Miracle-dz/UniFAR-FTUs huggingface dataset:
Tumor-Associated Macrophages (TAMs) Form an Interconnected Cellular Supportive Network in Anaplastic Thyroid Carcinoma

Background A relationship between the increased density of tumor-associated macrophages (TAMs) and decreased survival was recently reported in thyroid cancer patients. Among these tumors, anaplastic thyroid cancer (ATC) is one of the most aggressive solid tumors in humans. TAMs (type M2) have been recognized as promoting tumor growth. The purpose of our study was to analyze with immunohistochemistry the presence of TAMs in a series of 27 ATC. Methodology/Principal Findings Several macrophages markers such as NADPH oxidase complex NOX2-p22phox, CD163 and CD 68 were used. Immunostainings showed that TAMs represent more than 50% of nucleated cells in all ATCs. Moreover, these markers allowed the identification of elongated thin ramified cytoplasmic extensions, bestowing a “microglia-like” appearance on these cells which we termed “Ramified TAMs” (RTAMs). In contrast, cancer cells were totally negative. Cellular stroma was highly simplified since apart from cancer cells and blood vessels, RTAMs were the only other cellular component. RTAMs were evenly distributed and intermingled with cancer cells, and were in direct contact with other RTAMs via their ramifications. Moreover, RTAMs displayed strong immunostaining for connexin Cx43. Long chains of interconnected RTAMs arose from perivascular clusters and were dispersed within the tumor parenchyma. When expressed, the glucose transporter Glut1 was found in RTAMs and blood vessels, but rarely in cancer cells. Conclusion ATCs display a very dense network of interconnected RTAMs in direct contact with intermingled cancer cells. To our knowledge this is the first time that such a network is described in a malignant tumor. This network was found in all our studied cases and appeared specific to ATC, since it was not found in differentiated thyroid cancers specimens. Taken together, these results suggest that RTAMs network is directly related to the aggressiveness of the disease via metabolic and trophic functions which remain to be determined.
A Solution to the Collective Action Problem in Corporate Reorganization

The voting rules in Chapter 11 are supposed to ensure that debtor firms are appropriately liquidated or reorganized. However, these convoluted and internally inconsistent rules are poorly designed to produce such outcomes, and there is no evidence that they do. However, the major proposals for reform, including auctions and options-trading, neglect the fundamentally collective nature of choices in reorganizations. We argue that a more appealing reform is the improvement of voting rules through the use of an economically efficient procedure known as quadratic voting, according to which stakeholders may cast a number of votes equal to the square root of the liquidation value of their claims. We discuss how quadratic voting could be implemented through reform of Chapter 11 and the advantages it would bring.
A Nanoparticle Approach towards Morphology Controlled Organic Photovoltaics (OPV)

Silicon nano-particles grafted with two different organic oligomers were prepared; the oligomers used were a phenylene-vinylene (PV) oligomer and a 3,3'''-didodecylquaterthiophene. The graftings were performed by the use of two different functional groups, the PV oligomer was grafted by a hydroxyl-group in the form of a phenol and a lithium derivative was used to graft the 3,3'''-didodecylquaterthiophene. The morphology and size of the grafted particles were analyzed by atomic force microscopy (AFM) and the extent of the grafting was analyzed by NMR. Organic photovoltaics with normal geometry (ITO/PEDOT:PSS/active layer/Al) were prepared using these materials as a donor and phenyl-C61-butyric acid methyl ester ([60]PCBM) as the acceptor and yielded a power conversion efficiency (PCE) of 0.27%, an open circuit voltage (VOC) of 0.93 V, a short circuit current density (JSC) of 0.89 mA/cm2, and a fill factor (FF) of 32.5% for a lead device with an active area of 0.25 cm2.
Presenting and Navigating Styled RDF using Context-maps

This paper introduces context-maps based on the concept browser Conzilla as a user-friendly interface to formal knowledge that is expressed in RDF. Context-maps provide ways to visualize and edit RDF as graphs, where selected resources and statements may be presented in a styled fashion. An important problem to adress is the fact that RDF expressions are verbose, complex and not very intuitive to work with
Binding of Hemagglutinin and Influenza Virus to a Peptide-Conjugated Lipid Membrane

Hemagglutinin (HA) plays an important role in the first step of influenza virus (IFV) infection because it initiates the binding of the virus to the sialylgalactose linkages of the receptors on the host cells. We herein demonstrate that a HA-binding peptide immobilized on a solid support is available to bind to HA and IFV. We previously obtained a HA-binding pentapeptide (Ala-Arg-Leu-Pro-Arg), which was identified by phage-display selection against HAs from random peptide libraries. This peptide binds to the receptor-binding site of HA by mimicking sialic acid. A peptide-conjugated lipid (pep-PE) was chemically synthesized from the peptide and a saturated phospholipid. A lipid bilayer composed of pep-PE and an unsaturated phospholipid (DOPC) was immobilized on a mica plate; and the interaction between HA and the pep-PE/DOPC membrane was investigated using atomic force microscopy. The binding of IFV to the pep-PE/DOPC membrane was detected by an enzyme-linked immunosorbent assay and real-time reverse transcription PCR. Our results indicate that peptide-conjugated lipids are a useful molecular device for the detection of HA and IFV.
All vehicles are cars: subclass preferences in container concepts

This paper investigates the natural bias humans display when labeling images with a container label like vehicle or carnivore. Using three container concepts as subtree root nodes, and all available concepts between these roots and the images from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset, we analyze the differences between the images labeled at these varying levels of abstraction and the union of their constituting leaf nodes. We find that for many container concepts, a strong preference for one or a few different constituting leaf nodes occurs. These results indicate that care is needed when using hierarchical knowledge in image classification: if the aim is to classify vehicles the way humans do, then cars and buses may be the only correct results.
OLAP Cube-based Graph Approach for Bibliographic Data

There is a growing number of di erent research elds that are concerned with bibliographic data analysis. In many cases, data of interest can be described as heterogeneous information networks. To explore knowledge from that networks in a multidimensinal way, OLAP (Online Analytical Processing) analysis helps users to access data from di erent points of views. The ability of OLAP for analyzing classical data is clear. However it must be adapted to provide networked data by considering both nodes and edges. In order to take into account linked data in OLAP on networks, we propose a conceptual graph model to represent bibliographic networks. Then we propose graphs enriched by cubes. Each node and edge of the considered network are described by a cube. It allows the user to quickly analyze the information summarized into cubes. Our proposal also solves the slowly changing dimension problem in OLAP analysis. To illustrate our approach, we integrate three bibliographic databases and a computation process is de ned according to user's needs. Then our implementation shows results on a real data.
Profiling Attacker Behavior Following SSH Compromises

This practical experience report presents the results of an experiment aimed at building a profile of attacker behavior following a remote compromise. For this experiment, we utilized four Linux honeypot computers running SSH with easily guessable passwords. During the course of our research, we also determined the most commonly attempted usernames and passwords, the average number of attempted logins per day, and the ratio of failed to successful attempts. To build a profile of attacker behavior, we looked for specific actions taken by the attacker and the order in which they occurred. These actions were: checking the configuration, changing the password, downloading a file, installing/running rogue code, and changing the system configuration.
Tempo Induction and Beat Tracking for Audio Signals

The process of beat tracking and tempo induction can be primarily defined in terms of the concepts of tempo and beat. Tempo refers to the rate at which a musical piece is played, and is represented in score time units per real time units (beat per minute being its most popular format). A beat can be defined as a unit in a sequence of impulses which represent the tempo of a musical piece. In fact, beat has no exclusive definition, and is dependant on the context and representation. The two properties of a beat are its period, which refers to the interval between consecutive beat occurances, and phase, which represents the difference between the time of the actual beat occurance and its position in the musical score. Based on those definitions, tempo induction can be viewed as a process of estimating basic tempo from musical data, and beat tracking can be defined as a process of extracting beat information from a musical score. Beat tracking can be differentiated from score following, another popular subject in the field of musical analysis, through the fact that score following uses a musical score as part of the process, and beat tracking does not. Some of the most popular applications of beat tracking and tempo induction include performance analysis, perceptual modeling, audio content analysis and transcription, as well as device synchronization during musical performance. There is a significant difference in the tempo induction and beat tracking process while working on MIDI and Audio musical data representations. Since MIDI, being in its nature a symbolical representation of musical information, already has the data used by the process directly encoded in itself, it does not require any preprocessing. In contrast, audio data does need to be put through preprocessing stages, during which symbolic information (eg. note onsets) is extracted from the audio stream. One of the earliest works in audio beat tracking was implemented by (Schloss 1985). During the preprocessing stage, the system detected note onsets as peaks in the slope of a high-pass filtered signal, and used some of the traditional techniques implemented for MIDI beat tracking from that point on. Another system was implemented (Allen and Dannenberg 1990), which introduced a concept of a beam search—instead of having an individual outcome of the beat detection process (which would not be able to recover from any error it might encounter), the work suggested using a …
Applicability of Well-Established Memristive Models for Simulations of Resistive Switching Devices

Highly accurate and predictive models of resistive switching devices are needed to enable future memory and logic design. Widely used is the memristive modeling approach considering resistive switches as dynamical systems. Here we introduce three evaluation criteria for memristor models, checking for plausibility of the I-V characteristics, the presence of a sufficiently nonlinearity of the switching kinetics, and the feasibility of predicting the behavior of two antiserially connected devices correctly. We analyzed two classes of models: the first class comprises common linear memristor models and the second class widely used nonlinear memristive models. The linear memristor models are based on Strukov's initial memristor model extended by different window functions, while the nonlinear models include Pickett's physics-based memristor model and models derived thereof. This study reveals lacking predictivity of the first class of models, independent of the applied window function. Only the physics-based model is able to fulfill most of the basic evaluation criteria.
Metadata and provenance management

Scientists today collect, analyze, and generate TeraBytes and PetaBytes of data. These data are often shared and further processed and analyzed among collaborators. In order to facilitate sharing and data interpretations, data need to carry with it metadata about how the data was collected or generated, and provenance information about how the data was processed. This chapter describes metadata and provenance in the context of the data lifecycle. It also gives an overview of the approaches to metadata and provenance management, followed by examples of how applications use metadata and provenance in their scientific processes.
A Prototype System for Computer Vision Based Human Computer Interaction

With the development of information technology in our society, we can expect that computer systems to a larger extent will be embedded into our environment. These environments will impose needs for new types of human-computerinteraction, with interfaces that are natural and easy to use. In particular, the ability to interact with computerized equipment without need for special external equipment is attractive. Today, the keyboard, the mouse and the remote control are used as the main interfaces for transferring information and commands to computerized equipment. In some applications involving three-dimensional information, such as visualization, computer games and control of robots, other interfaces based on trackballs, joysticks and datagloves are being used. In our daily life, however, we humans use our vision and hearing as main sources of information about our environment. Therefore, one may ask to what extent it would be possible to develop computerized equipment able to communicate with humans in a similar way, by understanding visual and auditive input. Perceptual interfaces based on speech have already started to nd a number of commercial and technical applications. For examples, systems are now available where speech commands can be use for dialing numbers in cellular
Do managerial ties in China always produce value? Competition, uncertainty, and domestic vs. foreign firms

While most advocate that foreign firms should utilize managerial ties to conduct business in China, recent literature cautions that such ties may offer only conditional value. This study examines three sources of heterogeneity that may condition the value of ties: firm ownership (foreign vs. domestic), competition, and structural uncertainty. Results from a survey of 280 firms in China indicate that though foreign and domestic firms utilize ties at a similar level, their performance gains from tie utilization differ. Managerial ties have a monotonic, positive effect on performance for domestic firms, whereas the effect is curvilinear (i.e., inverted U-shaped) for foreign firms. Therefore, compared with domestic firms, foreign firms have a competitive disadvantage from tie utilization. Furthermore, managerial ties are less effective for fostering performance when competition becomes more intense. However, ties lead to higher levels of firm performance when structural uncertainty increases. Overall, these results support the contingency view of managerial ties and caution companies about the unconditional use of ties as the market becomes more heterogeneous. Copyright © 2008 John Wiley & Sons, Ltd.
Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles

Computational breath analysis is a growing research area aiming at identifying volatile organic compounds (VOCs) in human breath to assist medical diagnostics of the next generation. While inexpensive and non-invasive bioanalytical technologies for metabolite detection in exhaled air and bacterial/fungal vapor exist and the first studies on the power of supervised machine learning methods for profiling of the resulting data were conducted, we lack methods to extract hidden data features emerging from confounding factors. Here, we present Carotta, a new cluster analysis framework dedicated to uncovering such hidden substructures by sophisticated unsupervised statistical learning methods. We study the power of transitivity clustering and hierarchical clustering to identify groups of VOCs with similar expression behavior over most patient breath samples and/or groups of patients with a similar VOC intensity pattern. This enables the discovery of dependencies between metabolites. On the one hand, this allows us to eliminate the effect of potential confounding factors hindering disease classification, such as smoking. On the other hand, we may also identify VOCs associated with disease subtypes or concomitant diseases. Carotta is an open source software with an intuitive graphical user interface promoting data handling, analysis and visualization. The back-end is designed to be modular, allowing for easy extensions with plugins in the future, such as new clustering methods and statistics. It does not require much prior knowledge or technical skills to operate. We demonstrate its power and applicability by means of one artificial dataset. We also apply Carotta exemplarily to a real-world example dataset on chronic obstructive pulmonary disease (COPD). While the artificial data are utilized as a proof of concept, we will demonstrate how Carotta finds candidate markers in our real dataset associated with confounders rather than the primary disease (COPD) and bronchial carcinoma (BC). Carotta is publicly available at http://carotta.compbio. sdu.dk [1].
Algorithm of Document Skew Detection Based on Character Vertices

We propose a new skew detection algorithm, which is based on the lowest pixels of a set of characters. The skew angle is evaluated by applying moment calculation, and the method to get characters in one text-line is described in detail. Document images of various languages, including English and Chinese, can be validly treated by this algorithm. Both the document images with large skew angle and the document images containing a few characters can be treated by this algorithm.
Multimodal Interest Level Estimation via Variational Bayesian Mixture of Robust CCA

This paper presents a method which estimates interest level while watching videos, based on collaborative use of facial expression and biological signals such as electroencephalogram (EEG) and electrocardiogram (ECG). To the best of our knowledge, no studies have been carried out on the collaborative use of facial expression and biological signals for estimating interest level. Since training data, which is used for estimating interest level, is generally small and imbalanced, Variational Bayesian Mixture of Robust Canonical Correlation Analysis (VBMRCCA) is newly applied to facial expression and biological signals, which are obtained from users while they are watching the videos. Unlike some related works, VBMRCCA is used to obtain the posterior distributions which represent the latent correlation between facial expression and biological signals in our method. Then, the users' interest level can be estimated by comparing the posterior distributions of the positive class data with those of the negative. Consequently, successful interest level estimation, via collaborative use of facial expression and biological signals, becomes feasible.
Finally, safely-extensible and efficient language-integrated query

Language-integrated query is an embedding of database queries into a host language to code queries at a higher level than the all-to-common concatenation of strings of SQL fragments. The eventually produced SQL is ensured to be well-formed and well-typed, and hence free from the embarrassing (security) problems. Language-integrated query takes advantage of the host language's functional and modular abstractions to compose and reuse queries and build query libraries. Furthermore, language-integrated query systems like T-LINQ generate efficient SQL, by applying a number of program transformations to the embedded query. Alas, the set of transformation rules is not designed to be extensible. We demonstrate a new technique of integrating database queries into a typed functional programming language, so to write well-typed, composable queries and execute them efficiently on any SQL back-end as well as on an in-memory noSQL store. A distinct feature of our framework is that both the query language as well as the transformation rules needed to generate efficient SQL are safely user-extensible, to account for many variations in the SQL back-ends, as well for domain-specific knowledge. The transformation rules are guaranteed to be type-preserving and hygienic by their very construction. They can be built from separately developed and reusable parts and arbitrarily composed into optimization pipelines. With this technique we have embedded into OCaml a relational query language that supports a very large subset of SQL including grouping and aggregation. Its types cover the complete set of intricate SQL behaviors.
Forecasting With a Forward-Looking DGE Model - Combining Long-Run Views of Financial Markets With Macro Forecasting

To develop forecasting procedures with a forward-looking dynamic general equilibrium model, we built a small New-Keynesian model and calibrated it to euro area data.It was essential in this context that we allowed for long-run growth in GDP.We brought additional asset price equations based on the expectations hypothesis and the Gordon growth model, into the standard open economy model, in order to extract information on private sector long-run expectations on fundamentals, and to combine that information into the macro economic forecast.We propose a method of transforming the model in forecasting use in such a way, as to match, in an economically meaningful way, the short-term forecast levels, especially of the model's jump-variables, to the parameters affecting the long-run trends of the key macroeconomic variables.More specifically, in the model we have used for illustrative purposes, we pinned down the long-run inflation expectations and domestic and foreign potential growth-rates using the model's steady state solution in combination with, by assumption, forward looking information in up-to-date financial market data.Consequently, our proposed solution preserves consistency with market expectations and results, as a favourable by-product, in forecast paths with no initial, first forecast period jumps.Furthermore, no ad hoc re-calibration is called for in the proposed forecasting procedures, which clearly is an advantage from point of view of transparency in communication.Key words: forecasting, New Keynesian model, DSGE model, rational expectations, open economy JEL classification numbers: E17, E30, E31, F41
Viral hijacking of a replicative helicase loader and its implications for helicase loading control and phage replication

Replisome assembly requires the loading of replicative hexameric helicases onto origins by AAA+ ATPases. How loader activity is appropriately controlled remains unclear. Here, we use structural and biochemical analyses to establish how an antimicrobial phage protein interferes with the function of the Staphylococcus aureus replicative helicase loader, DnaI. The viral protein binds to the loader’s AAA+ ATPase domain, allowing binding of the host replicative helicase but impeding loader self-assembly and ATPase activity. Close inspection of the complex highlights an unexpected locus for the binding of an interdomain linker element in DnaI/DnaC-family proteins. We find that the inhibitor protein is genetically coupled to a phage-encoded homolog of the bacterial helicase loader, which we show binds to the host helicase but not to the inhibitor itself. These findings establish a new approach by which viruses can hijack host replication processes and explain how loader activity is internally regulated to prevent aberrant auto-association. DOI: http://dx.doi. org/10.7554/eLife.14158. 001