Miracle-dz/UniFAR-FTUs huggingface dataset:
An unusual beta-spectrin associated with clustered acetylcholine receptors

The clustering of acetylcholine receptors (AChR) in the postsynaptic membrane is an early event in the formation of the neuromuscular junction. The mechanism of clustering is still unknown, but is generally believed to be mediated by the postsynaptic cytoskeleton. We have identified an unusual isoform of beta-spectrin which colocalizes with AChR in AChR clusters isolated from rat myotubes in vitro. A related antigen is present postsynaptically at the neuromuscular junction of the rat. Immunoprecipitation, peptide mapping and immunofluorescence show that the beta-spectrin in AChR clusters resembles but is distinct from the beta-spectrin of human erythrocytes. alpha-Spectrin appears to be absent from AChR clusters. Semiquantitative immunofluorescence techniques indicate that there are from two to seven beta-spectrin molecules present for every clustered AChR, the higher values being obtained from rapidly prepared clusters, the lower values from clusters that require several minutes or more for isolation. Upon incubation of isolated AChR clusters for 1 h at room temperature, beta-spectrin is slowly depleted and the AChR redistribute into microaggregates. The beta-spectrin that remains associated with the myotube membrane is concentrated at these microaggregates. beta- Spectrin is quantitatively lost from clusters upon digestion with chymotrypsin, which causes AChR to redistribute in the plane of the membrane. These results suggest that AChR in clusters is closely linked to an unusual isoform of beta-spectrin.
Contrasting Tactics in Motor Control by Vertebrates and Arthropods1

Abstract Vertebrates and arthropods share the common problem of controlling a rigid, articulated skeleton using neurally-controlled, striated muscle. Since this condition has arisen independently in the two groups, there is no reason to assume, a priori, that the control mechanisms used by the two groups will be the same. Indeed, there appear to be fundamental differences in the tactics used by the two groups. Insects and crustaceans use small numbers of heterogeneous motoneurons, while vertebrates (mammals especially) use many, more homogeneous, motor axons. In particular, arthropods make extensive use of peripheral neuromodulation to alter the properties of both neuromuscular junctions and muscle fibers. There has been little consideration of the functional consequences of these differences. I suggest that, faced with a size constraint on the number of motor units available, arthropods use peripheral modulation of muscle properties to achieve the flexibility and dynamic range that vertebrates achieve through recruitment of motor units.
THE EFFECT OF DIRECTED READING THINKING ACTIVITY (DRTA) LEARNING STRATEGY AND READING INTEREST FOR READING COMPREHENSION ABILITY

This research is intended to find the different of reading comprehension ability of student that teached by teaching Directed Reading Thinking Activity (DRTA) learning strategy within Know-Want to Know-Learned (KWL) learning strategy, the different of reading comprehension ability of student that have the high reading interest and low reading interest, and the interaction between learning strategy and studied reading interest on the reading comprehension ability. This study was a quasi experimental research methode design by 2×2 factorial design. Population of the research are all student at fifth grade of elementary school in Laweyan district Surakarta in the academic year of 2017/2018. Samples were taken by cluster random sampling technique. The selected sample are Pajang 1 elementary school and Karangasem 1 elementary School. The data collecting uses test and non test technique. This research used two ways anava data analysis technique and used Scheffe method as a further testing after two ways anova with 5% of significance level. The result of research shows that a different of reading comprehension ability of student that teached by Directed Reading Thinking (DRTA) learning strategy within Know-Want to Know-Learned (KWL) learning strategy, there was a different of reading comprehension ability of student that have the high reading interest and low reading interest, and there was an interaction between learning strategy and reading interest on the reading comprehension ability.
ESD protection design for gigahertz differential LNA in a 65-nm CMOS process

The electrostatic discharge (ESD) immunity test for EMC was one important reliability regulation. The turn-on-efficient on-chip ESD protection circuit is required to clamp the overstress voltage. A new design of ESD protection diodes with embedded silicon-controlled rectifier (SCR) was proposed to protect the gigahertz differential low-noise amplifier (LNA). Experimental results had shown that the proposed ESD protection design for the differential LNA can achieve excellent ESD robustness and good RF performances.
CAR: Context-Aware Adaptive Routing for Delay-Tolerant Mobile Networks

Most of the existing research work in mobile ad hoc networking is based on the assumption that a path exists between the sender and the receiver. On the other hand, applications of decentralised mobile systems are often characterised by network partitions. As a consequence delay tolerant networking research has received considerable attention in the recent years as a means to obviate to the gap between ad hoc network research and real applications. In this paper we present the design, implementation and evaluation of the context-aware adaptive routing (CAR) protocol for delay tolerant unicast communication in intermittently connected mobile ad hoc networks. The protocol is based on the idea of exploiting nodes as carriers of messages among network partitions to achieve delivery. The choice of the best carrier is made using Kalman filter based prediction techniques and utility theory. We discuss the implementation of CAR over an opportunistic networking framework, outlining possible applications of the general principles at the basis of the proposed approach. The large scale performance of the CAR protocol are evaluated using simulations based on a social network founded mobility model, a purely random one and real traces from Dartmouth College.
Segmentation and localization of epithelial cells in the histopathological images of stomach adenocarcinoma

Histopathological examination of the stomach tissue samples visually by a pathologist is the confirmatory test available for stomach cancer diagnosis. As the manual interpretation is subjected to inter and intra-observer variability, an objective analysis is the subject of concern in the current scenario. Architectural analysis of the tissue samples proved to be effective in the diagnosis and grading of adenocarcinoma. The effectiveness of this kind of computer aided diagnosis and grading is greatly dependent on the exact localization of the individual epithelial cells of the glandular region. In this work an efficient and simple morphological operations were formulated for epithelial cell localization. Also the removal of the stromal cell nuclei which is usually done prior to feature extraction to increase the efficiency of the system is also discussed elaborately.
Characterizing energy consumption in a visual sensor network testbed

In this work we characterize the energy consumption of a visual sensor network testbed. Each node in the testbed consists of a "single-board computer", namely Crossbow's Stargate, equipped with a wireless network card and a Webcam. We assess energy consumption of activities representative of the target application (e.g., perimeter surveillance) using a benchmark that runs (individual and combinations of) "basic" tasks such as processing, flash memory access, image acquisition, and communication over the network. In our characterization, we consider the various hardware states the system switches through as it executes these benchmarks, e.g., different radio modes (sleep, idle, transmission, reception), and Webcam modes (off, on, and acquiring image). We report both steady-state and transient energy consumption behavior obtained by direct measurements of current with a digital multimeter. We validate our measurements against results obtained using the Stargate's on-board energy consumption measuring capabilities
Nonlinear ICA solutions for convolutive mixing of PNL mixtures

This paper introduces an ICA approach to a nonlinear convolutive BSS problem. The mixing model considered here is an evolution of the post nonlinear one: it is the convolutive mixing of PNL mixture. The main aim of this paper is to enlarge the set of blind sources separation problems that can be approached by nonlinear ICA with some stricter mixing environments than the one just widely described in literature. The flexibility of the algorithm is given by the on line estimation of the score function performed by spline neurons.
Improved space-time coding for MIMO-OFDM wireless communications

Improved space-time coding for multiple-input multiple-output orthogonal frequency division multiplexing is studied for wireless systems using QPSK modulation for four transmit and four receive antennas. A 256-state code is shown to perform within 3 dB of outage capacity (and within 2 dB with perfect channel estimation), which is better than any other published result without using iterative decoding.
Antiparallel protocadherin homodimers use distinct affinity- and specificity-mediating regions in cadherin repeats 1-4

Protocadherins (Pcdhs) are cell adhesion and signaling proteins used by neurons to develop and maintain neuronal networks, relying on trans homophilic interactions between their extracellular cadherin (EC) repeat domains. We present the structure of the antiparallel EC1-4 homodimer of human PcdhYB3, a member of the γ subfamily of clustered Pcdhs. Structure and sequence comparisons of α, β, and γ clustered Pcdh isoforms illustrate that subfamilies encode specificity in distinct ways through diversification of loop region structure and composition in EC2 and EC3, which contains isoform-specific conservation of primarily polar residues. In contrast, the EC1/EC4 interface comprises hydrophobic interactions that provide non-selective dimerization affinity. Using sequence coevolution analysis, we found evidence for a similar antiparallel EC1-4 interaction in non-clustered Pcdh families. We thus deduce that the EC1-4 antiparallel homodimer is a general interaction strategy that evolved before the divergence of these distinct protocadherin families.
A Casein Kinase II Phosphorylation Site in AtYY1 Affects Its Activity, Stability, and Function in the ABA Response

The phosphorylation and dephosphorylation of proteins are crucial in the regulation of protein activity and stability in various signaling pathways. In this study, we identified an ABA repressor, Arabidopsis Ying Yang 1 (AtYY1) as a potential target of casein kinase II (CKII). AtYY1 physically interacts with two regulatory subunits of CKII, CKB3, and CKB4. Moreover, AtYY1 can be phosphorylated by CKII in vitro, and the S284 site is the major CKII phosphorylation site. Further analyses indicated that S284 phosphorylation can enhance the transcriptional activity and protein stability of AtYY1 and hence strengthen the effect of AtYY1 as a negative regulator in the ABA response. Our study provides novel insights into the regulatory mechanism of AtYY1 mediated by CKII phosphorylation.
Mobile Target Localization Based on Mean Shift in Wireless Sensor Networks

In order to localize the mobile targets in real time and with high accuracy, by employing mean shift algorithm to generate the proposal distribution for the joint particle filter, this paper proposes a novel mobile target localization algorithm, which we called mean shift particle filter. The mean shift particle filter algorithm significantly improves the accuracy of the particle state estimation and reduces the necessary number of samples by using the current observations in sampling procedure to obtain a sample distribution. It also reduces the interference among multiple targets in close proximity by weighting samples according to the virtual hamming distances and interaction potentials. By arranging the state distributions of mobile targets, the proposed scheme can handle the multiple peaks in state estimation of mobile targets and improves the localization accuracy.
Identification of early salt stress responsive proteins in seedling roots of upland cotton (Gossypium hirsutum L.) employing iTRAQ-based proteomic technique

Soil salinity is a major abiotic stress that limits plant growth and agricultural productivity. Upland cotton (Gossypium hirsutum L.) is highly tolerant to salinity; however, large-scale proteomic data of cotton in response to salt stress are still scant. Here, an isobaric tag for relative and absolute quantitation (iTRAQ)-based proteomic technique was employed to identify the early differentially expressed proteins (DEPs) from salt-treated cotton roots. One hundred and twenty-eight DEPs were identified, 76 of which displayed increased abundance and 52 decreased under salt stress conditions. The majority of the proteins have functions related to carbohydrate and energy metabolism, transcription, protein metabolism, cell wall and cytoskeleton metabolism, membrane and transport, signal transduction, in addition to stress and defense. It is worth emphasizing that some novel salt-responsive proteins were identified, which are involved in cell cytoskeleton metabolism (actin-related protein2, ARP2, and fasciclin-like arabinogalactan proteins, FLAs), membrane transport (tonoplast intrinsic proteins, TIPs, and plasma membrane intrinsic proteins, PIPs), signal transduction (leucine-rich repeat receptor-like kinase encoding genes, LRR-RLKs) and stress responses (thaumatin-like protein, TLP, universal stress protein, USP, dirigent-like protein, DIR, desiccation-related protein PCC13-62). High positive correlation between the abundance of some altered proteins (superoxide dismutase, SOD, peroxidase, POD, glutathione S-transferase, GST, monodehydroascorbate reductase, MDAR, and malate dehydrogenase, MDH) and their enzyme activity was evaluated. The results demonstrate that the iTRAQ-based proteomic technique is reliable for identifying and quantifying a large number of cotton root proteins. qRT-PCR was used to study the gene expression levels of the five above-mentioned proteins; four patterns are consistent with those of induced protein. These results showed that the proteome of cotton roots under NaCl stress is complex. The comparative protein profiles of roots under salinity vs control improves the understanding of the molecular mechanisms involved in the tolerance of plants to salt stress. This work provides a good basis for further functional elucidation of these DEPs using genetic and/or other approaches, and, consequently, candidate genes for genetic engineering to improve crop salt tolerance.
Finding and Typing New Named Entities in Tibetan from Chinese-Tibetan Parallel Corpora

Currently there is much interest in the automatic acquisition of entities, with the goal of Named Entity Recognition (NER). However previous work has focused primarily on major languages, with the large, structured, and semantically rich knowledge bases and using the large corpus with annotated NER tags. In this paper, we describe a method for Chinese-Tibetan bilingual named entity recognition using easily obtainable bilingual dictionary and parallel political corpora. We present two distinct steps for NER, one step identifying entity candidates in Tibetan, and the second step typing the entity into the semantic class. We then test the approach on the dataset and give the analysis of NE type errors.
Suboptimal Adaptive Filter for Discrete-Time Linear Stochastic Systems

This paper considers the problem of recursive filtering for linear discrete-time systems with uncertain observation. A new approximate adaptive filter with a parallel structure is herein proposed. It is based on the optimal mean square combination of arbitrary number of correlated estimates which is also derived. The equation for error covariance characterizing the mean-square accuracy of the new filter is derived. In consequence of parallel structure of the filtering equations the parallel computers can be used for their design. It is shown that this filter is very effective for multisensor systems containing different types of sensors. A practical implementation issue to consider this filter is also addressed. Example demonstrates the accuracy and efficiency of the proposed filter.
An Instance-based Learning Approach for Ontology Matching

This paper proposes an instance-based learning approach for the ontology matching problem. This approach is applicable to scenarios where instances of the ontologies to be matched are exchanged between sources. An initial population of instances is used as a training set of a non-supervised algorithm that constructs mappings between properties of classes from the ontologies. To demonstrate the e ectiveness of our approach, we carried out a set of experiments in which we varied the size of the training set for two di erent ontologies. Our results showed that our approach achieves low false positive rates for su cient large datasets, although it is totally dependent on the heterogeneity of the domains of properties.
The dark side of mSUGRA

We study the μ μ>0 in global fits is calculated with Markov Chain Monte Carlo methods and bridge sampling. The fits include state-of-the-art two-loop MSSM contributions to the electroweak observables MW and sin 2θwl, as well as the anomalous magnetic moment of the muon (g−2)μ, the relic density of dark matter and other relevant indirect observables. μ 0) = 0.07−0.16.
Computational Pipeline for NIRS-EEG Joint Imaging of tDCS-Evoked Cerebral Responses—An Application in Ischemic Stroke

Transcranial direct current stimulation (tDCS) modulates cortical neural activity and hemodynamics. Electrophysiological methods (electroencephalography-EEG) measure neural activity while optical methods (near-infrared spectroscopy-NIRS) measure hemodynamics coupled through neurovascular coupling (NVC). Assessment of NVC requires development of NIRS-EEG joint-imaging sensor montages that are sensitive to the tDCS affected brain areas. In this methods paper, we present a software pipeline incorporating freely available software tools that can be used to target vascular territories with tDCS and develop a NIRS-EEG probe for joint imaging of tDCS-evoked responses. We apply this software pipeline to target primarily the outer convexity of the brain territory (superficial divisions) of the middle cerebral artery (MCA). We then present a computational method based on Empirical Mode Decomposition of NIRS and EEG time series into a set of intrinsic mode functions (IMFs), and then perform a cross-correlation analysis on those IMFs from NIRS and EEG signals to model NVC at the lesional and contralesional hemispheres of an ischemic stroke patient. For the contralesional hemisphere, a strong positive correlation between IMFs of regional cerebral hemoglobin oxygen saturation and the log-transformed mean-power time-series of IMFs for EEG with a lag of about −15 s was found after a cumulative 550 s stimulation of anodal tDCS. It is postulated that system identification, for example using a continuous-time autoregressive model, of this coupling relation under tDCS perturbation may provide spatiotemporal discriminatory features for the identification of ischemia. Furthermore, portable NIRS-EEG joint imaging can be incorporated into brain computer interfaces to monitor tDCS-facilitated neurointervention as well as cortical reorganization.
A Markov Modulated Characterization of Packetized Voice and Data Traffic and Related Statistical Multiplexer Performance

We study the performance of a statistical multiplexer whose inputs consist of a superposition of packetized voice sources and data. The performance analysis predicts voice packet delay distributions, which usually have a stringent requirement, as well as data packet delay distributions. The superposition is approximated by a correlated Markov modulated Poisson process (MMPP), which is chosen such that several of its statistical characteristics identically match those of the superposition. Matrix analytic methods are then used to evaluate system performance measures. In particular, we obtain moments of voice and data delay distributions and queue length distributions. We also obtain Laplace-Stieitjes transforms of the voice and data packet delay distributions, which are numerically inverted to evaluate tails of delay distributions. It is shown how the matrix analytic methodology can incorporate practical system considerations such as finite buffers and a class of overload control mechanisms discussed in the literature. Comparisons with simulation show the methods to be accurate. The numerical results for the tails of the voice packet delay distribution show the dramatic effect of traffic variability and correlations on performance.
Protection against group B meningococcal disease. I. Comparison of group-specific and type-specific protection in the chick embryo model

Protection against group B meningococcal infection was examined using the chick embryo. 12-day-old embryos were challenged intravenously with various meningococcal strains. The chick embryo has an active reticuloendothelial system but lacks functional complement. In this model we found that protection against group B infection was primarily group specific. The group B polysaccharide antibody is an effective opsonin, but is a very poor bactericidal antibody. In contrast, the serotype antibody was bactericidal but only slightly protective in the chick embryo where protection is primarily phagocytic in nature. The group-specific and type-specific antibodies are strongly synergistic. Minute amounts of group B polysaccharide antibody caused a very significant increase in the protective effects of the serotype antibody.
IRADABE2: Lexicon Merging and Positional Features for Sentiment Analysis in Italian

English. This paper presents the participation of the IRADABE team to the SENTIPOLC 2016 task. This year we investigated the use of positional features together with the fusion of sentiment analysis resources with the aim to classify Italian tweets according to subjectivity, polarity and irony. Our approach uses as starting point our participation in the SENTIPOLC 2014 edition. For classification we adopted a supervised approach that takes advantage of support vector machines and neural networks. Italiano. Quest’articolo presenta il lavoro svolto dal team IRADABE per la partecipazione al task SENTIPOLC 2016. Il lavoro svolto include l’utilizzo di caratteristiche posizionali e la fusione di lessici specialistici, finalizzato alla classificazione di tweet in italiano, secondo la loro soggetività, polarità ed ironia. Il nostro approccio si basa sull’esperienza acquisita nel corso della partecipazione all’edizione 2014 di SENTIPOLC. Per la classificazione sono stati adottati dei metodi supervisionati come le macchine a supporto vettoriale e le reti neurali.
A MULTIPHASE FLUID-SOLID MODEL BASED ON THE LEVEL SET METHOD

The level set method provides a natural framework f r modelling multiphase phenomena in a Eulerian basis. The method is based on the evolution of the signed dist ance function from interfaces between phases of the flow , and automatically captures complex topological changes of the free surfaces such as breaking and merging. We pres nt an extension to the level set method for modelling the interaction between fluids and elastic solids, wher e the level set defines the boundary between fluid and so li . The multiphase method varies the underlying stress tens or depending on the sign of the level set, changing sm oothly from a rate of strain dependency within the liquid to a hypo-elastic strain dependency within the solid. We apply the model to two test problems, showing the potenti al of the model for industrial applications involving the coupled interaction of deformable elastic solids and fluids .