Miracle-dz/UniFAR-FTUs huggingface dataset:
Capping Protein Modulates the Dynamic Behavior of Actin Filaments in Response to Phosphatidic Acid in Arabidopsis[C][W]

Capping protein (CP) binds to the fast-growing ends of actin filaments and is negatively regulated by signaling lipids in vitro. This study demonstrates that the end-capping activity of CP is inhibited by membrane signaling phospholipids in living epidermal cells from Arabidopsis thaliana and that CP remodels the actin cytoskeleton in response to changes in these phospholipids. Remodeling of actin filament arrays in response to biotic and abiotic stimuli is thought to require precise control over the generation and availability of filament ends. Heterodimeric capping protein (CP) is an abundant filament capper, and its activity is inhibited by membrane signaling phospholipids in vitro. How exactly CP modulates the properties of filament ends in cells and whether its activity is coordinated by phospholipids in vivo is not well understood. By observing directly the dynamic behavior of individual filament ends in the cortical array of living Arabidopsis thaliana epidermal cells, we dissected the contribution of CP to actin organization and dynamics in response to the signaling phospholipid, phosphatidic acid (PA). Here, we examined three cp knockdown mutants and found that reduced CP levels resulted in more dynamic activity at filament ends, and this significantly enhanced filament-filament annealing and filament elongation from free ends. The cp mutants also exhibited more dense actin filament arrays. Treatment of wild-type cells with exogenous PA phenocopied the actin-based defects in cp mutants, with an increase in the density of filament arrays and enhanced annealing frequency. These cytoskeletal responses to exogenous PA were completely abrogated in cp mutants. Our data provide compelling genetic evidence that the end-capping activity of CP is inhibited by membrane signaling lipids in eukaryotic cells. Specifically, CP acts as a PA biosensor and key transducer of fluxes in membrane signaling phospholipids into changes in actin cytoskeleton dynamics.
Diversity in ranking using negative reinforcement

In this paper, we consider the problem of diversity in ranking of the nodes in a graph. The task is to pick the top-k nodes in the graph which are both 'central' and 'diverse'. Many graph-based models of NLP like text summarization, opinion summarization involve the concept of diversity in generating the summaries. We develop a novel method which works in an iterative fashion based on random walks to achieve diversity. Specifically, we use negative reinforcement as a main tool to introduce diversity in the Personalized PageRank framework. Experiments on two benchmark datasets show that our algorithm is competitive to the existing methods.
Transcutaneous antigen delivery system

Transcutaneous immunization refers to the topical application of antigens onto the epidermis. Transcutaneous immunization targeting the Langerhans cells of the skin has received much attention due to its safe, needle-free, and noninvasive antigen delivery. The skin has important immunological functions with unique roles for antigen-presenting cells such as epidermal Langerhans cells and dermal dendritic cells. In recent years, novel vaccine delivery strategies have continually been developed; however, transcutaneous immunization has not yet been fully exploited due to the penetration barrier represented by the stratum corneum, which inhibits the transport of antigens and adjuvants. Herein we review recent achievements in transcutaneous immunization, focusing on the various strategies for the enhancement of antigen delivery and vaccination efficacy. [BMB Reports 2013; 46(1): 17-24]
Galectin-1 in Early Acute Myocardial Infarction

Myocardial infarction (MI) is the most serious manifestation of coronary artery disease and the cause of significant mortality and morbidity worldwide. Galectin-1(GAL-1), a divalent 14.5-kDa protein, is present both inside and outside cells, and has both intracellular and extracellular functions. Hypoxia inducible factor-1 alpha (HIF-1α) is a transcription factor mediating early and late responses to myocardial ischemia. Identification of the pattern of expression of GAL-1 and HIF-1α in the heart during the first 24 hours following acute MI will help in understanding early molecular changes in this event and may provide methods to overcome serious complications. Mouse model of MI was used and heart samples were processed for immunohistochemical and immunofluorescent labeling and Enzyme linked immunosorbent assay to identify GAL-1 and HIF 1α levels in the heart during the first 24 hours following MI. There was significant increase in left ventricular GAL-1 at 20 (p = 0.001) and 30 minutes (p = 0.004) following MI. There was also a significant increase in plasma GAL-1 at 4 hours (p = 0.012) and 24 hours (p = 0.001) following MI. A significant increase in left ventricular HIF-1 α was seen at 20 minutes (p = 0.047) following MI. In conclusion, we show for the first time that GAL-1 level in the left ventricle is increased in early ischemic period. We also report for the first time that HIF-1 α is significantly increased at 20 minutes following MI. In addition we report for the first time that mouse plasma GAL-1 level is significantly raised as early as 4 hours following MI.
Approximate Homomorphisms : A framework for non-exact minimization in Markov Decision Processes

To operate effectively in complex environments learning agents require the ability to selectively ignore irrelevant details and form useful abstractions. In earlier work we explored in detail what constitutes a useful abstraction in a stochastic sequential decision problem modeled as a Markov Decision Process (MDP). We based our approach on the notion of an MDP homomorphism. In this article we look at relaxing the strict conditions imposed earlier and introduce approximate homomorphisms that allow us to construct useful abstract models even when the homomorphism conditions are not met exactly. We also present a result on bounding the loss resulting from this approximation.
Raising the reading skills of secondary-age students with severe and persistent reading difficulties: evaluation of the efficacy and implementation of a phonics-based intervention programme

Abstract The importance of reading skills to academic achievement, job acquisition and future success is well documented. Most of the research on reading interventions focuses on children in primary schools but many children start secondary school with very poor reading skills and schools require evidence-based interventions to support these children. The aims of this study were (i) to explore the efficacy of a phonics-based reading intervention programme, Toe By Toe, among secondary age students with severe reading difficulties and (ii) to examine perceived barriers to implementing this intervention programme. Results showed the intervention brought about a statistically significant improvement in the students’ decoding and word reading skills. Interviews with staff and students indicated a wide range of positive responses to the intervention and some key barriers to implementation. This study adds to the evidence base for the use of reading interventions in secondary schools to support students with severe reading difficulties.
Practical object recognition in autonomous driving and beyond

This paper is meant as an overview of the recent object recognition work done on Stanford's autonomous vehicle and the primary challenges along this particular path. The eventual goal is to provide practical object recognition systems that will enable new robotic applications such as autonomous taxis that recognize hailing pedestrians, personal robots that can learn about specific objects in your home, and automated farming equipment that is trained on-site to recognize the plants and materials that it must interact with. Recent work has made some progress towards object recognition that could fulfill these goals, but advances in model-free segmentation and tracking algorithms are required for applicability beyond scenarios like driving in which model-free segmentation is often available. Additionally, online learning may be required to make use of the large amounts of labeled data made available by tracking-based semi-supervised learning.
Setting Aims for Precision Learning

Setting instructional goals is one of the most important activities of special educators. When goals are clearly known by students and teachers, corrective action can be initiated quickly to ensure continued progress when a student's performance deteriorates (Howell, Kaplan, & O'Connell, 1979). Setting ambitious goals has been shown to have a positive effect on how special education teachers teach, as well as how their students achieve (Fuchs, Fuchs, & Deno, 1985; Fuchs, Fuchs, & Harnlett, 1989).
Graphical passwords for older computer users

Traditional text password authentication is widely used to gain access to computing resources. Older users find the cognitive effort, and manual dexterity skills needed to use strong text passwords challenging. We designed and evaluated our Graphical Password system based on touchscreen selection of familiar facial images embedded randomly among unfamiliar, yet similar images. In our usability study, older users demonstrated a recall rate of 97%, password entropy superior to short PINs, and authentication time comparable to short text passwords.
Pixelated image abstraction

We present an automatic method that can be used to abstract high resolution images into very low resolution outputs with reduced color palettes in the style of pixel art. Our method simultaneously solves for a mapping of features and a reduced palette needed to construct the output image. The results are an approximation to the results generated by pixel artists. We compare our method against the results of a naive process common to image manipulation programs, as well as the hand-crafted work of pixel artists. Through a formal user study and interviews with expert pixel artists we show that our results offer an improvement over the naive methods.
Perspectives on gene therapy in myotonic dystrophy type 1

Myotonic dystrophy type 1 (DM1) is an autosomal dominant neuromuscular disorder caused by a CTG expansion mutation located in the 3′ untranslated region of the DMPK (DM1 protein kinase) gene. According to current evidence, mutant DMPK mRNAs containing the trinucleotide expansion are retained in the nucleus, entrapping Muscleblind (MBNL1) protein and several transcription factors in ribonuclear foci and stabilizing CUG binding protein, Elav‐like family member 1 (CELF1), which ultimately causes aberrant pre‐mRNA splicing and gene expression of particular genes and associated pathogenesis in patients with DM1. At present, treatment for DM1 is limited to symptomatic intervention, and there is no therapeutic approach to prevent or reverse disease progression. This Mini‐Review is focused on the experimental advances obtained in cell‐based and animal models toward the development of therapeutic treatments against DM1, providing a discussion of their potential application in clinical trials. Because the central core of DM1 pathogenesis is gain‐of‐function of mutant RNA, most studies target the mutant RNA by use of antisense oligonucleotides or small chemical compounds to eliminate or ameliorate its toxic effects. However, alternative strategies focused on reversing DM1 features without targeting of mutant DMPK RNA have recently emerged. © 2010 Wiley‐Liss, Inc.
Defense Mechanisms against Machine Learning Modeling Attacks on Strong Physical Unclonable Functions for IOT Authentication: A Review

Security component in IoT system are very crucial because the devices within the IoT system are exposed to numerous malicious attacks. Typical security components in IoT system performs authentication, authorization, message and content integrity check. Regarding authentication, it is normally performed using classical authentication scheme using crypto module. However, the utilization of the crypto module in IoT authentication is not feasible because of the distributed nature of the IoT system which complicates the message cipher and decipher process. Thus, the Physical Unclonable Function (PUF) is suggested to replace crypto module for IoT authentication because it only utilizes responses from set of challenges instead of cryptographic keys to authenticate devices. PUF can generate large number of challenge-response pairs (CRPs) which is good for authentication because the unpredictability is high. However, with the emergence of machine learning modeling, the CRPs now can be predicted through machine learning algorithms. Various defense mechanisms were proposed to counter machine learning modeling attacks (ML-MA). Although they were experimentally proven to be able to increase resiliency against ML-MA, they caused the generated responses to be instable and incurred high area overhead. Thus, there is a need to design the best defense mechanism which is not only resistant to ML-MA but also produces reliable responses and reduces area overhead. This paper presents an analysis on defense mechanisms against ML-MA on strong PUFs for IoT authentication.
Coexistence of ZigBee wireless sensor networks and Bluetooth inside a vehicle

The increasing number of sensors deployed in cars calls for new design approaches in intra-vehicle electronic control unit (ECU) networks. ZigBee wireless sensor networks are seen as a good candidate technology, since they can provide high-performance short-range communication with low power consumption, as well as mesh networking capabilities. One of the main questions to answer before adoption of wireless technologies in a car is whether the performance delivered by these technologies is high enough. Firstly, a better understanding of the propagation characteristics in these environments is necessary. In addition, ZigBee sensor networks may experience interference from Bluetooth devices inside the car, commonly used for voice or data transfer applications, which may have a negative impact on the system performance. In this work, we study the performance of ZigBee sensor networks for intra-vehicle communications, in the presence of Bluetooth interference. In the first part, channel models are proposed for intra-vehicle communications in industrial, scientific and medical (ISM) 2.4-GHz band, obtained from channel measurements. In the second part, the system performance is evaluated in terms of packet error rate (PER) and average/peak packet latency, using measurements and calibrated simulations. The impact of topological and transmission parameters is studied, such as the sensor node locations or selected output power. Finally, recommendations for high performance operation are provided.
Modelling an agent-based integrated health and social care information system for older people

This paper argues the potential of agent technology to support delivery of integrated information systems for the health and social care sector. In doing so, it points out the similarities and the mutual characteristics (such as distribution of expertise) of integrated health and social care information systems and agent technology. On the other hand, it identifies the need to develop a mature and complete agent-oriented software engineering methodology tailored to the analysis and design of agent-based medical systems. The electronic Single Assessment Process (eSAP), an electronic system to deliver the integrated assessment of health and social care needs of older people, is modelled using the Tropos methodology.
RETracer: Triaging Crashes by Reverse Execution from Partial Memory Dumps

Many software providers operate crash reporting services to automatically collect crashes from millions of customers and file bug reports. Precisely triaging crashes is necessary and important for software providers because the millions of crashes that may be reported every day are critical in identifying high impact bugs. However, the triaging accuracy of existing systems is limited, as they rely only on the syntactic information of the stack trace at the moment of a crash without analyzing program semantics.In this paper, we present RETracer, the first system to triage software crashes based on program semantics reconstructed from memory dumps. RETracer was designed to meet the requirements of large-scale crash reporting services. RETracer performs binary-level backward taint analysis without a recorded execution trace to understand how functions on the stack contribute to the crash. The main challenge is that the machine state at an earlier time cannot be recovered completely from a memory dump, since most instructions are information destroying.We have implemented RETracer for x86 and x86-64 native code, and compared it with the existing crash triaging tool used by Microsoft. We found that RETracer eliminates two thirds of triage errors based on a manual analysis of 140 bugs fixed in Microsoft Windows and Office. RETracer has been deployed as the main crash triaging system on Microsoft’s crash reporting service.
Personalized e-commerce recommendations

Recommendation systems are special personalization tools that help users to find interesting information and services in complex online shops. Even though today's e-commerce environments have drastically evolved and now incorporate techniques from other domains and application areas such as Web mining, semantics, artificial intelligence, user modeling and profiling, etc. setting up a successful recommendation system is not a trivial or straightforward task. This paper argues that by monitoring, analyzing and understanding the behavior of customers, their demographics, opinions, preferences and history, as well as taking into consideration the specific e-shop ontology and by applying Web mining techniques, the effectiveness of produced recommendations can be significantly improved. In this way, the e-shop may upgrade users' interaction, increase its usability, convert users to buyers, retain current customers and establish long-term and loyal one-to-one relationships
Patterns of migrating soaring migrants indicate attraction to marine wind farms

Monitoring of bird migration at marine wind farms has a short history, and unsurprisingly most studies have focused on the potential for collisions. Risk for population impacts may exist to soaring migrants such as raptors with K-strategic life-history characteristics. Soaring migrants display strong dependence on thermals and updrafts and an affinity to land areas and islands during their migration, a behaviour that creates corridors where raptors move across narrow straits and sounds and are attracted to islands. Several migration corridors for soaring birds overlap with the development regions for marine wind farms in NW Europe. However, no empirical data have yet been available on avoidance or attraction rates and behavioural reactions of soaring migrants to marine wind farms. Based on a post-construction monitoring study, we show that all raptor species displayed a significant attraction behaviour towards a wind farm. The modified migratory behaviour was also significantly different from the behaviour at nearby reference sites. The attraction was inversely related to distance to the wind farm and was primarily recorded during periods of adverse wind conditions. The attraction behaviour suggests that migrating raptor species are far more at risk of colliding with wind turbines at sea than hitherto assessed.
A Model Checking based Test Case Generation Framework forWeb Services

BPEL (Business Process Execution Language) is an emerging standard language to describe Web service composition behaviour. The advanced features of BPEL such as concurrency and hierarchy make it challenging to verify BPEL models. Previously, we proposed WSA (Web service automata) to be the formal models for BPEL. Based on WSA, this paper presents a model checking based test case generation framework for BPEL. We apply SPIN and NuSMV model checkers as the test generation engine, and we encode the conventional structural test coverage criteria into LTL and CTL temporal logic. State coverage and transition coverage are used for BPEL control flow testing, and all-du-path coverage is used for BPEL dataflow testing. Two levels of test cases can be generated to test whether the implementation of Web services conforms to the BPEL behaviour and WSDL interface models. The generated test cases are executed on the JUnit test execution engine
Online (and Offline) Robust PCA: Novel Algorithms and Performance Guarantees

In this work, we study the online robust principal components' analysis (RPCA) problem. In recent work, RPCA has been defined as a problem of separating a low-rank matrix (true data), $L$, and a sparse matrix (outliers), $S$, from their sum, $M:=L + S$. A more general version of this problem is to recover $L$ and $S$ from $M:=L + S + W$ where $W$ is the matrix of unstructured small noise/corruptions. An important application where this problem occurs is in video analytics in trying to separate sparse foregrounds (e.g., moving objects) from slowly changing backgrounds. While there has been a large amount of recent work on solutions and guarantees for the batch RPCA problem, the online problem is largely open. "Online" RPCA is the problem of doing the above on-the-fly with the extra assumptions that the initial subspace is accurately known and that the subspace from which $l_t$ is generated changes slowly over time. We develop and study a novel "online" RPCA algorithm based on the recently introduced Recursive Projected Compressive Sensing (ReProCS) framework. Our algorithm improves upon the original ReProCS algorithm and it also returns even more accurate offline estimates. The key contribution of this work is a correctness result (complete performance guarantee) for this algorithm under reasonably mild assumptions. By using extra assumptions -- accurate initial subspace knowledge, slow subspace change, and clustered eigenvalues -- we are able to remove one important limitation of batch RPCA results and two key limitations of a recent result for ReProCS for online RPCA. To our knowledge, this work is among the first few correctness results for online RPCA. Most earlier results were only partial results, i.e., they required an assumption on intermediate algorithm estimates.