Miracle-dz/UniFAR-FTUs huggingface dataset:
A Map of the Protein Space: An Automatic Hierarchical Classification of all Protein Sequences

We investigate the space of all protein sequences. We combine the standard measures of similarity (SW, FASTA, BLAST), to associate with each sequence an exhaustive list of neighboring sequences. These lists induce a (weighted directed) graph whose vertices are the sequences. The weight of an edge connecting two sequences represents their degree of similarity. This graph encodes much of the fundamental properties of the sequence space. We look for clusters of related proteins in this graph. These clusters correspond to strongly connected sets of vertices. Two main ideas underlie our work: i) Interesting homologies among proteins can be deduced by transitivity. ii) Transitivity should be applied restrictively in order to prevent unrelated proteins from clustering together. Our analysis starts from a very conservative classification, based on very significant similarities, that has many classes. Subsequently, classes are merged to include less significant similarities. Merging is performed via a novel two phase algorithm. First, the algorithm identifies groups of possibly related clusters (based on transitivity and strong connectivity) using local considerations, and merges them. Then, a global test is applied to identify nuclei of strong relationships within these groups of clusters, and the classification is refined accordingly. This process takes place at varying thresholds of statistical significance, where at each step the algorithm is applied on the classes of the previous classification, to obtain the next one, at the more permissive threshold. Consequently, a hierarchical organization of all proteins is obtained. The resulting classification splits the space of all protein sequences into well defined groups of proteins. The results show that the automatically induced sets of proteins are closely correlated with natural biological families and super families. The hierarchical organization reveals finer sub-families that make up known families of proteins as well as many interesting relations between protein families. The hierarchical organization proposed may be considered as the first map of the space of all protein sequences. An interactive web site including the results of our analysis has been constructed, and is now accessible through http:/(/)www.protomap. cs.huji. ac.il
Transient versus Equilibrium Response of the Ocean’s Overturning Circulation to Warming

Much of the existing theory for the ocean’s overturning circulation considers steady-state equilibrium solutions. However, Earth’s climate is not in a steady state, and a better understanding of the ocean’s nonequilibrium response to changes in the surface climate is urgently needed. Here, the time-dependent response of the deep-ocean overturning circulation to atmospheric warming is examined using a hierarchy of idealized ocean models. The transient response to surface warming is characterized by a shoaling and weakening of the Atlantic meridional overturning circulation (AMOC)—consistent with results from coupled climate simulations. The initial shoaling and weakening of the AMOC occurs on decadal time scales and is attributed to a rapid warming of northern-sourced deep water. The equilibrium response to warming, in contrast, is associated with a deepening and strengthening of the AMOC. The eventual deepening of the AMOC is argued to be associated with abyssal density changes and driven by modified surface fluxes in the Southern Ocean, following a reduction of the Antarctic sea ice cover. Full equilibration of the AMOC requires a diffusive adjustment of the abyss and takes many millennia. The equilibration time scale is much longer than most coupled climate model simulations, highlighting the importance of considering integration time and initial conditions when interpreting the deep-ocean circulation in climate models. The results also show that past climates are unlikely to be an adequate analog for changes in the overturning circulation during the coming decades or centuries.
Minimal-Intelligence Agents for Bargaining Behaviors in Market-Based Environments

This report describes simple mechanisms that allow autonomous software agents to en gage in bargaining behaviors in market based environments Groups of agents with such mechanisms could be used in applications including market based control internet com merce and economic modelling After an introductory discussion of the rationale for this work and a brief overview of key concepts from economics work in market based control is reviewed to highlight the need for bargaining agents Following this the early experimental economics work of Smith and the recent results of Gode and Sunder are de scribed Gode and Sunder s work using zero intelligence zi traders that act randomly within a structured market appears to imply that convergence to the theoretical equilib rium price is determined more by market structure than by the intelligence of the traders in that market if this is true developing mechanisms for bargaining agents is of very limited relevance However it is demonstrated here that the average transaction prices of zi traders can vary signi cantly from the theoretical equilibrium level when supply and demand are asymmetric and that the degree of di erence from equilibrium is predictable from a pri ori statistical analysis In this sense it is shown here that Gode and Sunder s results are artefacts of their experimental regime Following this zero intelligence plus zip traders are introduced like zi traders these simple agents make stochastic bids Unlike zi traders they employ an elementary form of machine learning Groups of zip traders interacting in experimental markets similar to those used by Smith and Gode and Sunder are demonstrated and it is shown that the performance of zip traders is signi cantly closer to the human data than is the performance of Gode and Sunder s zi traders This document reports on work done during February to September while the author held a Visiting Academic post at Hewlett Packard Laboratories Bristol Filton Road Bristol BS QZ U K
Harnessing WordNet Senses for Supervised Sentiment Classification

Traditional approaches to sentiment classification rely on lexical features, syntax-based features or a combination of the two. We propose semantic features using word senses for a supervised document-level sentiment classifier. To highlight the benefit of sense-based features, we compare word-based representation of documents with a sense-based representation where WordNet senses of the words are used as features. In addition, we highlight the benefit of senses by presenting a part-of-speech-wise effect on sentiment classification. Finally, we show that even if a WSD engine disambiguates between a limited set of words in a document, a sentiment classifier still performs better than what it does in absence of sense annotation. Since word senses used as features show promise, we also examine the possibility of using similarity metrics defined on WordNet to address the problem of not finding a sense in the training corpus. We perform experiments using three popular similarity metrics to mitigate the effect of unknown synsets in a test corpus by replacing them with similar synsets from the training corpus. The results show promising improvement with respect to the baseline.
Architectural design of a distributed application with autonomic quality requirements

An autonomic system is essentially characterized by quality requirements that specify that the system should be able to adapt itself (configure optimize, heal, etc.) under varying circumstances and situations. These quality requirements call for an architecture centric software engineering approach. In this paper, we discuss and illustrate the architectural design of a complex real-world distributed application with autonomic quality requirements. In particular, we present an architecture with autonomous entities (agents) for managing warehouse logistics. We illustrate how the subsequent architectural decisions are guided by a reference architecture for situated multi-agent systems on the one hand, and by functional and quality requirements of the application on the other hand.
Dictionary Learning from Ambiguously Labeled Data

We propose a novel dictionary-based learning method for ambiguously labeled multiclass classification, where each training sample has multiple labels and only one of them is the correct label. The dictionary learning problem is solved using an iterative alternating algorithm. At each iteration of the algorithm, two alternating steps are performed: a confidence update and a dictionary update. The confidence of each sample is defined as the probability distribution on its ambiguous labels. The dictionaries are updated using either soft (EM-based) or hard decision rules. Extensive evaluations on existing datasets demonstrate that the proposed method performs significantly better than state-of-the-art ambiguously labeled learning approaches.
The Nonfunctional Allele TCRBV 6 S 1 B Is Strongly Associated with Helicobacter pylori Infection

ERDMUTE KUNSTMANN,* CORNELIA HARDT, ERCAN ELITOK, MARIANNE HARDER, SEBASTIAN SUERBAUM, ULRICH PEITZ, WOLFF SCHMIEGEL, AND JÖRG T. EPPLEN Department of Molecular Human Genetics, Ruhr-University, 44780 Bochum, Department of Internal Medicine, Ruhr-University, 44892 Bochum, Institut für Humangenetik, Universitätsklinikum, 45122 Essen, Institute of Hygiene and Microbiology, University of Wuerzburg, 97080 Wuerzburg, and Medical Department, Otto-von-Guericke-University, 39120 Magdeburg, Germany
Data-Driven Metaphor Recognition and Explanation

Recognizing metaphors and identifying the source-target mappings is an important task as metaphorical text poses a big challenge for machine reading. To address this problem, we automatically acquire a metaphor knowledge base and an isA knowledge base from billions of web pages. Using the knowledge bases, we develop an inference mechanism to recognize and explain the metaphors in the text. To our knowledge, this is the first purely data-driven approach of probabilistic metaphor acquisition, recognition, and explanation. Our results shows that it significantly outperforms other state-of-the-art methods in recognizing and explaining metaphors.
Power Spectrum and Signatures for Cascade Inflation

The power spectrum of M-theory cascade inflation is derived. It possesses three distinctive signatures: a decisive power suppression at small scales, oscillations around the scales that cross the horizon when the inflaton potential jumps and stepwise decrease in the scalar spectral index. All three properties result from features in the inflaton potential. Cascade inflation realizes assisted inflation in heterotic M-theory and is driven by non-perturbative interactions of N M5-branes. The features in the inflaton potential are generated whenever two M5-branes collide with the boundaries. The derived small-scale power suppression serves as a possible explanation for the dearth of observed dwarf galaxies in the Milky Way halo. The oscillations, furthermore, allow to directly probe M-theory by measurements of the spectral index and to distinguish cascade inflation observationally from other string inflation models.
Recent Advancement in the Thermal Design of Electric Motors

Traditionally electric motor manufactures have tended to concentrate effort on improving the electromagnetic design and have only dealt with the thermal design aspects superficially. However the increasing requirement for miniaturisation, energy efficiency and cost reduction, and the need to fully exploit new topologies and materials, make it imperative to analyse the thermal circuit to the same extent as the electromagnetic design.
A Hierarchical Graphical Model for Record Linkage

The task of matching co-referent records is known among other names as record linkage. For large record-linkage problems, often there is little or no labeled data available, but unlabeled data shows a reasonably clear structure. For such problems, unsupervised or semi-supervised methods are preferable to supervised methods. In this paper, we describe a hierarchical graphical model framework for the record-linkage problem in an unsupervised setting. In addition to proposing new methods, we also cast existing unsupervised probabilistic record-linkage methods in this framework. Some of the techniques we propose to minimize overfitting in the above model are of interest in the general graphical model setting. We describe a method for incorporating monotonicity constraints in a graphical model. We also outline a bootstrapping approach of using "single-field" classifiers to noisily label latent variables in a hierarchical model. Experimental results show that our proposed unsupervised methods perform quite competitively even with fully supervised record-linkage methods.
Campylobacter jejuni sequence types show remarkable spatial and temporal stability in Blackbirds

Background The zoonotic bacterium Campylobacter jejuni has a broad host range but is especially associated with birds, both domestic and wild. Earlier studies have indicated thrushes of the genus Turdus in Europe to be frequently colonized with C. jejuni, and predominately with host-associated specific genotypes. The European Blackbird Turdus merula has a large distribution in Europe, including some oceanic islands, and was also introduced to Australia by European immigrants in the 1850s. Methods The host specificity and temporal stability of European Blackbird C. jejuni was investigated with multilocus sequence typing in a set of isolates collected from Sweden, Australia, and The Azores. Results Remarkably, we found that the Swedish, Australian, and Azorean isolates were genetically highly similar, despite extensive spatial and temporal isolation. This indicates adaptation, exquisite specificity, and stability in time for European Blackbirds, which is in sharp contrast with the high levels of recombination and mutation found in poultry-related C. jejuni genotypes. Conclusion The maintenance of host-specific signals in spatially and temporally separated C. jejuni populations suggests the existence of strong purifying selection for this bacterium in European Blackbirds.
Average-Case Analysis of First Fit and Random Fit Bin Packing

We prove that the First Fit bin packing algorithm is stable under the input distribution U f k − 2 ; k g for all k (cid:21) 3, settling an open question from the recent survey by Coffman, Garey, and Johnson [3]. Our proof generalizes the multi-dimensional Markov chain analysis used by Kenyon, Rabani, and Sinclair to prove that Best Fit is also stable under these distributions [11]. Our proof is motivated by an analysis of Random Fit, a new simple packing algorithm related to First Fit, that is interesting in its own right. We show that Random Fit is stable under the input distributions U f k − 2 ; k g , as well as present worst-case bounds and some results on distributions U f k − 1 ; k g and U f k ; k g for Random Fit.
Surgically-induced mouse models in the study of bone regeneration: Current models and future directions

Bone regeneration has been extensively studied over the past several decades. The surgically-induced mouse model is the key animal model for studying bone regeneration, of the various research strategies used. These mouse models mimic the trauma and recovery processes in vivo and serve as carriers for tissue engineering and gene modification to test various therapies or associated genes in bone regeneration. The present review introduces a classification of surgically induced mouse models in bone regeneration, evaluates the application and value of these models and discusses the potential development of further innovations in this field in the future.
Characterization of ultrahydrophobic hierarchical surfaces fabricated using a single-step fabrication methodology

Hydrophobic surfaces with microscale roughness can be rendered ultrahydrophobic by the addition of sub-micron-scale roughness. A simple yet highly effective concept of fabricating hierarchical structured surfaces using a single-step deep reactive ion etch process is proposed. Using this method the complexities generally associated with the fabrication of two-tier roughness structures are eliminated. Three two-tier roughness surfaces with different roughness parameters are fabricated and tested. The surfaces are characterized in terms of the static contact angle and roll-off angle, and are compared with surfaces consisting of only single-tier microscale roughness. The evaporation characteristics of a sessile droplet on the hierarchical surfaces is also assessed relative to comparable single-roughness (SR) surfaces. The robustness of the new hierarchical roughness surfaces is verified through droplet impingement tests. The hierarchical surfaces exhibit very high contact angle and lower contact angle hysteresis compared to the SR surfaces and are more resistant to wetting. The energy loss during impact on the surfaces is quantified in terms of the coefficient of restitution for droplets bouncing off the surface.
Oral Administration of Bepotastine Besilate Suppressed Scratching Behavior of Atopic Dermatitis Model NC/Nga Mice

Background: Pruritus is the most severe problem in atopic dermatitis. Even though its mechanism is still not fully understood, antihistamines have been prescribed for atopic dermatitis. Objective: To evaluate the effect of antihistamine on atopic dermatitis, we analyzed the scratching behavior in atopic dermatitis model NC/Nga mice. Methods: BALB/c mice, in which scratching behavior was induced by intradermal injection of compound 48/80 (100 µg/100 µl/mouse), and NC/Nga mice, housed in a conventional environment and having developed spontaneous eczematous regions, were monitored with a SCLABA system after oral administration of bepotastine besilate. The number of eosinophils in the ear skin and the serum leukotriene B4 (LTB4) levels were also evaluated. Results: Bepotastine at doses of 3 and 10 mg/kg effectively inhibited the compound 48/80-induced scratching behavior of BALB/c mice 1 h after oral administration, comparable with the blood Tmax, which was reached within 0.8–1.6 h in humans. Bepotastine also significantly inhibited the scratching behavior of NC/Nga mice 1 h after oral administration. Even though 10 mg/kg bepotastine could not influence the number of tissue eosinophils, it effectively suppressed the serum LTB4 levels, just comparable with the suppression of scratch behavior of NC/Nga mice. Conclusion: Bepotastin effectively suppressed the scratch behavior of atopic dermatitis model mice, which may not simply be explained by the suppression of histamine but also by the suppression of other mediators like LTB4. Bepotastine could be useful in the treatment of pruritus, especially early after oral administration.
An integrative approach to modeling biological networks

Summary Networks are used to model real-world phenomena in various domains, including systems biology. Since proteins carry out biological processes by interacting with other proteins, it is expected that cellular functions are reflected in the structure of protein-protein interaction (PPI) networks. Similarly, the topology of residue interaction graphs (RIGs) that model proteins’ 3-dimensional structure might provide insights into protein folding, stability, and function. An important step towards understanding these networks is finding an adequate network model, since models can be exploited algorithmically as well as used for predicting missing data. Evaluating the fit of a model network to the data is a formidable challenge, since network comparisons are computationally infeasible and thus have to rely on heuristics, or “network properties. ” We show that it is difficult to assess the reliability of the fit of a model using any network property alone. Thus, we present an integrative approach that feeds a variety of network properties into five machine learning classifiers to predict the best-fitting network model for PPI networks and RIGs. We confirm that geometric random graphs (GEO) are the best-fitting model for RIGs. Since GEO networks model spatial relationships between objects and are thus expected to replicate well the underlying structure of spatially packed residues in a protein, the good fit of GEO to RIGs validates our approach. Additionally, we apply our approach to PPI networks and confirm that the structure of merged data sets containing both binary and co-complex data that are of high coverage and confidence is also consistent with the structure of GEO, while the structure of less complete and lower confidence data is not. Since PPI data are noisy, we test the robustness of the five classifiers to noise and show that their robustness levels differ. We demonstrate that none of the classifiers predicts noisy scale-free (SF) networks as GEO, whereas noisy GEOs can be classified as SF. Thus, it is unlikely that our approach would predict a real-world network as GEO if it had a noisy SF structure. However, it could classify the data as SF if it had a noisy GEO structure. Therefore, the structure of the PPI networks is the most consistent with the structure of a noisy GEO.
Cardiovascular risk estimation in older persons: SCORE O.P.

Aims Estimation of cardiovascular disease risk, using SCORE (Systematic COronary Risk Evaluation) is recommended by European guidelines on cardiovascular disease prevention. Risk estimation is inaccurate in older people. We hypothesized that this may be due to the assumption, inherent in current risk estimation systems, that risk factors function similarly in all age groups. We aimed to derive and validate a risk estimation function, SCORE O.P., solely from data from individuals aged 65 years and older. Methods and results 20,704 men and 20,121 women, aged 65 and over and without pre-existing coronary disease, from four representative, prospective studies of the general population were included. These were Italian, Belgian and Danish studies (from original SCORE dataset) and the CONOR (Cohort of Norway) study. The variables which remained statistically significant in Cox proportional hazards model and were included in the SCORE O.P. model were: age, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, smoking status and diabetes. SCORE O.P. showed good discrimination; area under receiver operator characteristic curve (AUROC) 0.74 (95% confidence interval: 0.73 to 0.75). Calibration was also reasonable, Hosmer–Lemeshow goodness of fit test: 17.16 (men), 22.70 (women). Compared with the original SCORE function extrapolated to the ≥65 years age group discrimination improved, p = 0.05 (men), p < 0.001 (women). Simple risk charts were constructed. On simulated external validation, performed using 10-fold cross validation, AUROC was 0.74 and predicted/observed ratio was 1.02. Conclusion SCORE O.P. provides improved accuracy in risk estimation in older people and may reduce excessive use of medication in this vulnerable population.
Using V2V communication to create Over-the-horizon Awareness in multiple-lane highway scenarios

This paper describes the Over-the-Horizon Awareness (OTHA) protocol which provides an extended view of the traffic ahead to Driver Support Systems (DSS) by means of multi-hop vehicle-to-vehicle communication. The protocol extends and adapts the TrafficFilter [1] to make it suitable for multiple-lane highway scenarios. As basis, we rely on the information required by the Congestion Assistant [2], a driver support system that aids drivers in traffic jams. Simulation results show that OTHA achieves high node reachability and information accuracy.