Miracle-dz/UniFAR-FTUs huggingface dataset:
CD317/tetherin is an organiser of membrane microdomains

Summary The integral membrane protein tetherin has been associated with an eclectic mix of cellular processes, including restricting the release of a range of enveloped viruses from infected cells. The unusual topology of tetherin (it possesses both a conventional transmembrane domain and a glycosylphosphatidylinositol anchor), its localisation to membrane microdomains (lipid rafts) and the fact that its cytosolic domain can be linked (indirectly) to the actin cytoskeleton, led us to speculate that tetherin might form a ‘tethered picket fence’ and thereby play a role in the organisation of lipid rafts. We now show that knocking down expression of tetherin leads to changes in the distribution of lipid raft-localised proteins and changes in the organisation of lipids in the plasma membrane. These changes can be reversed by re-expression of wild-type tetherin, but not by any of a range of tetherin-based constructs, indicating that no individual feature of the tetherin sequence is dispensable in the context of its lipid raft organising function.
Software architectures and Open Source Software: Where can research leverage the most?

Software architectures have been playing a central role in software engineering research for some years now. They are considered of pivotal importance in the success of complex software systems development. However, with the emergence of Open Source Software (OSS) development, a new opportunity for studying architectural issues arises. In this paper, we introduce accepted notions of software architectures (Section 2), discuss some of the known issues in OSS (Section 3), resulting in a set of aspects we consider to be relevant for future research (Section 4).
Of Mechanism Design and Multiagent Planning (extended abstract)

Multiagent planning methods are concerned with planning by and for a group of agents. If the agents are self-interested, they may be tempted to lie in order to obtain an outcome that is more rewarding for them. We therefore study the multiagent planning problem from a mechanism design perspective, showing how to incentivise agents to be truthful. We prove that the well-known truthful VCG mechanism is not always truthful in the context of optimal planning, and present a modiﬁcation to ﬁx this. Finally, we present some (domain-dependent) poly-time planning algorithms using this ﬁx that maintain truthfulness in spite of their non-optimality.
Electrical and mechanical properties of multi-walled carbon nanotubes reinforced PMMA and PS composites

The use of multi-walled carbon nanotubes (MWCNT) as reinforcing material for thermoplastic polymer matrices, polymethyl methacrylate (PMMA), and polystyrene (PS) has been studied. MWCNT were synthesized by chemical vapor deposition (CVD) technique using ferrocene-toluene mixture. As-prepared nanotubes were ultrasonically dispersed in toluene and subsequently dispersed in PMMA and PS. Thin polymer composite films were fabricated by solvent casting. The effect of nanotube content on the electrical and mechanical properties of the nanocomposites was investigated. An improvement in electrical conductivity from insulating to conducting with increasing MWCNT content was observed. The carbon nanotube network showed a classical percolating network behavior with a low percolation threshold. Electromagnetic interference (EMI) shielding effectiveness value of about 18 dB was obtained in the frequency range 8.0–12 GHz (X-band), for a 10 vol% CNT loading. An improved composite fabrication process using casting followed by compression molding and use of functionalized MWCNT resulted in increased composites strength. POLYM. COMPOS. , 2008. © 2008 Society of Plastics Engineers
A Tree Transducer Model for Grammatical Error Correction

We present an approach to grammatical error correction for the CoNLL 2013 shared task based on a weighted tree-to-string transducer. Rules for the transducer are extracted from the NUCLE training data. An n-gram language model is used to rerank k-best sentence lists generated by the transducer. Our system obtains a precision, recall and F1 score of 0.27, 0.1333 and 0.1785, respectively, on the official test set. On the revised annotations, the F1 score increases to 0.2505. Our system ranked 6th out of the participating teams on both the original and revised test set annotations.
Patients with Pain Need Less Stigma, Not More.

“Protection from and relief of pain and suffering are a fundamental feature of the human contract we make as parents, partners, children, family, friends, and community members, as well as a cardinal underpinning of the art and science of healing. ” With these words a blue-ribbon panel of clinicians, researchers and policymakers convened by the prestigious Institute of Medicine opened its landmark 2011 report, Relieving Pain in America . To prepare this report, the panel distilled a vast literature and developed new, detailed estimates of the economic cost of pain in the United States. The panel also reached out to the public, soliciting written or oral testimonies from more than 2,000 persons with pain, their family members and care providers. These testimonies eloquently convey the stigma experienced by nearly every patient with chronic pain: Stigma—shaming and shunning—continues to befall patients with chronic pain, as do inequities in access to care. Tragically, people with the fewest resources to resist pain’s debilitating effects—minorities, the very young or very old, those with HIV, cancer, or substance abuse, the poor or homeless—are also marginalized by society. These “outsiders” have the least access to appropriate pain assessment and treatment. Even prosperous individuals often find their need for pain prevention or control is not addressed. Why is appropriate pain treatment so hard to deliver? The reasons are complex but must be addressed now. First, humans are social animals who look to their peers to …
Permanently proliferating rat vascular smooth muscle cell with maintained expression of smooth muscle characteristics, including actin of the vascular smooth muscle type

Cells of an established clonal line (RVF-SMC) derived from rat vena cava are described by light and electron microscope methods and biochemical analysis of the major proteins. The cells are flat, and they moderately elongate and form monolayers. They are characterized by prominent cables of microfilaments bundles decoratable with antibodies to actin and alpha-actinin. These bundles contain numerous densely stained bodies and are often flanked by typical rows of surface caveolae and vesicles. The cells are rich in intermediate-sized filaments of the vimentin type but do not show detectable amounts of desmin and cytokeratin filaments. Isoelectric focusing and protein chemical studies have revealed actin heterogeneity. In addition to the two cytoplasmic actins, beta and gamma, common to proliferating cells, two smooth muscle-type actins (an acidic alpha-like and a gamma-like) are found. The major (alpha-type) vascular smooth muscle actin accounts for 28% of the total cellular actin. No skeletal muscle or cardiac muscle actin has been detected. The synthesis of large amounts of actin and vimentin and the presence of at least three actins, including alpha- like actin, have also been demonstrated by in vitro translation of isolated poly(A)+ mRNAs. This is, to our knowledge, the first case of expression of smooth muscle-type actin in a permanently growing cell. We conclude that permanent cell growth and proliferation is compatible with the maintained expression of several characteristic cell features of the differentiated vascular smooth muscle cell including the formation of smooth muscle-type actin.
A New Approach For Multi-Document Summarization

----------------------------------------------------------------------ABSTRACT------------------------------------------------------------This paper presents a new multi-document summarization system using manifold ranking and mutual reinforcement approach. Manifold-ranking has been recently subjugated for query-based summarization. It propagates the relevance from query to the document sentences by making use of both the relationships among the sentences and the relationships between the given query and the sentences. Set of document covers a number of topic themes. In this a model is proposed to enhance manifold-ranking based relevance propagation via mutual reinforcement between sentences and clusters. The proposed model uses two new sentence ranking algorithms, namely the reinforcement after relevance propagation algorithm and the reinforcement during relevance propagation algorithm.
Machine Vision and Applications Evaluation of Statistical and Multiple-hypothesis Tracking for Video Traffic Surveillance

Conventional tracking methods encounter difficulties as the number of objects, clutter, and sensors increase, because of the requirement for data association. Statistical tracking, based on the concept of network tomography, is an alternative that avoids data association. It estimates the number of trips made from one region to another in a scene based on interregion boundary traffic counts accumulated over time. It is not necessary to track an object through a scene to determine when an object crosses a boundary. This paper describes statistical tracing and presents an evaluation based on the estimation of pedestrian and vehicular traffic intensities at an intersection over a period of 1 month. We compare the results with those from a multiple-hypothesis tracker and manually counted ground-truth estimates.
[Psi 13,14] bombesin analogues inhibit growth of small cell lung cancerin vitro and in vivo.

Bombesin/gastrin releasing peptide (BN/GRP) functions as an autocrine growth factor in small cell lung cancer (SCLC). Previously, this autocrine growth cycle was disrupted by a monoclonal antibody which binds to the carboxyl terminal of BN and neutralizes the peptide so that it is unable to interact with the BN/GRP receptor. Here a series of BN analogues were synthesized which have a reduced peptide bond near the carboxyl terminal. The analogues inhibited specific binding of 125I-GRP to SCLC cell line NCI-H345 in a dose-dependent manner and the analogue [D-Nal6, Psi13,14, Phe14] BN6-14 was approximately 6-fold more potent than was (Psi13,14, Leu14)BN with a 50% inhibition concentration value of 5 nM. [DNal6, Psi13,14, Phe14]BN6-14 and [Psi13,14, Leu14]BN had no effect on the cytosolic Ca2+ levels but antagonized the increase in cytosolic Ca2+ caused by 10 nM BN. [Psi13,14, Leu14]BN (1 microM) inhibited the growth of SCLC in vitro using a clonogenic assay by approximately 70% Also, injection of [Psi13,14, Leu14]BN (10 micrograms, s.c.) inhibited the growth of SCLC xenografts in nude mice in vivo by approximately 50%. These data suggest that the autocrine growth cycle of BN/GRP in SCLC may also be disrupted by peptide antagonists which bind to the BN receptor.
The Change of Expression Configuration Affects Identity-Dependent Expression Aftereffect but Not Identity-Independent Expression Aftereffect

The present study examined the influence of expression configuration on cross-identity expression aftereffect. The expression configuration refers to the spatial arrangement of facial features in a face for conveying an emotion, e.g., an open-mouth smile vs. a closed-mouth smile. In the first of two experiments, the expression aftereffect is measured using a cross-identity/cross-expression configuration factorial design. The facial identities of test faces were the same or different from the adaptor, while orthogonally, the expression configurations of those facial identities were also the same or different. The results show that the change of expression configuration impaired the expression aftereffect when the facial identities of adaptor and tests were the same; however, the impairment effect disappears when facial identities were different, indicating the identity-independent expression representation is more robust to the change of the expression configuration in comparison with the identity-dependent expression representation. In the second experiment, we used schematic line faces as adaptors and real faces as tests to minimize the similarity between the adaptor and tests, which is expected to exclude the contribution from the identity-dependent expression representation to expression aftereffect. The second experiment yields a similar result as the identity-independent expression aftereffect observed in Experiment 1. The findings indicate the different neural sensitivities to expression configuration for identity-dependent and identity-independent expression systems.
The hidden layer design for staked denoising autoencoder

Deep learning can achieve the complex function approximation and the characteristics of the input data by studying a deep nonlinear network. At present, one of the most important problems in the study of deep learning is how to construct a reasonable structure. This paper studies the deep learning model of stacked denoising autoencoder (SDA) and the remaining task is to construct its reasonable model. We introduce three effective methods to construct the structure of the SDA. Numerical experiments imply that the structure obtained by the golden section principle performs the best.
GenSoFNN: a generic self-organizing fuzzy neural network

Existing neural fuzzy (neuro-fuzzy) networks proposed in the literature can be broadly classified into two groups. The first group is essentially fuzzy systems with self-tuning capabilities and requires an initial rule base to be specified prior to training. The second group of neural fuzzy networks, on the other hand, is able to automatically formulate the fuzzy rules from the numerical training data. No initial rule base needs to be specified prior to training. A cluster analysis is first performed on the training data and the fuzzy rules are subsequently derived through the proper connections of these computed clusters. However, most existing neural fuzzy systems (whether they belong to the first or second group) encountered one or more of the following major problems. They are (1) inconsistent rule-base; (2) heuristically defined node operations; (3) susceptibility to noisy training data and the stability-plasticity dilemma; and (4) needs for prior knowledge such as the number of clusters to be computed. Hence, a novel neural fuzzy system that is immune to the above-mentioned deficiencies is proposed in this paper. This new neural fuzzy system is named the generic self-organizing fuzzy neural network (GenSoFNN). The GenSoFNN network has strong noise tolerance capability by employing a new clustering technique known as discrete incremental clustering (DIC). The fuzzy rule base of the GenSoFNN network is consistent and compact as GenSoFNN has built-in mechanisms to identify and prune redundant and/or obsolete rules. Extensive simulations were conducted using the proposed GenSoFNN network and its performance is encouraging when benchmarked against other neural and neural fuzzy systems.
Achievable rates for transmission of discrete constellations over the Gaussian MAC channel

In this paper we examine the achievable rate region of the Gaussian Multiple Access Channel (MAC) when suboptimal transmission schemes are employed. Focusing on the two-user case and assuming uncoded Pulse Amplitude Modulation (PAM), we derive a rate region that is a pentagon, and propose a strategy with which it can be achieved. We also compare the region with outer bounds and with orthogonal transmission.
Efficient Boundary Estimation for Practical Deployment of Mobile Sensors in Hybrid Sensor Networks

We address the deployment issues in a hybrid sensor network consisting of both static and mobile sensor nodes. Existing deployment schemes often assume either known regular boundaries of the region, or that mobile sensors are able to detect the region boundary. This is overly idealistic especially for unknown, outdoor environments. In our proposed two-phase deployment scheme, following their initial random deployment, the static sensors estimate the boundary of the unknown region by using the right-hand rule. This phase results in the identification of static boundary nodes, B-nodes. The mobile sensors are assumed concentrated at one or more points within the target area. In phase II, mobile sensor nodes spread in the target area in a distributed manner using one of the proposed variations of the virtual force algorithm. Neighboring B-nodes form a Virtual Boundary and exerts repulsive forces on mobile nodes to keep them in the target area. Using simulations, we demonstrate the effectiveness of our proposed scheme in uniformly deploying mobile sensor nodes in a hybrid sensor network
2.2 Å resolution cryo-EM structure of β-galactosidase in complex with a cell-permeant inhibitor

Pushing the limits of electron microscopy Recent advances in cryo–electron microscopy (cryo-EM) allow structures of large macromolecules to be determined at near-atomic resolution. So far, though, resolutions approaching 2 Å, where features key to drug design are revealed, remain the province of x-ray crystallography. Bartesaghi et al. achieved a resolution of 2.2 Å for a 465-kD ligand-bound protein complex using cryo-EM. The density map is detailed enough to show close to 800 water molecules, magnesium and sodium ions, and precise side-chain conformations. These results bring routine use of cryo-EM in rational drug design a step closer. Science, this issue p. 1147 Advances in electron microscopy allow protein structure determination at resolutions useful in drug discovery. Cryo–electron microscopy (cryo-EM) is rapidly emerging as a powerful tool for protein structure determination at high resolution. Here we report the structure of a complex between Escherichia coli β-galactosidase and the cell-permeant inhibitor phenylethyl β-d-thiogalactopyranoside (PETG), determined by cryo-EM at an average resolution of ~2.2 angstroms (Å). Besides the PETG ligand, we identified densities in the map for ~800 water molecules and for magnesium and sodium ions. Although it is likely that continued advances in detector technology may further enhance resolution, our findings demonstrate that preparation of specimens of adequate quality and intrinsic protein flexibility, rather than imaging or image-processing technologies, now represent the major bottlenecks to routinely achieving resolutions close to 2 Å using single-particle cryo-EM.
Distance-based outlier detection

Detecting outliers in data is an important problem with interesting applications in a myriad of domains ranging from data cleaning to financial fraud detection and from network intrusion detection to clinical diagnosis of diseases. Over the last decade of research, distance-based outlier detection algorithms have emerged as a viable, scalable, parameter-free alternative to the more traditional statistical approaches. In this paper we assess several distance-based outlier detection approaches and evaluate them. We begin by surveying and examining the design landscape of extant approaches, while identifying key design decisions of such approaches. We then implement an outlier detection framework and conduct a factorial design experiment to understand the pros and cons of various optimizations proposed by us as well as those proposed in the literature, both independently and in conjunction with one another, on a diverse set of real-life datasets. To the best of our knowledge this is the first such study in the literature. The outcome of this study is a family of state of the art distance-based outlier detection algorithms. Our detailed empirical study supports the following observations. The combination of optimization strategies enables significant efficiency gains. Our factorial design study highlights the important fact that no single optimization or combination of optimizations (factors) always dominates on all types of data. Our study also allows us to characterize when a certain combination of optimizations is likely to prevail and helps provide interesting and useful insights for moving forward in this domain.
Differences Among Cell-structure Ontologies: FMA, GO, & CCO

When different groups create models or ontologies of the same knowledge domain, this creates challenges for knowledge sharing. To identify these challenges, we compare cellular structure as modeled by the Foundational Model of Anatomy(FMA), the Gene Ontology (GO), and the Cell Component Ontology (CCO). These ontologies all model the physical anatomy of a cell, and we expected them to be similar in scope. However, we discovered that the actual differences among the mare substantial. These differences represent variations based on theory-driven vs. emergent construction,as well as differences in how small application ontologies like the CCO are created from reference ontologies. In this paper, we provide a description and analysis of these differences. By studying differences in language, granularity, breadth of coverage,and model organization, we hope to gain a better understanding of how to map between related ontologies.
Engineering Adaptive Digital Investigations using Forensics Requirements

A digital forensic investigation aims to collect and analyse the evidence necessary to demonstrate a potential hypothesis of a digital crime. Despite the availability of several digital forensics tools, investigators still approach each crime case from scratch, postulating potential hypotheses and analysing large volumes of data. This paper proposes to explicitly model forensic requirements in order to engineer software systems that are forensic-ready and guide the activities of a digital investigation. Forensic requirements relate some speculative hypotheses of a crime to the evidence that should be collected and analysed in a crime scene. In contrast to existing approaches, we propose to perform proactive activities to preserve important - potentially ephemeral - evidence, depending on the risk of a crime to take place. Once an investigation starts, the evidence collected proactively is analysed to assess if some of the speculative hypotheses of a crime hold and what further evidence is necessary to support them. For each hypothesis that is satisfied, a structured argument is generated to demonstrate how the evidence collected supports that hypothesis. Our evaluation results suggest that the approach provides correct investigative findings and reduces significantly the amount of evidence to be collected and the hypotheses to be analysed.
An ontological and hierarchical approach for supply chain event aggregation

Time Petri Nets (TPN) have been applied to model the basic event patterns that arise commonly in supply chains. Additionally these TPN-specified patterns can be aggregated to create more complicated supply chain event systems. In our previous work, meanwhile, we introduced SCOPE (Situation Calculus Ontology for PEtri nets), which semantically describes Petri Nets using the Situation Calculus. In this paper, we show that TESCOPE, which extends SCOPE to incorporate the concept of time, can be naturally applied for supply chain event aggregation. That is, we show that supply-chain event patterns can be easily represented as TESCOPE-based Golog procedures, where Golog is a logic language built on top of the Situation Calculus; We further demonstrate by examples that these basic Golog procedures can be aggregated semantically and hierarchically into complex ones.