Miracle-dz/UniFAR-FTUs huggingface dataset:
Prognostic significance of circulating tumor cells in patients with metastatic colorectal cancer.

BACKGROUND We demonstrated that circulating tumor cell (CTC) number at baseline and follow-up is an independent prognostic factor in metastatic colorectal cancer (mCRC). This analysis was undertaken to explore whether patient and treatment characteristics impact the prognostic value of CTCs. PATIENTS AND METHODS CTCs were enumerated with immunomagnetic separation from the blood of 430 patients with mCRC at baseline and on therapy. Patients were stratified into unfavorable and favorable prognostic groups based on CTC levels of > or = 3 or <3 CTCs/7.5 ml, respectively. Subgroups were analyzed by line of treatment, liver involvement, receipt of oxaliplatin, irinotecan, or bevacizumab, age, and Eastern Cooperative Oncology Group performance status (ECOG PS). RESULTS Seventy-one percent of deaths have occurred. Median follow-up for living patients is 25.8 months. For all patients, progression-free survival (PFS) and overall survival (OS) for unfavorable compared with favorable baseline CTCs is shorter (4.4 versus 7.8 m, P = 0.004 for PFS; 9.4 versus 20.6 m, P < 0.0001 for OS). In all patient subgroups, unfavorable baseline CTC was associated with inferior OS (P < 0.001). In patients receiving first- or second-line therapy (P = 0.003), irinotecan (P = 0.0001), having liver involvement (P = 0.002), >/=65 years (P = 0.0007), and ECOG PS of zero (P = 0.04), unfavorable baseline CTC was associated with inferior PFS. CONCLUSION Baseline CTC count is an important prognostic factor within specific subgroups defined by treatment or patient characteristics.
Design of a Web System Using Context-Awareness

Currently, user requirements of the World Wide Web are diverse, growing more so with each passing year. Furthermore, each web user wants to retrieve data and/or merchandise more conveniently and quickly. As a result of different search criteria and dispositions of web users, data retrieval leads to unnecessarily repetitious operations in the use of the environment implemented by web designers. In this paper, we will suggest a system that analyzes user patterns on the Web using the technique of log file analysis and then transfers the web site information of users effectively. We analyzed the log files for customer data using EC-Miner, one of the tools used in data mining, and offered an appropriate layout, corresponding with personalization by giving a weight to each transport path.
Multi-layer feature extractions for image classification — Knowledge from deep CNNs

Recently, there has been considerable research into the application of deep learning to image recognition. Notably, deep convolutional neural networks (CNNs) have achieved excellent performance in a number of image classification tasks, compared with conventional methods based on techniques such as Bag-of-Features (BoF) using local descriptors. In this paper, to cultivate a better understanding of the structure of CNN, we focus on the characteristics of deep CNNs, and adapt them to SIFT+BoF-based methods to improve the classification accuracy. We introduce the multi-layer structure of CNNs into the classification pipeline of the BoF framework, and conduct experiments to confirm the effectiveness of this approach using a fine-grained visual categorization dataset. The results show that the average classification rate is improved from 52.4% to 69.8%.
A Mechanism for Reproducing Human Gait in Footwear Comfort Tests

This work describes the development of a conceptual articulated mechanism, which aims to reproduce the human foot movement during a walking gait in comfort tests for evaluating the quality of footwear. The main goal is to emulate such movement with relatively few degrees of freedom, so that the automatic control of the mechanism is facilitated. The general design of the mechanism is developed by reviewing current projects in the robotic field and comparing them to the anatomy of a real foot. Also, experimental data from video gammetry tests are employed to define the actual dimensions and the final configuration of the proposed system.
Small RNA class transition from siRNA/piRNA to miRNA during pre-implantation mouse development

Recent studies showed that small interfering RNAs (siRNAs) and Piwi-interacting RNA (piRNA) in mammalian germ cells play important roles in retrotransposon silencing and gametogenesis. However, subsequent contribution of those small RNAs to early mammalian development remains poorly understood. We investigated the expression profiles of small RNAs in mouse metaphase II oocytes, 8–16-cell stage embryos, blastocysts and the pluripotent inner cell mass (ICM) using high-throughput pyrosequencing. Here, we show that during pre-implantation development a major small RNA class changes from retrotransposon-derived small RNAs containing siRNAs and piRNAs to zygotically synthesized microRNAs (miRNAs). Some siRNAs and piRNAs are transiently upregulated and directed against specific retrotransposon classes. We also identified miRNAs expression profiles characteristic of the ICM and trophectoderm (TE) cells. Taken together, our current study reveals a major reprogramming of functional small RNAs during early mouse development from oocyte to blastocyst.
Fast Subgoaling for Pathfinding via Real-Time Search

Real-time heuristic search is a standard approach to pathfind- ing when agents are required to make decisions in a bounded, very short period of time. An assumption usually made in the development and evaluation of real-time algorithms is that the environment is unknown. Nevertheless, in many interesting applications such as pathfinding for automnomous characters in video games, the environment is known in advance. Recent real-time search algorithms such as D LRTA* and kNN LRTA* exploit knowledge about the environment while pathfinding under real-time constraints. Key to those algorithms is the computation of subgoals in a preprocessing step. Subgoals are subsequently used in the online planning phase to obtain high-quality solutions. Preprocessing in those algorithms, however, requires significant computation. In this paper we propose a novel preprocessing algorithm that generates subgoals using a series of backward search episodes carried out from potential goals. The result of a single backward search episode is a tree of subgoals that we then use while planning online. We show the advantages of our approach over state-of-the-art algorithms by carrying out experiments on standard real-time search benchmarks.
Data Mining by Soft Computing Methods for The Coronary Heart Disease Database

For improvement of data mining technology, the advantages and disadvantages on respective data mining methods should be discussed by comparison under the same condition. For this purpose, the Coronary Heart Disease database (CHD DB) was developed in 2004, and the data mining competition was held in the International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES). In the competition, two methods based on soft computing were presented. In this paper, we report the overview of the CHD DB and the soft computing methods, and discuss the features of respective methods by comparison of the experimental results.
Data Encryption Based on Multi-granularity Reversible Cellular Automata

Traditional reversible cellular automata (RCA) is fit for cryptography for its rules being an affine function, i. e. one reversible CA’ s rule can be applied in encryption process while another counterpart rule can be applied in decryption process. However, there is few number of traditional RCA; for example, there are only six reversible rules in elementary CA. This property caused it cannot meet large key-space of cryptography. In this paper, an extended RCA is proposed aimed to be applied in cryptography. Analysis indicates that the extended RCA can be obtained by a pair of complementary traditional rule. Furthermore, in order to improve the complexity of CA’ s dynamics, which is a crux in cryptography, traditional CA model is replaced by multi-granularity cellular automata (MGCA). In MGCA, different granularity cells have their own rules. Based on MGCA and RCA a cryptography algorithm that is proposed which called MGRCA. In MGRCA, cells have different granularity and can adjust their granularity dynamically by “ split-recombination” behavior during the process of encryption and decryption. Unlike DES, the length of block in MGRCA can be adjusted by user. The analysis results show that the cryptosystem can resist brute attack and differential attack, and also has high security. The hardware that shared by encryption and decryption caused the cryptosystem has a strong practicability.
Rotation estimation for a satellite from Sun sensors

We develop a method to reconstruct the rotation motion of a satellite from Sun sensor measurements. Mathematically, the estimation objective is formulated as the problem of phase reconstruction for a 2-dimensional periodic function. A computationally straightforward solution is proposed. Theoretical developments allow to assess the robustness to model imperfections, sampling and noise. Simulation results illustrate practically obtainable performance.
Spatial Correlation of Radio Waves for Multi-Antenna Applications in Indoor Multipath Environments

This paper presents the results of a concerted effort in characterizing the spatial correlation of radio waves detected by a multi-element antenna system in indoor environments. The number of arriving paths and their directions are first studied through a series of measurements. The results are then used in a simulation to obtain spatial correlation indoors. It is found that generally an antenna array positioned near the center of the room will experience more rapidly decreasing correlation with spacing, which is a desired trait. In addition, a linear array oriented perpendicular to the length of the room in general shows better characteristics in terms of faster slope of correlation compared to the one in parallel with the length of the room. The average correlation indoors is also found to be similar to the correlation function arising from the two-dimensional circularly distributed random scatterers proposed by Clarke. For practical implementation of antenna arrays in small terminals for indoor wireless applications, it is suggested that the inter-element spacing be made 0.3? in the least.
De Novo Initiation of RNA Synthesis by the Arterivirus RNA-Dependent RNA Polymerase

ABSTRACT All plus-strand RNA viruses encode an RNA-dependent RNA polymerase (RdRp) that functions as the catalytic subunit of the viral replication/transcription complex, directing viral RNA synthesis in concert with other viral proteins and, sometimes, host proteins. RNA synthesis essentially can be initiated by two different mechanisms, de novo initiation and primer-dependent initiation. Most viral RdRps have been identified solely on the basis of comparative sequence analysis, and for many viruses the mechanism of initiation is unknown. In this study, using the family prototype equine arteritis virus (EAV), we address the mechanism of initiation of RNA synthesis in arteriviruses. The RdRp domains of the members of the arterivirus family, which are part of replicase subunit nsp9, were compared to coronavirus RdRps that belong to the same order of Nidovirales, as well as to other RdRps with known initiation mechanisms and three-dimensional structures. We report here the first successful expression and purification of an arterivirus RdRp that is catalytically active in the absence of other viral or cellular proteins. The EAV nsp9/RdRp initiates RNA synthesis by a de novo mechanism on homopolymeric templates in a template-specific manner. In addition, the requirements for initiation of RNA synthesis from the 3′ end of the viral genome were studied in vivo using a reverse genetics approach. These studies suggest that the 3′-terminal nucleotides of the EAV genome play a critical role in viral RNA synthesis.
Exploring Longitudinal Use of Activity Trackers

Networked activity trackers have become an important tool for assessing physical activity, as part of behavior change interventions, within epidemiological studies, or driven by personal interest. However the use of activity trackers is often abandoned after a few months, and their effect on health behavior is unclear. We conducted a longitudinal study observing the use of activity trackers by 34 patients recovering from a myocardial infarction. The patients used the tracker continuously for up to more than one year. We observe two usage patterns: Regular use on some but not all days per week is observed for all patients and remains consistent during the usage period. Uninterrupted use on many successive days is observed for some patients only, happens only once per patient, and mostly at the beginning of the use. We hypothesize that the activity tracker is particularly used as a motivational tool by objectively measuring one's physical activity, and only some people use it as a tool for learning one's behavior.
Context-Awareness Based Multidimensional Personalized Recommendation

This paper proposes the CaPeR which is the context-awareness based architecture for personalized recommendation. The CaPeR provides the personalized recommender engine and the peer-to-peer context management framework. With a hybrid approach, the personalized recommender engine combines those contexts into the decisions on recommendations to get more comprehensive recommendation effectiveness. Owing to the distribution and limit of resources of the mobile terminals, the CaPeR constructs a context management framework based on the mechanism of Registration query. Through designing an efficient mechanism of message intercommunications, the CaPeR builds up the architecture for sharing and fusing of contexts to satisfy the requirements of recommendations. At the end of this paper, the Flowing Desktop is given a description as the prototype of the CaPeR.
Enteromorpha detection in aerial images using support vector machines

In this paper, we introduce a simple approach for detecting enteromorpha based on statistical learning of image features using support vector machines (SVM). The approach first classifies an enteromorpha image into two classes: enteromorpha and background. Then it extracts features from those two classes and uses them for training the SVM model. Finally, the predicting process is carried out in a pixel by pixel manner using the learned model. The model uses saturation in NTSC color space or filtered images by Gabor filter as the input features while the output class label is treated as 1 or 2 (enteromorpha or background), which is assigned to the location that is being predicted. In fact, this application is only a two-class pattern classification problem. Experimental results show that the method can be effectively applied to detecting enteromorpha in aerial images.
Implementing Energetically Sustainable Routing Algorithms for Autonomous WSNs

Energy harvesting technologies make it possible to exploit environmental power to realize autonomous wireless sensor networks with unlimited lifetime. However, the actual autonomy of a wireless network depends on the energetic sustainability of its workload that depends, on its turn, on the algorithms adopted to route packets from the sensor nodes to the sink. A theoretical framework for assessing and optimizing the energetic sustainability of routing algorithms has been recently proposed. In this paper we discuss the actual applicability of optimal energetically sustainable routing algorithms and the issues raised by non-stationary environmental conditions. Furthermore, we present a distributed algorithm, running on sensor nodes, that is able to re-compute optimal routing tables on the field in order to adapt to the actual distribution of environmental power.
Identification of a Novel Small Non-Coding RNA Modulating the Intracellular Survival of Brucella melitensis

Bacterial small non-coding RNAs (sRNAs) are gene expression modulators respond to environmental changes, stressful conditions, and pathogenesis. In this study, by using a combined bioinformatic and experimental approach, eight novel sRNA genes were identified in intracellular pathogen Brucella melitensis. BSR0602, one sRNA that was highly induced in stationary phase, was further examined and found to modulate the intracellular survival of B. melitensis. BSR0602 was present at very high levels in vitro under stresses similar to those encountered during infection in host macrophages. Furthermore, BSR0602 was found to be highly expressed in the spleens of infected mice, suggesting its potential role in the control of pathogenesis. BSR0602 targets the mRNAs coding for gntR, a global transcriptional regulator, which is required for B. melitensis virulence. Overexpression of BSR0602 results in distinct reduction in the gntR mRNA level. B. melitensis with high level of BSR0602 is defective in bacteria intracellular survival in macrophages and defective in growth in the spleens of infected mice. Therefore, BSR0602 may directly inhibit the expression of gntR, which then impairs Brucellae intracellular survival and contributes to Brucella infection. Our findings suggest that BSR0602 is responsible for bacterial adaptation to stress conditions and thus modulate B. melitensis intracellular survival.
Evaluation of textural features for multispectral images

Remote sensing is a field that has wide use, leading to the fact that it has a great importance. Therefore performance of selected features plays a great role. In order to gain some perspective on useful textural features, we have brought together state-of-art textural features in recent literature, yet to be applied in remote sensing field, as well as presenting a comparison with traditional ones. Therefore we selected most commonly used textural features in remote sensing that are grey-level co-occurrence matrix (GLCM) and Gabor features. Other selected features are local binary patterns (LBP), edge orientation features extracted after applying steerable filter, and histogram of oriented gradients (HOG) features. Color histogram feature is also used and compared. Since most of these features are histogram-based, we have compared performance of bin-by-bin comparison with a histogram comparison method named as diffusion distance method. During obtaining performance of each feature, k-nearest neighbor classification method (k-NN) is applied.
A Graph-based Approach for Higher Order Gis Topological Analysis

© 2004 International Society for Photogrammetry and Remote Sensing. All rights reserved. Retrieving structured information from an initial random collection of objects may be carried out by understanding the spatial arrangement between them, assuming no prior knowledge about those objects. As far as topology is concerned, contemporary desktop GIS packages do not generally support further analysis beyond adjacency. Thus, one of the original motivations of this work was to develop new ideas for scene analysis by building up a graph-based technique for better interpretation and understanding of spatial relationships between GIS vector-based objects beyond its first level of adjacency; the final aim is the performance of some kind of local feature organization into a more meaningful global scene by using graph theory. As the example scenario, a LiDAR data set is being used to test the technique that we plan to develop and implement. After the generation of the respective TIN, two different binary classifications were applied to the TIN facets (based on two different slope thresholds) and TIN facets have been aggregated into homogeneous polygons according to their slope characteristics. A graph-based clustering procedure inside these polygonal regions, by establishing a neighbourhood graph, followed by the delineation of cluster shapes and the derivation of cluster characteristics in order to obtain higher level geographic entities information (regarding sets of buildings, vegetation areas, and say, land-use parcels) is object of further work. The results we are expecting to obtain might be useful to support land-use mapping, image understanding or, generally speaking, to support clustering analysis and generalization processes.
Exploration on electrical and electronic teaching reform by information technology

This paper deals with how to use information technology to promote the teaching reform in electrical and electronic courses, so as to improve students' comprehensive ability. Firstly, the situation of electrical and electronic education, electrical and electronic curriculum and the teaching reform in electrical and electronic courses are given. Then, the relationship between information technology and electrical and electronic teaching reform is discussed. Finally, the ways to improve electrical and electronic teaching are proposed. This effort is to improve students' comprehensive innovation ability by means of information technology and putting the proposed methods into practice.
Spring 2014 ATTACHMENT REPRESENTATIONS AND MOTHER-CHILD DIALOGUE

This is to certify that the thesis/dissertation prepared By Entitled For the degree of Is approved by the final examining committee: To the best of my knowledge and as understood by the student in the 7KHVLV'LVVHUWDWLRQ$JUHHPHQW 3XEOLFDWLRQ'HOD\DQGCHUWLILFDWLRQDisclaimer (Graduate School Form), this thesis/dissertation DGKHUHVWRWKHSURYLVLRQVRIPurdue University's " Policy on Integrity in Research " and the use of copyrighted material. ii To my Mother Mammai iii ACKNOWLEDGEMENTS This thesis would not have been possible without the guidance of my respected professors. I would like to thank my major professor, Dr. German Posada, for allowing me to enjoy data collection in natural settings, Dr. Karen Diamond, for helping me identify structure in these settings, and Dr. Sharon Christ, for teaching me how to analyze all this complexity. I am deeply grateful to Fulbright Foreign Student Program for the opportunity to receive top scientific training and learn from people from all over the world. With the emerging linguistic competencies of the child, dialogue becomes a regular part of mother-child everyday interactions, and may serve as one of the mechanisms of transmission of attachment in early and middle childhood. The goal of this study was to investigate the hypothesis about the co-constructive nature of children's knowledge of secure base script. Formation of the secure base script is a result of child-caregiver interactions that have been consolidated into a unit of knowledge available for use in attachment related situations. Individual attachment narratives and mother-child co-constructed narratives were collected from 86 mother-child dyads when children were 3.7 years old. Using Structural Equation Modeling, this study tested the relationships between maternal knowledge of secure base script, child knowledge of secure base script, and maternal dialogic co-construction skills, observed as mothers helped their children with narrative construction. Further, it was tested if maternal co-construction skills mediated the relationship between maternal secure base script knowledge and child secure base script knowledge. Confirming the co-constructive nature of children's attachment representations, a significant relationship was found between maternal co-construction skills and child knowledge of secure base script. A trend between maternal viii access to secure base script and maternal co-construction skills was found, suggesting that maternal attachment representations may be related to the way mothers converse with their children. No significant effects of maternal access to secure base script on child access to secure base script were found when examining the relationship directly and via co-construction skills. Implications of these findings are discussed …