits within the four potential constraints to cyber research and produces information that is directly applicable to the development of cybersecurity curricula. Our technique could potentially be used for other research purposes and personnel selection and by researchers interested in other high-security populations.
Computer-assisted automated image recognition of celiac disease using confocal endomicroscopy

Celiac disease (CD) is an immune-mediated enteropathy triggered by exposure to gluten and similar proteins which affects genetically susceptible persons, increasing their risk of different complications such as malignant lymphomas, small-bowel neoplasia, oropharyngeal tumors, osteoporosis, and bone fractures. In the clinical practice, the diagnosis of CD is made with a diagnostic intestinal biopsy and the concomitant presence of a positive celiac serology. Small bowels mucosa damage due to CD involves various degrees of endoscopically relevant lesions, that are not easily recognized: their overall sensitivity and positive predictive values are poor even when zoom endoscopy is used. Confocal laser endomicroscopy (CLE) has recently revealed to be a useful technique for in vivo virtual histology of small bowels mucosa, but requires skilled and specifically trained operators. We present a computer-based method for the automatic diagnosis of CD-related lesions on the basis of appearance features of confocal images. Comparing the automatic results with the histological gold standard, the proposed method classifies confocal images with accuracy comparable to human observer, suggesting the possibility of real time diagnosis of CD during endoscopy and a noninvasive method to replace biopsy.
An efficient Gaussian random number architecture for MIMO channel emulators

Gaussian random numbers (GRN) generators are indispensable components in channel emulators for producing multiplicative and additive noises. Efficient designs of these GRN generators are required for testing the newest communications standards, which consider multiple channels working at higher data rates. In this paper, a new reconfigurable architecture for the generation of GRN at each clock cycle is proposed. The design consists in the polynomial approximation of the inverse method, implemented through parallel computing techniques using processor arrays. Time and area analysis carried out show a performance improvement when compared with previous works. Finally, the architecture's statistical performance was corroborated in QAM-VBLAST transmission scheme.
Lack of Population Genetic Structuring in Ocelots (Leopardus pardalis) in a Fragmented Landscape

Habitat fragmentation can promote patches of small and isolated populations, gene flow disruption between those populations, and reduction of local and total genetic variation. As a consequence, these small populations may go extinct in the long-term. The ocelot (Leopardus pardalis), originally distributed from Texas to southern Brazil and northern Argentina, has been impacted by habitat fragmentation throughout much of its range. To test whether habitat fragmentation has already induced genetic differentiation in an area where this process has been documented for a larger felid (jaguars), we analyzed molecular variation in ocelots inhabiting two Atlantic Forest fragments, Morro do Diabo (MD) and Iguacu Region (IR). Analyses using nine microsatellites revealed mean observed and expected heterozygosity of 0.68 and 0.70, respectively. The MD sampled population showed evidence of a genetic bottleneck under two mutational models (TPM = 0.03711 and SMM = 0.04883). Estimates of genetic structure (FST = 0.027; best fit of k = 1 with STRUCTURE) revealed no meaningful differentiation between these populations. Thus, our results indicate that the ocelot populations sampled in these fragments are still not significantly different genetically, a pattern that strongly contrasts with that previously observed in jaguars for the same comparisons. This observation is likely due to a combination of two factors: (i) larger effective population size of ocelots (relative to jaguars) in each fragment, implying a slower effect of drift-induced differentiation; and (ii) potentially some remaining permeability of the anthropogenic matrix for ocelots, as opposed to the observed lack of permeability for jaguars. The persistence of ocelot gene flow between these areas must be prioritized in long-term conservation planning on behalf of these felids.
Document identification for copyright protection using centroid detection

A way to discourage illicit reproduction of copyrighted or sensitive documents is to watermark each copy before distribution. A unique mark is embedded in the text whose recipient is registered. The mark can be extracted from a possibly noisy illicit copy, identifying the registered recipient. Most image marking techniques are vulnerable to binarization attack and, hence, not suitable for text marking. We propose a different approach where a text document is marked by shifting certain text lines slightly up or down or words slightly left or right from their original positions. The shifting pattern constitutes the mark and is different on different copies. In this paper we develop and evaluate a method to detect such minute shifts. We describe a marking and identification prototype that implements the proposed method. We present preliminary experimental results which suggest that centroid detection performs remarkably well on line shifts even in the presence of severe distortions introduced by printing, photocopying, scanning, and facsimile transmission.
Characterization and Comparison of the CPK Gene Family in the Apple (Malus × domestica) and Other Rosaceae Species and Its Response to Alternaria alternata Infection

As one of the Ca2+ sensors, calcium-dependent protein kinase (CPK) plays vital roles in immune and stress signaling, growth and development, and hormone responses, etc. Recently, the whole genome of apple (Malus × domestica), pear (Pyrus communis), peach (Prunus persica), plum (Prunus mume) and strawberry (Fragaria vesca) in Rosaceae family has been fully sequenced. However, little is known about the CPK gene family in these Rosaceae species. In this study, 123 CPK genes were identified from five Rosaceae species, including 37 apple CPKs, 37 pear CPKs, 17 peach CPKs, 16 strawberry CPKs, and 16 plum CPKs. Based on the phylogenetic tree topology and structural characteristics, we divided the CPK gene family into 4 distinct subfamilies: Group I, II, III, and IV. Whole-genome duplication (WGD) or segmental duplication played vital roles in the expansion of the CPK in these Rosaceae species. Most of segmental duplication pairs in peach and plum may have arisen from the γ triplication (~140 million years ago [MYA]), while in apple genome, many duplicated genes may have been derived from a recent WGD (30~45 MYA). Purifying selection also played a critical role in the function evolution of CPK family genes. Expression of apple CPK genes in response to apple pathotype of Alternaria alternata was verified by analysis of quantitative real-time RT-PCR (qPCR). Expression data demonstrated that CPK genes in apple might have evolved independently in different biological contexts. The analysis of evolution history and expression profile laid a foundation for further examining the function and complexity of the CPK gene family in Rosaceae.
An iterative MapReduce framework for sports-based tweet clustering

In recent years, social media has evolved into a vital source for real-time information. Sports is one of the most popular topics on social media and attracts the attention of users all over the world. However, a large amount of data is generated on a daily basis, making it difficult for the fans to follow the topics of their interest. Clustering of these posts can resolve this issue by retrieving unambiguous and distinct topics. MapReduce is a programming paradigm that is very effective in designing distributed applications that can be deployed on the cloud. Clustering algorithms are generally iterative in nature. The performance gain offered by MapReduce cannot be completely realized by these algorithms due to the inherent architectural bottlenecks associated with iterative tasks. Twister is a MapReduce-based framework designed to minimize these bottlenecks. In this paper, we propose a distributed framework that gathers sports-related tweets and clusters them into distinct topics using the DBSCAN algorithm customized for Twister. The accuracy of the framework was analysed using the precision-recall scoring mechanism to determine the set of DBSCAN and framework parameters that result in the best set of clusters. The performance of our framework is evaluated based on our clustering results and simulations using the MRSim simulator. We expect that this framework could be used as a model for performing topic detection over generic tweets. We have used the domain of sports to establish the proof of this concept.
Attention and the stages of pain processing.

OBJECTIVE Explore the relationships between the four stages of pain processing and attention in chronic pain sufferers. DESIGN A cross-sectional, retrospective study of 736 subjects participating in an outpatient university-based tertiary care pain treatment program. METHODS Self-report measures of pain, pain-related unpleasantness, and suffering (Pain Experience Visual Analog Scales) in conjunction with a structured interview assessing illness behavior (adaptation of the Psychosocial Pain Inventory) and attention (Digit Span subtest of the Wechsler Adult Intelligence Scale-Revised) were employed. OUTCOME MEASURES Separate step-wise multiple regression analyses were conducted using variables that measure each of the four stages of pain processing as predictors, with Digit Span being the criterion variable. RESULTS Multiple regression analyses showed that, of the four pain stages, only suffering and pain behavior were related to attentional performance. Specifically, an individual's perception of lifestyle interference due to pain, level of depression, and the degree of solicitous responses from others each uniquely contributed to Digit Span performance. CONCLUSIONS Treatment interventions specifically targeting suffering and pain behavior may prove efficacious in addressing the attentional problems in chronic pain.
Modeling Telecommunication Infrastructures Integrating Wideband Wireless and Wired Networks

Future telecommunication systems will integrate wireline and wireless networks in a single, advanced infrastructure. It is expected that such systems, in the wireless part, will exploit microcellular architectures in order to guarantee wideband multimedia services and to sustain the continuous growth of subscribers. In such a context, asynchronous transfer mode and Internet protocol are the basic technologies being considered by several studies and field implementations. In both cases, particular care has been devoted to cope with a large number of handoffs. This article considers some aspects of this integrated telecommunication infrastructure, proposing an improvement, for the wired part, of the virtual connection tree and shadow cluster concepts, whereas for the wireless part, a new model for the analysis of a dynamic channel allocation scheme is presented. The obtained analytical results, confirmed by simulation in a wide range of load conditions and user mobility, allow one to evaluate the main system performance parameters in terms of blocking probability of new calls, handoff-blocking probability, and forced termination probability.
3D reconstruction method of the proximal femur and shape correction

The aim of this work is to present a 3D reconstruction method of the proximal femur shape using contours identification from pairs of 2D X-ray radiographs without any prior acknowledge. 3D personalized model was reconstructed following a processing chain of seven different steps. After localization of the 2D contours on the images and the matching points of these contours, a 3D contour is generated using an algorithm based on a mathematical model. Thus, with a reduced number of pairs of images, we reconstruct a 3D points cloud, which enables obtaining a closed 3D surface. The accuracy of our approach was evaluated by comparing the reconstruction result with the 3D CT-scan reconstruction of cadaveric proximal femur. The estimated error shows that it is possible to rebuild the proximal femur shape from a limited number of radiographs.
A graph-based cross-lingual projection approach for spoken language understanding portability to a new language

The portability of spoken language understanding to a new language can be improved by the results of automatic translation. However, the translation errors can cause the falling-off in the quality of the target language system. This paper proposes a graph-based projection approach to improve the robustness against the translation errors in cross-lingual spoken language understanding. The experimental results show that our proposed approach can significantly improve the performances of the task in a new language.
Transfer Learning for Multi-agent Coordination

Transfer learning leverages an agent’s experience in a source task in order to improve its performance in a related target task. Recently, this technique has received attention in reinforcement learning settings. Training a reinforcement learning agent on a suitable source task allows the agent to reuse this experience to significantly improve performance on more complex target problems. Currently, reinforcement learning transfer approaches focus almost exclusively on speeding up learning in single agent systems. In this paper we investigate the potential of applying transfer learning to the problem of agent coordination in multi-agent systems. The idea underlying our approach is that agents can determine how to deal with the presence of other agents in a relatively simple training setting. By then generalizing this knowledge, the agents can use this experience to speed up learning in more complex multi-agent learning tasks.
From Atoms to the Solar System: Generating Lexical Analogies from Text

A lexical analogy is two pairs of words (w1, w2) and (w3, w4) such that the relation between w1 and w2 is identical or similar to the relation between w3 and w4. For example, (abbreviation, word) forms a lexical analogy with (abstract, report), because in both cases the former is a shortened version of the latter. Lexical analogies are of theoretic interest because they represent a second order similarity measure: relational similarity. Lexical analogies are also of practical importance in many applications, including textunderstanding and learning ontological relations. This thesis presents a novel system that generates lexical analogies from a corpus of text documents. The system is motivated by a well-established theory of analogymaking, and views lexical analogy generation as a series of three processes: identifying pairs of words that are semantically related, finding clues to characterize their relations, and generating lexical analogies by matching pairs of words with similar relations. The system uses a dependency grammar to characterize semantic relations, and applies machine learning techniques to determine their similarities. Empirical evaluation shows that the system performs remarkably well, generating lexical analogies at a precision of over 90%.
Coupled Ocean Circulation and Wind-Wave Models With Data Assimilation Using Altimeter Statistics

Coupled ocean wave and circulation models have been implemented under the IOEPAS program (Integrated Ocean Environment Prediction and Assimilation System). The third generation wave model, WAM and the South East Asian Ocean Model (SEAOM) based on the Princeton Ocean Model (POM) are coupled through wave-current interaction terms. The modeling capability has been improved from the altimeter data in terms of better boundary inputs. Data assimilation of altimeter data and physical measurements, if any, is a new feature of the prediction system. WAM and SEAOM run in parallel, exchanging data as they execute. This work is aimed at a comprehensive ocean modeling system for the regional seas surrounding Singapore waters.
Leveraging compression in the tableau data engine

Data sets are growing rapidly and there is an attendant need for tools that facilitate human analysis of them in a timely manner. To help meet this need, column-oriented databases (or "column stores") have come into wide use because of their low latency on analytic workloads. Column stores use a number of techniques to produce these dramatic performance techniques, including the ability to perform operations directly on compressed data. In this paper, we describe how the Tableau Data Engine (an internally developed column store) leverages a number of compression techniques to improve query performance. The approach is simpler than existing systems for operating on compressed data and more unified, removing the necessity for custom data access mechanisms. The approach also uses some novel metadata extraction techniques to improve the choices made by the system's run-time optimizer.