Miracle-dz/UniFAR-FTUs huggingface dataset:
In vivo gene silencing (with siRNA) of pulmonary expression of MIP‐2 versus KC results in divergent effects on hemorrhage‐induced, neutrophil‐mediated septic acute lung injury

Lung injury in trauma patients exposed to a secondary infectious/septic challenge contributes to the high morbidity/mortality observed in this population. Associated pathology involves a dys‐regulation of immune function, specifically, sequestration of activated polymorphonuclear neutrophils (PMN) in the lungs. The targeting of PMN is thought to involve the release of chemokines from cells within the local environment, creating a concentration gradient along which PMN migrate to the focus of inflammation. Keratinocyte‐derived chemokine (KC) and macrophage‐inflammatory protein‐2 (MIP‐2) are murine neutrophil chemokines identified as playing significant but potentially divergent roles in the pathogenesis of acute lung injury (ALI). In the current study, we examined the contribution of local pulmonary cells to the production of KC and MIP‐2 and the pathogenesis of ALI. We hypothesized that local silencing of KC or MIP‐2, via the local administration of small interference RNA (siRNA) against KC or MIP‐2, following traumatic shock/hemorrhage (Hem), would suppress signaling for PMN influx to the lung, thereby reducing ALI associated with a secondary septic challenge (cecal ligation and puncture). Assessment of siRNA local gene silencing was done in green fluorescent protein (GFP)‐transgenic, overexpressing mice. A marked suppression of GFP expression was observed in the lung 24 h following intratracheal (i.t.) instillation of GFP siRNA, which was not observed in the liver. To test our hypothesis, siRNA against KC or MIP‐2 (75 ug/C3H/Hen mouse) was instilled (i.t.) 2 h post‐Hem (35 mm Hg for 90 min, 4× LRS Rx.). Twenty‐four hours after, mice were subjected to septic challenge and then killed 24 h later. i.t. MIP‐2 siRNA significantly (P<0.05, ANOVA‐Tukey’s test, n=5–6/group) reduced tissue and plasma interleukin (IL)‐6, tissue MIP‐2 (enzyme‐linked immunosorbent assay), as well as neutrophil influx [myeloperoxidase (MPO) activity]. In contrast, KC siRNA treatment reduced plasma KC, tissue KC, and IL‐6 but produced no significant reduction in plasma IL‐6 or MPO. Neither treatment reduced tissue or plasma levels of tumor necrosis factor α compared with vehicle. These data support not only our hypothesis that local pulmonary chemokine production of MIP‐2, to a greater extent than KC, contributes to the pathogenesis of PMN‐associated ALI following Hem but also the use of siRNA as a potential therapeutic.
Training-Efficient Feature Map for Shift-Invariant Kernels

Random feature map is popularly used to scale up kernel methods. However, employing a large number of mapped features to ensure an accurate approximation will still make the training time consuming. In this paper, we aim to improve the training efficiency of shift-invariant kernels by using fewer informative features without sacrificing precision. We propose a novel feature map method by extending Random Kitchen Sinks through fast data-dependent subspace embedding to generate the desired features. More specifically, we describe two algorithms with different tradeoffs on the running speed and accuracy, and prove that O(l) features induced by them are able to perform as accurately as O(l2) features by other feature map methods. In addition, several experiments are conducted on the real-world datasets demonstrating the superiority of our proposed algorithms.
Prostaglandin E2 receptor EP4 as the common target on cancer cells and macrophages to abolish angiogenesis, lymphangiogenesis, metastasis, and stem-like cell functions

We previously established that COX‐2 overexpression promotes breast cancer progression and metastasis. As long‐term use of COX‐2 inhibitors (COX‐2i) can promote thrombo‐embolic events, we tested an alternative target, prostaglandin E2 receptor EP4 subtype (EP4), downstream of COX‐2. Here we used the highly metastatic syngeneic murine C3L5 breast cancer model to test the role of EP4‐expressing macrophages in vascular endothelial growth factor (VEGF)‐C/D production, angiogenesis, and lymphangiogenesis in situ, the role of EP4 in stem‐like cell (SLC) functions of tumor cells, and therapeutic effects of an EP4 antagonist RQ‐15986 (EP4A). C3L5 cells expressed all EP receptors, produced VEGF‐C/D, and showed high clonogenic tumorsphere forming ability in vitro, functions inhibited with COX‐2i or EP4A. Treating murine macrophage RAW 264.7 cell line with COX‐2i celecoxib and EP4A significantly reduced VEGF‐A/C/D production in vitro, measured with quantitative PCR and Western blots. Orthotopic implants of C3L5 cells in C3H/HeJ mice showed rapid tumor growth, angiogenesis, lymphangiogenesis (CD31/LYVE‐1 and CD31/PROX1 immunostaining), and metastasis to lymph nodes and lungs. Tumors revealed high incidence of EP4‐expressing, VEGF‐C/D producing macrophages identified with dual immunostaining of F4/80 and EP4 or VEGF‐C/D. Celecoxib or EP4A therapy at non‐toxic doses abrogated tumor growth, lymphangiogenesis, and metastasis to lymph nodes and lungs. Residual tumors in treated mice revealed markedly reduced VEGF‐A/C/D and phosphorylated Akt/ERK proteins, VEGF‐C/D positive macrophage infiltration, and proliferative/apoptotic cell ratios. Knocking down COX‐2 or EP4 in C3L5 cells or treating cells in vitro with celecoxib or EP4A and treating tumor‐bearing mice in vivo with the same drug reduced SLC properties of tumor cells including preferential co‐expression of COX‐2 and SLC markers ALDH1A, CD44, OCT‐3/4, β‐catenin, and SOX‐2. Thus, EP4 is an excellent therapeutic target to block stem‐like properties, angiogenesis, and lymphangiogenesis induced by VEGF‐A/C/D secreted by cancer cells and tumor infiltrating macrophages.
Variants of Longest Common Subsequence Problem

Longest common subsequence is a classical problem in computer science. It is a problem of finding longest subsequence common to the given input sequences [2]. Information in various applications such as bioinformatics is expressed as sequence of characters (e.g. DNA or gene sequence). LCS is a similarity measure of these character sequences and has practical applications in genomics and molecular biology. For example, LCS is used in finding evidence for species common origin and also used in gene discovery and evolutionary tree construction. Motivated by practical scenarios in applications of genomics and computational biology, different variants of LCS problem were introduced for more accurate measure of similarity. This project is to study the following variants of longest common subsequence. 1. Constrained LCS: Multiple substring exclusive constraints [3]. 2. LCS in k-length substrings [2]. 3. Variable-gapped LCS [1]. In this project, these variants are implemented and compared with the dynamic programming approach of standard LCS problem.
Unsupervised multiobjective design for weighted median filters using genetic algorithm

In this paper, a new unsupervised design method of the weighted median filter (WMF) is proposed for recovering images from impulse noise. A design problem of WMFs is to determine a suitable window shape, and an appropriate weight for each element in the window. The purpose of the filter for the noise removal is generally to estimate the original values precisely for corrupted pixels while preserving the original values of non-corrupted pixels. WMF is required to output the image with higher preservation quality and higher restoration quality, however, these qualities often have a trade-off relation. Here, we formulate the design of WMF as a multi-objective optimization problem that treats the preservation performance and the restoration performance as trade-off functions. Through the experiments, we show our method obtains a wide variety of filters that have the high preservation performance or the high restoration performance at one search process. In addition, we also discuss how to select a good set of sophisticated filters from the designed filters.
Nonverbal Behavior Analysis Chapter 12 Nonverbal Behavior Analysis

The last decade marked the emergence of the automated understanding of face-to-face social interaction as a research problem in computing. IM2 was originally focused on meetings (a quintessential form of interaction), and so over the years a body of work directed towards analyzing and inferring a variety of behaviors and interactions resulted from the project. One key aspect of the IM2 work has been the use of nonverbal communication as measurable evidence of social phenomena. The role of nonverbal behavioral cues (gaze, facial expressions, gestures, vocalizations, postures, etc.) as carriers of socially relevant information has been the subject of research in psychology and communication for decades. Furthermore, computing research has developed a large number of approaches aimed at automatic analysis and synthesis of gaze, facial expressions, gestures, and paralanguage. Over 12 years, IM2 enabled both the development of perceptual technologies (computer vision and signal processing) to extract behavioral cues, and their integration to address questions connected to inference of various social variables in increasingly diverse situations (Gatica-Perez, 2009, Vinciarelli et al., 2009b). Table 12.1 shows a timeline of some of the investigated research lines. The initial research that linked audiovisual perception and social behavior in IM2 can be traced back to 2002 with the initial use of the Smart Meeting Room as a sensing platform to study small group interaction (refer to Chap. 1). The original contribution was the dual realization that groups could be studied as units (as opposed to considering individuals as the ultimate analysis target), and that characterizing group behavior computationally could be achieved through the integration of machine perception technologies with findings in the small group research literature. Such a concept took shape through the definition of " meeting actions " , a categorization system that related speaking turns and visual activity, and a framework for recognition using audio and video observations and Hidden Markov Models (McCowan et al., 2005).
Novel Molecular Beacon Probe-Based Real-Time RT-PCR Assay for Diagnosis of Crimean-Congo Hemorrhagic Fever Encountered in India

Crimean-Congo hemorrhagic fever (CCHF) is an emerging zoonotic disease in India and requires immediate detection of infection both for preventing further transmission and for controlling the infection. The present study describes development, optimization, and evaluation of a novel molecular beacon-based real-time RT-PCR assay for rapid, sensitive, and specific diagnosis of Crimean-Congo hemorrhagic fever virus (CCHFV). The developed assay was found to be a better alternative to the reported TaqMan assay for routine diagnosis of CCHF.
Do Institutional Factors Matter for the Speed of Disinflation

A central issue for macroeconomic policy is the optimal speed of disinflation. The cold turkey view states that a credible disinflation is less costly if it is quick. An alternative view is that gradualism is less costly because of frictions in wage and price setting. Institutional factors, such as central bank independence or labor market structure, are argued to be important in influencing expectations or wage responsiveness. The paper examines whether various institutional factors explain the disinflation path taken by 21 OECD countries. The cross country analysis finds that institutional factors have no influence on the speed of disinflation; most important is the level of inflation before the disinflation.
Comparison of Modified Kneser-Ney and Witten-Bell smoothing techniques in statistical language model of Bahasa Indonesia

Smoothing is one technique to overcome data sparsity in statistical language model. Although in its mathematical definition there is no explicit dependency upon specific natural language, different natures of natural languages result in different effects of smoothing techniques. This is true for Russian language as shown by Whittaker [2]. In this paper, We compared Modified Kneser-Ney and Witten-Bell smoothing techniques in statistical language model of Bahasa Indonesia. We used train sets of totally 22M words that we extracted from Indonesian version of Wikipedia. As far as we know, this is the largest train set used to build statistical language model for Bahasa Indonesia. The experiments with 3-gram, 5-gram, and 7-gram showed that Modified Kneser-Ney consistently outperforms Witten-Bell smoothing technique in term of perplexity values. It is interesting to note that our experiments showed 5-gram model for Modified Kneser-Ney smoothing technique outperforms that of 7-gram. Meanwhile, Witten-Bell smoothing is consistently improving over the increase of n-gram order.
Fletcher : A Web Service for Planning

Artificial intelligence planning has traditionally been realized in applications that are tightly bound to planning systems. In this paper, we describe Fletcher, a Web service that exposes and augments a planning system. Fletcher provides an alternative to including a planner implementation within applications. In addition to benefiting applications that need to execute planning tasks, Fletcher gives human authors the option to be involved in the planning process directly.
Introducing Unobserved Heterogeneity in Earnings Mobility

This paper introduces and describes unobserved heterogeneity in earnings quintiles transition matrices in the US. Unobserved heterogeneity is found to play a crucial role in earnings mobility. Each individual is attracted, given his characteristics, towards a specific zone of the distribution. At the stationnary equilibrium, the earnings quintiles distribution is thus segmented. Interestingly, while the level of earnings mobility has remained quite stable since 1970, the width of these zones has decreased, such that this segmentation was more pronounced in the 80's and the 90's than in the 70's, especially in the middle of the quintiles distribution.
On an agent-based architecture for collaborative continuous auditing

Information technology has changed the way organizations do business and are driving the imperative changes in accounting and auditing. One of the fundamental changes is a move toward increased emphasis on internal control over the reliability of information systems. For a variety of reasons, it becomes necessary to examine and audit the transactions to understand the integrity of the information generated by an information system. Today the widespread availability of inter-networking dramatically makes it possible to increase the frequency of periodic audits by redesigning the auditing architecture around the idea of continuous auditing. In this paper, we have proposed an agent-based architecture to explore the idea of collaborative continuous auditing. For the evaluation of the architecture, JADE is chosen as the platform for implementation.
Ear biometrics: a survey of detection, feature extraction and recognition methods

The possibility of identifying people by the shape of their outer ear was first discovered by the French criminologist Bertillon, and refined by the American police officer Iannarelli, who proposed a first ear recognition system based on only seven features. The detailed structure of the ear is not only unique, but also permanent, as the appearance of the ear does not change over the course of a human life. Additionally, the acquisition of ear images does not necessarily require a person's cooperation but is nevertheless considered to be non-intrusive by most people. Owing to these qualities, the interest in ear recognition systems has grown significantly in recent years. In this survey, the authors categorise and summarise approaches to ear detection and recognition in 2D and 3D images. Then, they provide an outlook over possible future research in the field of ear recognition, in the context of smart surveillance and forensic image analysis, which they consider to be the most important application of ear recognition characteristic in the near future.
Investigation into effectiveness of rough sets in prediction of enzyme and protein structure classes

Among various methods in protein function prediction, rough set has recently been applied to prediction of protein structural classes. However, this was a blind application on a single but small data set of high homology, which did not consider investigation of various parameters in the rough set. The aim of this paper is therefore to study rough set in the area through comprehensive and consistent analysis and then to present a practical strategy in the rough set-based protein function prediction. To achieve this aim, three different data sets were considered: the first data set for prediction of six main enzyme classes, and other two for prediction of structural classes. Boolean reasoning, Entropy scaling and Equal frequency binning were used for discretization along with two methods for producing reducts and rules, genetic and Johnson's algorithms. It can be seen that the predictive accuracies were poor for the enzyme dataset whereas it performed better at prediction of the protein structural classes. It is also observed that the dataset with low homology produced poor accuracies than the dataset with high homology. Furthermore, various parameters and methods used in the rough set were sensitive to the problems in the area, as well as the data sets of low and high homology and different number of the features. The results appear to indicate that the equal frequency-based approach combined with genetic algorithm yields higher prediction. However, other methods such as Boolean reasoning with the genetic algorithm are also found to be promising. Further investigation will provide a practical strategy that can be used in the rough set-based protein function prediction as well as other areas of Bioinformatics.
Ideological (mis)match? Mapping extreme right ideological discourse and voter preferences.

The existing literature has long mused over the questions of who belongs and who does not belong to the extreme right party family, as well as why some extreme right parties seem to be consistently - or occasionally - more successful than others. For decades, scholars have failed to reach a consensus regarding the definition of the extreme right, used a plethora of labels to describe it, and disagreed on the defining characteristics of the party family. In order to progress from this conundrum, this thesis explores the question of whether and how the extreme right can be defined as a multi-dimensional party family based on two strategic-discursive dimensions, and the extent to which the location of each individual member of the extreme right family will affect the number and the nature of the voters whom they will attract. This question is answered - and resulting model tested - in Great Britain, Germany, and France. The thesis combines a variety of quantitative and qualitative methods including text analysis of party manifestoes, face-to-face interviews of extreme right party elites (in two countries), and analysis of survey data. Our model stipulates that extreme right parties emphasise different conceptions of an authoritarian dimension (ranging from a social/reactionary to an institutional/repressive pole) and a negative conception of identity (spanning from a civic/populist to a cultural/xenophobic scale). Based on this bi-dimensional conceptual map, four dominant sub-types of extreme right parties can be identified, all of which are represented in the three party systems, and evidenced by both party manifestoes and elites ' discourse. We also show that the different positions espoused by each party have an impact on the ideological identity of the party, intra-extreme right party competition, the types of voters each party attracts and ultimately, the level of electoral success it obtains.
Improved algorithm for computing all the DC operating points of diode-transistor circuits

The paper deals with the circuits containing diodes and bipolar transistors, having multiple DC solutions. A method for finding bounds on the location of all the solutions is developed. The method exploits some monotonic operators and generates two sequences bounding the solutions from below and above. The sequences are convergent and their limits are the bounds on all the DC solutions. The method has been implemented as a preliminary procedure of an earlier developed algorithm for finding all the solutions, based on the idea of successive contraction, division and elimination of some hyperrectangular regions, where the solutions are sought. Numerical examples show that the proposed approach is efficient and improves the analysis of transistor circuits having multiple DC solutions. It is illustrated via two numerical examples.
Linking Proteins to Signaling Pathways for Experiment Design and Evaluation

Biomedical experimental work often focuses on altering the functions of selected proteins. These changes can hit signaling pathways, and can therefore unexpectedly and non-specifically affect cellular processes. We propose PathwayLinker, an online tool that can provide a first estimate of the possible signaling effects of such changes, e.g., drug or microRNA treatments. PathwayLinker minimizes the users' efforts by integrating protein-protein interaction and signaling pathway data from several sources with statistical significance tests and clear visualization. We demonstrate through three case studies that the developed tool can point out unexpected signaling bias in normal laboratory experiments and identify likely novel signaling proteins among the interactors of known drug targets. In our first case study we show that knockdown of the Caenorhabditis elegans gene cdc-25.1 (meant to avoid progeny) may globally affect the signaling system and unexpectedly bias experiments. In the second case study we evaluate the loss-of-function phenotypes of a less known C. elegans gene to predict its function. In the third case study we analyze GJA1, an anti-cancer drug target protein in human, and predict for this protein novel signaling pathway memberships, which may be sources of side effects. Compared to similar services, a major advantage of PathwayLinker is that it drastically reduces the necessary amount of manual literature searches and can be used without a computational background. PathwayLinker is available at http://PathwayLinker.org. Detailed documentation and source code are available at the website.
CRITICAL THINKING , AND ENGINEERING CURRICULA

1 Brett Gunnink, University of Missouri-Columbia, Department of Civil and Environmental Engineering, E2509 EBE, Columbia, MO 65203, gunninkb@missouri.edu 2 Kristen L. Sanford Bernhardt, Lafayette College, Department of Civil and Environmental Engineering, 315 AHE, Easton, PA 18042, sanfordk@lafayette.edu Abstract Recently, writing-across-the-curriculum and critical thinking approaches have gained favor in higher education. However, while these movements have sprouted and grown roots in colleges of arts and science across the country, they have found the ground less fertile in colleges of engineering. These movements certainly influenced recent changes in the philosophy and approach to the accreditation of engineering programs, manifest in ABET Engineering Criteria 2000. Several ABET program outcomes are difficult to achieve using traditional approaches to engineering education. For example, it seems particularly difficult to show that graduate engineers will have the broad education necessary to understand the impact of civil engineering solutions in a global and societal context, and knowledge of contemporary issues as they relate to engineering problems. We believe that there is much to be gained from broader application of the philosophies and techniques of these movements to the education of engineers. This paper presents examples of assignments that promote these goals.