Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-color superresolution imaging of genetically expressed probes within individual adhesion complexes

Accurate determination of the relative positions of proteins within localized regions of the cell is essential for understanding their biological function. Although fluorescent fusion proteins are targeted with molecular precision, the position of these genetically expressed reporters is usually known only to the resolution of conventional optics (≈200 nm). Here, we report the use of two-color photoactivated localization microscopy (PALM) to determine the ultrastructural relationship between different proteins fused to spectrally distinct photoactivatable fluorescent proteins (PA-FPs). The nonperturbative incorporation of these endogenous tags facilitates an imaging resolution in whole, fixed cells of ≈20–30 nm at acquisition times of 5–30 min. We apply the technique to image different pairs of proteins assembled in adhesion complexes, the central attachment points between the cytoskeleton and the substrate in migrating cells. For several pairs, we find that proteins that seem colocalized when viewed by conventional optics are resolved as distinct interlocking nano-aggregates when imaged via PALM. The simplicity, minimal invasiveness, resolution, and speed of the technique all suggest its potential to directly visualize molecular interactions within cellular structures at the nanometer scale.
Pressure ulcers: e-learning to improve classification by nurses and nursing students.

AIMS AND OBJECTIVES To detect problems when classifying pressure ulcers and to examine whether an e-learning program is able to increase the classification skills of qualified nurses and nursing students. BACKGROUND Both the distinction between pressure ulcer grades and the differentiation between moisture lesions is difficult. Misclassification and incorrect identification of the lesions results in inadequate preventive and therapeutic measures. Education and training are important for spreading evidence-based insights about this topic. DESIGN Repeated measure design, consisting of one pretest and three posttests. METHODS The experimental intervention consisted of a one-hour session during which the participants independently went over an e-learning program. The control intervention consisted of a one-hour lecture. Both interventions had the same learning contents. A convenience sample of 212 qualified nurses and 214 final-year nursing students was randomly assigned to an experimental-and a control group. RESULTS In the pretest, the classification skills were low. After the intervention, these skills improved significantly in both groups. The nursing students achieved better results when using the e-learning program. Among the qualified nurses, there was no difference between the learning methods. Although the classification skills decreased in the posttests, they did not drop under the level of the pretest. CONCLUSION The actual classification system does not provide the necessary information about the severity of a pressure ulcer. The differential diagnosis between a moisture lesion was complicated. The classification skills of the qualified nurses and nursing students increased by both learning methods. Repetition remains, however, necessary. RELEVANCE TO CLINICAL PRACTICE Both instruction methods are adequate to acquire the knowledge about the differences between moisture lesions and pressure ulcers. E-learning allows studying in one's own time and place, and could therefore be very suitable for repetition of the training. How to organise this training could be a subject for further research.
Dimethylbis(pyrazine-2-carboxylato-κ2 N 1,O)tin(IV)

In the title compound, [Sn(CH3)2(C5H3N2O2)2], the SnIV atom is twice N,O-chelated by two pyrazine-2-carboxylate ligands. The distorted six-coordination is completed by two tin-bound methyl C atoms. The C2N2O2 donor set defines a skewed trapezoidal–bipyramidal geometry. Intermolecular π–π interactions between the pyrazine rings [centroid–centroid distance = 3.8112 (13) Å] are observed.
Protein structure prediction of CASP5 comparative modeling and fold recognition targets using consensus alignment approach and 3D assessment

For the fifth round of Critical Assessment of Techniques for Protein Structure Prediction (CASP5) all comparative modeling (CM) and fold recognition (FR) target proteins were modeled using a combination of consensus alignment strategy and 3D assessment. A large number and broad variety of prediction targets, with sequence identity between each modeled domain and the related known structure, ranging from 6 to 49%, represented all difficulty levels in comparative modeling and fold recognition. The critical steps in modeling, selection of template(s) and generation of sequence‐to‐structure alignment, were based on the results of secondary structure prediction and tertiary fold recognition carried out using the Meta Server coupled with the 3D‐Jury system. The main idea behind the modeling procedure was to select the most common alignment variants provided by individual servers, as well as to generate several alternatives for questionable regions and to evaluate them in 3D by building corresponding molecular models. Analysis of fold‐specific features and sequence conservation patterns for the target family was also widely used at this stage. For both CM and FR targets remote homologs of known structure were clearly recognized by the 3D‐Jury system. In the analogous fold recognition subcategory, the correct fold was identified for five out of eight domains. The average alignment accuracy for FR models (48%) was far less than for CM predictions (80%). These finding, coupled with the observation that in the majority of cases the submitted models were not closer to the experimental structure than their best templates, indicate that, especially for difficult targets, there is still ample room for improvement. Proteins 2003;53:410–417. © 2003 Wiley‐Liss, Inc.
Systematic effects in the sound horizon scale measurements

We investigate three potential sources of bias in distance estimations made assuming that a very simple estimator of the baryon acoustic oscillation (BAO) scale provides a standard ruler. These are the effects of the non-linear evolution of structure, scale-dependent bias and errors in the survey window function estimation. The simple estimator used is the peak of the smoothed correlation function, which provides a variance in the BAO scale that is close to optimal, if appropriate low-pass filtering is applied to the density field. While maximum-likelihood estimators can eliminate biases if the form of the systematic error is fully modelled, we estimate the potential effects of unmodelled or mis-modelled systematic errors. Non-linear structure growth using the Smith et al. prescription biases the acoustic scale by <0.3 per cent at z ≥ 1 under the correlation-function estimator. The biases due to representative but simplistic models of scale-dependent galaxy bias are below 1 per cent at z ≥ 1 for bias behaviour in the realms suggested by halo model calculations, which is expected to be below the statistical errors for a 1000-deg 2 spectroscopic survey. The distance bias due to a survey window function error is given in a simple closed form and it is shown that it has to be kept below 2 per cent so as not to bias acoustic scale more than 1 per cent at z = 1, although the actual tolerance can be larger depending upon galaxy bias. These biases are comparable to statistical errors for ambitious surveys if no correction is made for them. We show that rms photometric zero-point errors (at limiting magnitude 25 mag) below 0.14 and 0.01 mag for redshift z = 1 (red galaxies) and z = 3 (Lyman-break galaxies), respectively, are required in order to keep the distance estimator bias below 1 per cent.
Pursuing multiuser diversity in an OFDM system with decentralized channel state information

Usually, centralized channel state information (CSI) is assumed to pursue the multiuser diversity with a smart transmission scheduler. However, such centralized CSI will be impractical for a broadband system with a huge number of mobile users. In this paper, by concentrating on an orthogonal frequency division multiplex (OFDM) based broadband wireless communication system, a decentralized method to pursue multiuser diversity is proposed. The key in our proposal is the channel-aware ALOHA based subcarrier assignment: Each mobile user measures the channel on all subcarriers and tries to capture particular ones, by transmitting a request packet with probability p on these subcarriers, when it believes that its channels are good enough (e.g., when the measured channel fading level exceeds a predetermined threshold /spl gamma/). Several implementation schemes are developed, by attacking the optimization of p and /spl gamma/ with objective functions based on the downlink sum throughput. Computer simulations show that in comparison with the round-robin scheme, the developed schemes offer quite distinct throughput benefit and some further throughput improvement can be obtained when the correlation among the subcarrier channel gains is exploited with a subcarrier merging technique.
Genre Specific Dictionaries for Harmonic/Percussive Source Separation

Blind source separation usually obtains limited performance on real and polyphonic music signals. To overcome these limitations, it is common to rely on prior knowledge under the form of side information as in Informed Source Separation or on machine learning paradigms applied on a training database. In the context of source separation based on factorization models such as the Non-negative Matrix Factorization, this supervision can be introduced by learning specific dictionaries. However, due to the large diversity of musical signals it is not easy to build sufficiently compact and precise dictionaries that will well characterize the large array of audio sources. In this paper, we argue that it is relevant to construct genre-specific dictionaries. Indeed, we show on a task of harmonic/percussive source separation that the dictionaries built on genre-specific training subsets yield better performances than cross-genre dictionaries.
Cross-Kerr nonlinearity in optomechanical systems

We consider the response of a nanomechanical resonator interacting with an electromagnetic cavity via a radiation pressure coupling and a cross-Kerr coupling. Using a mean field approach we solve the dynamics of the system, and show the different corrections coming from the radiation pressure and the cross-Kerr effect to the usually considered linearized dynamics.
Association Rules and Predictive Models for e-Banking Services Vasilis

The introduction of data mining methods in the banking area although conducted in a slower way than in other fields, mainly due to the nature and sensitivity of bank data, can already be considered of great assistance to banks as to prediction, forecasting and decision making. One particular method is the investigation for association rules between products and services a bank offers. Results are generally impressive since in many cases strong relations are established, which are not easily observed at a first glance. These rules are used as additional tools aiming at the continuous improvement of bank services and products helping also in approaching new customers. In addition, the development and continuous training of prediction models is a very significant task, especially for bank organizations. The establishment of such models with the capacity of accurate prediction of future facts enhances the decision making and the fulfillment of the bank goals, especially in case these models are applied on specific bank units. E-banking can be considered such a unit receiving influence from a number of different sides. Scope of this paper is the demonstration of the application of data mining methods to e-banking. In other words association rules concerning e-banking are discovered using different techniques and a prediction model is established, depending on e-banking parameters like the transactions volume conducted through this alternative channel in relation with other crucial parameters like the number of active users.
Restriction site placement in virus genomes using Particle Swarm Optimization

The Presence of unique restriction sites (URSs) within a sequence is very important so that the sequence may be cut unambiguously in exactly one place with a restriction enzyme. Restriction site manipulation in virus genome may produce virus variants to serve as potential vaccines. Therefore, a number of applications are invented for the manipulation of viral genomes to produce attenuated viruses. Recently, automatic generation of URSs in the sequence has been investigated through different approaches (e.g., Greedy, Weighted Bipartite and Max-Min Gap) and found effective. The aim of this study is to investigate URS enhancement in a given virus genome considering it as an optimization task. URS placement (URSP) is a kind of pattern matching problem; therefore, formulation of existing optimization approach(s) to solve it efficiently is very important timely research so that working with large sized genomes becomes easy. In this study, Particle Swarm Optimization (PSO), the popular method of optimization, has been modified and formulated to solve URSP problem. The proposed URSP-PSO method has been tested on a set of benchmark virus genome sequences and found to increase URSs in a large number. The method is also found better than existing methods for large sized genome sequences.
Genomics NFk B Activation-Induced Anti-apoptosis Renders HER 2-Positive Cells Drug Resistant and Accelerates Tumor Growth

Breast cancers with HER2 overexpression are sensitive to drugs targeting the receptor or its kinase activity. HER2-targeting drugs are initially effective against HER2-positive breast cancer, but resistance inevitably occurs. We previously found that NF-kB is hyperactivated in a subset of HER2-positive breast cancer cells and tissue specimens. In this study, we report that constitutively active NF-kB rendered HER2-positive cancer cells resistant to anti-HER2 drugs and cells selected for lapatinib resistance upregulatedNF-kB. In both circumstances, cells were antiapoptotic and grew rapidly as xenografts. Lapatinib-resistant cells were refractory to HER2 and NF-kB inhibitors alone but were sensitive to their combination, suggesting a novel therapeutic strategy. A subset of NFkB–responsive genes was overexpressed in HER2-positive and triple-negative breast cancers, and patients with this NF-kB signature had poor clinical outcome. Anti-HER2 drug resistance may be a consequence of NF-kB activation, and selection for resistance results inNF-kB activation, suggesting that this transcription factor is central to oncogenesis and drug resistance. Clinically, the combined targeting of HER2 and NF-kB suggests a potential treatment paradigm for patients who relapse after anti-HER2 therapy. Patients with these cancers may be treated by simultaneously suppressing HER2 signaling and NF-kB activation. Implications:The combination of an inhibitor of IkB kinase (IKK) inhibitor and anti-HER2 drugsmay be a novel treatment strategy for drug-resistant human breast cancers. Mol Cancer Res; 12(3); 408–20. 2013 AACR.
Towards a proteomics meta-classification

There is a recognized need for a meta-classification that can serve as a foundation for more refined ontologies in the field of proteomics. Standard data sources classify proteins in terms of just one or two specific aspects. Thus SCOP (structural classification of proteins) is described as classifying proteins on the basis of structural features; SWlSS-PROT annotates proteins on the basis of their structure and of parameters like post-translational modifications. Such data sources are connected to each other by pairwise term-to-term mappings. However, there are obstacles which stand in the way of combining them together to form a robust meta-classification of the needed sort. We discuss some formal ontological principles which should be taken into account within the existing data sources in order to make such a meta-classification possible, taking into account also the gene ontology (GO) and its application to the annotation of proteins.
Computational Properties of Resolution-based Grounded Semantics

In the context of Dung's theory of abstract argumentation frameworks, the recently introduced resolution-based grounded semantics features the unique property of fully complying with a set of general requirements, only partially satisfied by previous literature proposals. This paper contributes to the investigation of resolution-based grounded semantics by analyzing its computational properties with reference to a standard set of decision problems for abstract argumentation semantics: (a) checking the property of being an extension for a set of arguments; (b) checking agreement with traditional grounded semantics; (c) checking the existence of a non-empty extension; (d) checking credulous acceptance of an argument; (e) checking skeptical acceptance of an argument. It is shown that problems (a)-(c) admit polynomial time decision processes, while (d) is NP-complete and (e) coNP-complete.
Monte-Carlo Bus Regulation

In this paper we want to minimize passengers waiting times at the bus stops by making buses wait at a stop. We compare a simple rule based approach to a Monte- Carlo method for this problem. When allocated enough time, the Monte-Carlo method gives better results. If the passengers arrivals and the bus travel times are known, the best algorithm is nested Monte-Carlo search with mem- orization which clearly outperforms nested Monte-Carlo search without memorization as well as Monte-Carlo and rule based regulation.
A Scalable Heuristic Scheduling Strategy for 60GHz WPAN STDMA System with Directional Antennas

This paper proposes a scalable heuristic spatial time division multiple access (STDMA) time-slot scheduling algorithm for millimeter-wave wireless personal area network (WPAN) with spatial division multiple access (SDMA) through directional antenna system. A detailed cross layer design is proposed including the medium access control (MAC) layer and the physical (PHY) layer designs. The proposed hybrid STDMA/SDMA scheduling engine possesses the capability of flexible throughput-enhancement employing different levels (i.e. heuristic order) of algorithm complexity. Firstly, it is found that the scheduling engine with exhaustive searching algorithm is able to improve up to 9-fold of the achievable system throughput. Secondly, it is determined that heuristic order of 5 is sufficient to provide optimum throughput enhancement with minimized system complexity. Thirdly, the analysis of required overhead to perform the STDMA scheduling engine is also discussed.
Integrating spatial information and geochemistry for improved lithological classification of drill hole samples

At Sunrise Dam Gold Mine (Western Australia) geochemical data has been collected from drill hole samples in order to improve the quality and consistency of geological logging and rock classification. Traditional visual logging of the drill core has proved very difficult due to multiple phases of alteration and the similarity in appearance of many of the lithological units. Classification of lithological units from geochemical data can provide consistent results; however using geochemical data without reference to its spatial context can result in very noisy downhole plots (i.e. the presence of numerous small units). This paper introduces a method of integrating spatial information by first grouping the drill hole samples into spatially continuous domains then applying clustering to these spatial domains. The results of this new method are compared against traditional techniques in order to demonstrate the noise reduction that can be achieved.
Fault detection and isolation using concatenated wavelet transform variances and discriminant analysis

A method for fault detection and isolation is developed using the concatenated variances of the continuous wavelet transform (CWT) of plant outputs. These concatenated variances are projected onto the principal component space corresponding to the covariance matrix of the concatenated variances. Fisher and quadratic discriminant analyses are then performed in this space to classify the concatenated sample CWT variances of outputs in a given time window. The sample variance is a variance estimator obtained by taking the displacement average of the squared wavelet transforms of the current outputs. This method provides an alternative to the multimodel approach used for fault detection and identification, especially when system inputs are unmeasured stochastic processes, as is assumed in the case of the mechanical system example. The performance of the method is assessed using matrices having the percentage of correct condition identification in the diagonal and the percentages misclassified conditions in the off-diagonal elements. Considerable performance improvements may be obtained due to concatenation-when two or more outputs are available-and to discriminant analysis, as compared with other wavelet variance methods.
Molecular Karyotypes of Hodgkin and Reed–Sternberg Cells at Disease Onset Reveal Distinct Copy Number Alterations in Chemosensitive versus Refractory Hodgkin Lymphoma

Purpose: To determine the recurring DNA copy number alterations (CNA) in classical Hodgkin lymphoma (HL) by microarray-based comparative genomic hybridization (aCGH) using laser capture microdissected CD30+ Hodgkin and Reed–Sternberg (HRS) cells. Experimental Design: Archived tissues from 27 CD30+ HL plus control samples were analyzed by DNA microarrays. The HL molecular karyotypes were compared with the genomic profiles of germinal center B cells and treatment outcome (chemotherapy responsive vs. primary refractory disease). Results: Gains and losses observed in more than 35% of HL samples were localized to 22 and 12 chromosomal regions, respectively. Frequent gains (>65%) were associated with growth and proliferation, NF-κB activation, cell-cycle control, apoptosis, and immune and lymphoid development. Frequent losses (>40%) observed encompassed tumor suppressor genes (SPRY1, NELL1, and ID4, inhibitor of DNA binding 4), transcriptional repressors (TXNIP, thioredoxin interacting protein), SKP2 (S-phase kinase-associated protein 2; ubiquitin ligase component), and an antagonist of NF-κB activation (PPARGC1A). In comparison to the germinal center profiles, the most frequent imbalances in HL were losses in 5p13 (AMACR, GDNF, and SKP2), and gains in 7q36 (SHH, sonic hedgehog homolog) and 9q34 (ABL1, CDK9, LCN2, and PTGES). Gains (>35%) in the HL chemoresponsive patients housed genes known to regulate T-cell trafficking or NF-κB activation (CCL22, CX3CL1, CCL17, DOK4, and IL10), whereas the refractory samples showed frequent loss of 4q27 (interleukin; IL21/IL2) and 17p12, and gain of 19q13.3 (BCL3/RELB). Conclusion: We identified nonrandom CNAs in the molecular karyotypes of classical HL. Several recurring genetic lesions correlated with disease outcome. These findings may be useful prognostic markers in the counseling and management of patients and for the development of novel therapeutic approaches in primary refractory HL. Clin Cancer Res; 17(10); 3443–54. ©2011 AACR.
A Sequencing Problem in Mixed-Model Assembly Line Including a Painting Line

In order to keep production balance at a mixed-model assembly line and a painting line, large WIP(Work- In-Process) inventories are required between two lines. To increase the eciency of line handling through reducing the inventories under this circumstance, this paper concerns with a sequencing problem for a mixed-model assembly line that includes a painting line where the uncertain elements regarding the defective products exist. Then, we formulate a new type of the sequencing problem minimizing the line stoppage time and the idle time with forecasting the supply time of the products from the painting line. Finally, we examine the eectiv eness of the proposed sequencing through computer simulations.