Miracle-dz/UniFAR-FTUs huggingface dataset:
Kinesin-related KIP3 of Saccharomyces cerevisiae Is Required for a Distinct Step in Nuclear Migration

Spindle orientation and nuclear migration are crucial events in cell growth and differentiation of many eukaryotes. Here we show that KIP3, the sixth and final kinesin-related gene in Saccharomyces cerevisiae, is required for migration of the nucleus to the bud site in preparation for mitosis. The position of the nucleus in the cell and the orientation of the mitotic spindle was examined by microscopy of fixed cells and by time-lapse microscopy of individual live cells. Mutations in KIP3 and in the dynein heavy chain gene defined two distinct phases of nuclear migration: a KIP3-dependent movement of the nucleus toward the incipient bud site and a dynein-dependent translocation of the nucleus through the bud neck during anaphase. Loss of KIP3 function disrupts the unidirectional movement of the nucleus toward the bud and mitotic spindle orientation, causing large oscillations in nuclear position. The oscillatory motions sometimes brought the nucleus in close proximity to the bud neck, possibly accounting for the viability of a kip3 null mutant. The kip3 null mutant exhibits normal translocation of the nucleus through the neck and normal spindle pole separation kinetics during anaphase. Simultaneous loss of KIP3 and kinesin-related KAR3 function, or of KIP3 and dynein function, is lethal but does not block any additional detectable movement. This suggests that the lethality is due to the combination of sequential and possibly overlapping defects. Epitope-tagged Kip3p localizes to astral and central spindle microtubules and is also present throughout the cytoplasm and nucleus.
Predictive value of platelet to lymphocyte ratio and mean platelet volume in atrial fibrillation after isolated coronary artery bypass graft operation

Aim: The relationship between inflammation and the development of atrial fibrillation and the relationship between platelet to lymphocyte ratio and mean platelet volume in relation to inflammation were investigated in patients with atrial fibrillation after isolated coronary artery bypass graft operation. Material And Methods: Clinical and laboratory data of 132 patients, who did not have preoperative atrial fibrillation and underwent isolated coronary artery bypass graft operation using cardiopulmonary pump, were analyzed retrospectively. Patients were divided into two groups as patients with developed atrial fibrillation and those without atrial fibrillation. Concomitant diseases, preoperative drug use, echocardiographic and angiographic findings, preoperative, intraoperative and postoperative laboratory parameters were obtained from medical records and compared statistically. Results: Of the 132 patients, 68 with atrial fibrillation (mean age: 68.3+/-10.7 years, 50 males and 18 females) and 64 patients without atrial fibrillation (mean age: 64.5+/-9.8 years, 55 males and 9 females) were identified, and difference in the mean age was statistically significant (p=0.016). Postoperative platelet to lymphocyte ratio and mean platelet volume values (280.3+/-112.3; 9.9+/-1.7) were also significantly higher in those with atrial fibrillation group than preoperative values (129.2+/-49.3; 9.6+/-1.6, p<0.0001, p<0.0009). Conclusıon: According to our study higher values of postoperative platelet to lymphocyte ratio and mean platelet volume are correlated with development of atrial fibrillation after coronary artery bypass graft operation.
A multi-agent system for distributed cluster analysis

One of the approaches used to improve the accuracy and relevancy in information retrieval is cluster analysis. Clustering methods determine relationships among text documents, and allow the determination of similar groups or clusters of documents. These methods are computationally expensive, thereby limiting their use to a relatively small set of documents. This paper describes a multi-agent system to cluster large data sets. This technique is then compared to hierarchical agglomerative clustering using a small set of text data. Results show that the agent-based approach can significantly reduce the time required to cluster large data sets.
The Critical Role of Metabolic Pathways in Aging

Aging is characterized by a deterioration in the maintenance of homeostatic processes over time, leading to functional decline and increased risk for disease and death. The aging process is characterized metabolically by insulin resistance, changes in body composition, and physiological declines in growth hormone (GH), insulin-like growth factor-1 (IGF-1), and sex steroids. Some interventions designed to address features of aging, such as caloric restriction or visceral fat depletion, have succeeded in improving insulin action and life span in rodents. Meanwhile, pharmacologic interventions and hormonal perturbations have increased the life span of several mammalian species without necessarily addressing features of age-related metabolic decline. These interventions include inhibition of the mammalian target of rapamycin and lifetime deficiency in GH/IGF-1 signaling. However, strategies to treat aging in humans, such as hormone replacement, have mostly failed to achieve their desired response. We will briefly discuss recent advances in our understanding of the complex role of metabolic pathways in the aging process and highlight important paradoxes that have emerged from these discoveries. Although life span has been the major outcome of interest in the laboratory, a special focus is made in this study on healthspan, as improved quality of life is the goal when translated to humans.
The role of attachment style and sexual self-schemas in romantic and sexual relationships: a study of dating couples

In this study, we examined the role o f attachment style and sexual self-schema in the relational and sexual functioning of dating couples. Participants were 210 undergraduate, heterosexual couples from Introductory Psychology courses. Consistent with previous research, study results suggest that attachment style is associated with greater relationship quality and that sexual self-schema is associated with better sexual functioning. More importantly, the study extends previous research by suggesting that sexual self-schema may affect relationship quali^ and attachment style may affect sexual functioning. First, men with schematic sexual self-concepts generally reported better relationship quali^; they described their relationships as more passionate, intimate, and committed. Second, insecure (i.e., anxious or avoidant) attachment was associated with poorer sexual functioning in both genders. Among the women, avoidant attachment had particularly detrimental consequences for sexual response and sexual satisfaction. Among the men, avoidance was associated with mcreased sexual intercourse fiequency. Both avoidant and anxious attachment were related to disruptions in men’s sexual response. Finally, anxious attachment was associated with decreased sexual satisfaction in men. Explanations for the above findings are considered, with an emphasis on the importance of gender-role dif&raices.
Intergenerational Value Transmission Based on Culture

This study explored the way in which culture impacts value transmission between parents and children. Specifically, I used previous literature to inform the defining of individualism and collectivism and how value transmission allows for these cultures to be preserved through the generations. Transmission of values was analyzed by conducting six semi-structured, in-depth interviews which resulted in findings that built on previous literature. I found that the values that the participants held came from cultural, parental and religious influences. Collectivist participants more consistently identified with maintaining similar values as their parents into adulthood while individualist participants reported more autonomous tendencies in value selection. Additionally, religion emerged as a common theme, and I found that individualist participants exhibited some collectivist value tendencies due to religion. These findings allow us to determine whether our values originate from culture, religion or other influences. More importantly, the findings serve as a guide for parents across all cultures who intentionally seek to instill good values in their children.
Towards an online energy allocation policy for distributed estimation with sensor collaboration using energy harvesting sensors

We study an energy allocation problem for distributed estimation with sensor collaboration, where collaboration refers to the act of sharing measurements with neighboring sensors prior to transmission to the fusion center, and the sensors are equipped with energy harvesters to replenish their power from the environment. Based on the statistics of the harvested energy and dynamics of energy flow at each sensor, we propose a provably efficient online energy allocation policy for distributed estimation with sensor collaboration. The proposed online policy relies on solving an offline non-convex optimization problem, in which the estimation distortion is minimized subject to energy and network topology constraints. We employ semidefinite programming to find the globally-optimal solution of the non-convex problem. We show that the proposed online policy is asymptotically consistent and provide mean square error of the optimal offline solution over an infinite time horizon.
Hyperdiploidy with 58-66 chromosomes in childhood B-acute lymphoblastic leukemia is highly curable: 58951 CLG-EORTC results.

The aim of our study was to analyze the factors contributing to heterogeneity of prognosis in patients with hyperdiploidy>50 chromosomes (HD>50), a group of B-cell precursor acute lymphoblastic leukemia with favorable outcome. The 541 HD>50 patients registered prospectively in the 58951 European Organisation for Research and Treatment of Cancer (EORTC) Children's Leukemia Group (CLG) trial, identified by karyotype (446 patients) and by DNA index (DI) (490 patients), had a 6-year event-free survival (EFS) of 89.0% (standard error [SE] = 1.5%) and a 6-year overall survival (OS) of 95.9% (SE = 0.9%). The strongest prognostic factor was the modal number of chromosomes (MNC): the 6-year EFS of 51-53, 54-57, and 58-66 MNC groups were 80%, 89%, and 99%, respectively (P < .0001). Ploidy assessed by DI was also a favorable factor: the higher the DI, the better the outcome. The 6-year EFS of the 3 subgroups of DI < 1.16/≥1.16-<1.24/≥1.24 were 83%, 90%, and 95%, respectively (P = .009). All usual combinations of trisomies (chromosomes 4, 10, 17, 18) were significant favorable factors but had lower EFS when MNC was lower than 58. In multivariate analysis, MNC remained the strongest factor. Consequently, the best indicator for excellent outcome was ploidy assessed by karyotype because patients with 58-66 chromosomes stood every chance of being cured (OS of 100% at 6-year follow-up) with less-intensive therapy. This trial was registered at www.clinicaltrials.gov as #NCT00003728. Registered: http://www.eortc. org/, http://clinicaltrials.gov/show/NCT00003728.
Belief propagation for graph partitioning

We study the belief-propagation algorithm for the graph bi-partitioning problem, i.e. the ground state of the ferromagnetic Ising model at a fixed magnetization. Application of a message passing scheme to a model with a fixed global parameter is not banal and we show that the magnetization can in fact be fixed in a local way within the belief-propagation equations. Our method provides the full phase diagram of the bi-partitioning problem on random graphs, as well as an efficient heuristic solver that we anticipate to be useful in a wide range of application of the partitioning problem.
Multi-resolution resource behavior queries using wavelets

Different adaptive applications are interested in the dynamic behavior of a resource over different fine- to coarse-grain time-scales. The resource's sensor runs at some fine-grain resource-appropriate sampling rate, producing a discrete-time resource signal. It can be very inefficient to to answer a coarse-grain application query by directly using the fine-grain resource signal. We address this gap between the sensor and its different client applications with a novel query model that explicitly incorporates time-scale as a parameter. The query model is implemented on top of an inherently multi-scale wavelet-based representation of the signal (which could be communicated over a set of multicast channels). A query uses only the wavelet coefficients necessary for its time-scale (and thus could listen to a subset of the channels), greatly reducing the data that need to be communicated. We present very promising initial results on host load signals, showing the tradeoff between compactness and query error. Finally, we describe some of the other operations that the wavelet representation enables.
Visual spam campaigns analysis using abstract graphs representation

In this work we present a visual analytics tool introducing a new kind of graph visualization that exploits the nodes' degree to provide a simplified and more abstract, yet accurate, representation of the most important elements of a security data set and their inter-relationships. Our visualization technique is designed to address two shortcomings of existing graph visualization techniques: scalability of visualization and comprehensibility of results. The main goal of our visual analytics tool is to provide security analysts with an effective way to reason interactively about various attack phenomena orchestrated by cyber criminals. We demonstrate the use of our tool on a large corpus of spam emails by visualizing spam campaigns performed by spam botnets. In particular, we focus on the analysis of spam sent in March 2011 to understand the impact of the Rustock takedown on the botnet ecosystem. As spam botnets continue to play a significant role in the worldwide spam problem, we show with this application how security visualization based on abstract graphs can help us gain insights into the strategic behavior of spam botnets, and a better understanding of large-scale spammers operations.
Diagnostic fault simulation for synchronous sequential circuits

In this paper, a time and memory-efficient diagnostic fault simulator for sequential circuits is first presented. A distributed diagnostic fault simulator is then presented based on the sequential algorithm to improve the speed of the diagnostic process. In the sequential diagnostic fault simulator, the number of fault-pair output response comparisons has been minimized by using an indistinguishability fault list that stores the faults that are indistinguishable from each fault. Due to the symmetrical relationship of the fault-pair distinguishability, fault list sizes are reduced. Therefore, the different diagnostic measures of a given test set can be generated very quickly using a small amount of memory. To further speed up the process of finding the indistinguishable fault list for each fault, a distributed approach is proposed and developed. The major idea for this approach is that each processor constructs the indistinguishable fault lists for a certain percentage of faults only. Experimental results show that the sequential diagnostic fault simulator runs faster and uses less memory than a previously developed one and that the distributed algorithm even achieves superlinear speedup for a very large sequential benchmark circuit, s35932. To the authors' knowledge, no distributed diagnostic fault simulation system for sequential circuits has been proposed before.
Blame the data or blame the theory? : On(in-)comparability in international survey research

This dissertation project contains three articles dealing with particular examples of cross-national (in)comparability in international survey research, addressing different elements of the Total Survey Error framework. The central focus of the articles is on measurement error and construct validity. By examining the relationship between socioeconomic status and attitudes toward progressive taxation, the first article reveals theory generalization limitations across countries with different welfare state designs. Dealing with the latent construct of religiosity, the second article discusses the cross-national comparability of religious profiles (typologies) and the role denominations play for the profile formation process. The third article evaluates an indicator of religious self-assessment by comparing its predictive potential with two multi-variate survey instruments for religiosity across countries. All articles are based on data taken from the International Social Survey Programme (ISSP).
Efficient top-k querying over social-tagging networks

Online communities have become popular for publishing and searching content, as well as for finding and connecting to other users. User-generated content includes, for example, personal blogs, bookmarks, and digital photos. These items can be annotated and rated by different users, and these social tags and derived user-specific scores can be leveraged for searching relevant content and discovering subjectively interesting items. Moreover, the relationships among users can also be taken into consideration for ranking search results, the intuition being that you trust the recommendations of your close friends more than those of your casual acquaintances. Queries for tag or keyword combinations that compute and rank the top-k results thus face a large variety of options that complicate the query processing and pose efficiency challenges. This paper addresses these issues by developing an incremental top-k algorithm with two-dimensional expansions: social expansion considers the strength of relations among users, and semantic expansion considers the relatedness of different tags. It presents a new algorithm, based on principles of threshold algorithms, by folding friends and related tags into the search space in an incremental on-demand manner. The excellent performance of the method is demonstrated by an experimental evaluation on three real-world datasets, crawled from deli.cio. us, Flickr, and LibraryThing.
Bird classification algorithms: theory and experimental results

To minimize the number of birdstrikes, a common method is to use microphone arrays to monitor and identify dangerous birds near the airport or some critical locations in the airspace. However, it was recognized that the range of existing ground-based acoustic monitoring devices is only limited to a few hundred meters. Moreover, the bird classification performance in low signal-to-noise environments such as airports is not very satisfactory. This paper summarizes the development of a high performance bird classification system using a hidden Markov model (HMM) and Gaussian mixture model (GMM). Experimental results verified the classification performance.
Detour Routing Protocol for Geographic Sensor Networks

Routing in sensor networks is a challenging research topic due to the barriers (holes) on the natural topography often causing routing algorithms to fail. Most geographic routing algorithms in sensor networks apply the greedy forwarding method to discovery a path to the destination without the global states. However, greedy forwarding could fail because the local minima problem. In this paper, we propose a novel algorithm called Detour Routing based on Quadrant Classification (DRQC), to reduce the occurrence of the local minima problem by avoiding sending packages to the nodes which could have the local minima problem. The basic idea of DRQC is based on that each node knows the geographic information of its 2-hop neighbors, and then each node decides its state: red or white. A red node has no local minima problem, but a white node could have local minima problem. DRQC requires that each node chooses one of its 2-hop neighbors, which is red and whose distance to the destination is the shortest one, as the next hop for forwarding a packet. If no such a neighbor exists, then traditional 2-hop Greedy scheme is applied to select the next forwarding node. We implement the DRQC algorithm and compare it with two algorithms: 1-hop greedy and 2-hop greedy. Simulation results show that our algorithm (DRQC) has higher packet delivery rate (96%) than the two algorithms: 1-hop Greedy (79%) and 2-hop Greedy (87%).
Evolutionary Design via Indirect Encoding of Non-Uniform Rational Basis Splines

A novel approach to produce 2D designs by adapting the HyperNEAT algorithm to evolve non-uniform rational basis splines (NURBS) is presented. This representation is proposed as an alternative to previous pixel-based approaches primarily motivated by aesthetic interests, and not designed for optimization tasks. This spline representation outperforms previous pixel-based approaches on target matching tasks, performing well even in matching irregular target shapes. In addition to improved evolvability in the face of a well defined fitness metric, a NURBS representation has the added virtues of being continuous rather than discrete, as well as being intuitive and easily modified by graphic and industrial designers.
A novel binarization approach for text in images

Accurate recognition of scene text and overlaid text is still a challenging issue due to degradation and complex background, and text binarization is crucial for recognition accuracy. This paper presents an effective method to extract characters in images and video frames. Our method assumes that background pixels possess good spatial connectivity and high appearance similarity to boundary pixels in a cropped text string image. It first computes the confidence of pixels as text. Then the confidence map is exploited to partition text regions into characters. Further, each character region is clustered into different layers and background components are removed to generate candidate binarization results. The final result is obtained based on the scores of each layer. Our method is validated by better recognition rates and segmentation accuracy on the ICADR03 dataset and a big dataset of overlaid text.
Targeted Attacks on Teleoperated Surgical Robots: Dynamic Model-Based Detection and Mitigation

This paper demonstrates targeted cyber-physical attacks on teleoperated surgical robots. These attacks exploit vulnerabilities in the robot's control system to infer a critical time during surgery to drive injection of malicious control commands to the robot. We show that these attacks can evade the safety checks of the robot, lead to catastrophic consequences in the physical system (e.g., sudden jumps of robotic arms or system's transition to an unwanted halt state), and cause patient injury, robot damage, or system unavailability in the middle of a surgery. We present a model-based analysis framework that can estimate the consequences of control commands through real-time computation of robot's dynamics. Our experiments on the RAVEN II robot demonstrate that this framework can detect and mitigate the malicious commands before they manifest in the physical system with an average accuracy of 90%.