Miracle-dz/UniFAR-FTUs huggingface dataset:
Relative and Absolute Quantification of Postsynaptic Density Proteome Isolated from Rat Forebrain and Cerebellum*S

The postsynaptic density (PSD) of central excitatory synapses is essential for postsynaptic signaling, and its components are heterogeneous among different neuronal subtypes and brain structures. Here we report large scale relative and absolute quantification of proteins in PSDs purified from adult rat forebrain and cerebellum. PSD protein profiles were determined using the cleavable ICAT strategy and LC-MS/MS. A total of 296 proteins were identified and quantified with 43 proteins exhibiting statistically significant abundance change between forebrain and cerebellum, indicating marked molecular heterogeneity of PSDs between different brain regions. Moreover we utilized absolute quantification strategy, in which synthetic isotope-labeled peptides were used as internal standards, to measure the molar abundance of 32 key PSD proteins in forebrain and cerebellum. These data confirm the abundance of calcium/calmodulin-dependent protein kinase II and PSD-95 and reveal unexpected stoichiometric ratios between glutamate receptors, scaffold proteins, and signaling molecules in the PSD. Our data also demonstrate that the absolute quantification method is well suited for targeted quantitative proteomic analysis. Overall this study delineates a crucial molecular difference between forebrain and cerebellar PSDs and provides a quantitative framework for measuring the molecular stoichiometry of the PSD.
Instrument identification in solo and ensemble music using Independent Subspace Analysis

We investigate the use of Independent Subspace Analysis (ISA) for instrument identification in musical recordings. We represent short-term log-power spectra of possibly polyphonic music as weighted non-linear combinations of typical note spectra plus background noise. These typical note spectra are learnt either on databases containing isolated notes or on solo recordings from different instruments. We show that this model has some theoretical advantages over methods based on Gaussian Mixture Models (GMM) or on linear ISA. Preliminary experiments with five instruments and test excerpts taken from commercial CDs give promising results. The performance on clean solo excerpts is comparable with existing methods and shows limited degradation under reverberant conditions. Applied to a difficult duo excerpt, the model is also able to identify the right pair of instruments and to provide an approximate transcription of the notes played by each instrument.
Stochastic Language Generation in a Dialogue System: Toward a Domain Independent Generator

Abstract : Until recently. surface generation in dialogue systems has served the purpose of simply providing a backend to other areas of research. The generation component of such systems usually consists of templates and canned text, providing inflexible, unnatural output. To make matters worse, the resources are typically specific to the domain in question and not portable to new tasks. In contrast, domain-independent generation systems typically require large grammars, full lexicons, complex collocational information, and much more. Furthermore, these frameworks have primarily been applied to text applications and it is not clear that the same systems could perform well in a dialogue application. This paper explores the feasibility of adapting such systems to create a domain-independent generation component useful for dialogue systems. It utilizes the domain independent semantic form of The Rochester Interactive Planning System (TRIPS) with a domain independent stochastic surface generation module. We show that a written text language model can be used to predict dialogue utterances from an over-generated word forest. We also present results from a human oriented evaluation in an emergency planning domain.
Artifacts in ECG-synchronized MDCT coronary angiography.

OBJECTIVE In MDCT coronary angiography, image artifacts are the major cause of false-positive and false-negative interpretations regarding the presence of coronary artery stenoses. Hence, it is important that observers reporting these investigations are aware of the potential presence of image artifacts and that these artifacts are recognized. CONCLUSION The article explores the technical causes for various artifacts in MDCT coronary angiography imaging and clinical examples are given.
Transferable Plasmids Mediating Multiple-Antibiotic Resistance in Streptococcus faecalis subsp. liquefaciens

In strain HK187 (Streptococcus faecalis subsp. liquefaciens) four plasmids were isolated. The molecular weights, as analyzed in neutral sucrose gradients and by electron microscopy, were found to be about 36 × 106, 31 × 106, 26 × 106, and 4 × 106. Plasmid 3, also designated pFK14, with a molecular weight of 26 × 106, was found to be responsible for resistance to chloramphenicol and erythromycin and also for high-level resistance to streptomycin and lincomycin. In mixed cultures these four resistances could be transferred with high frequency to a plasmid-free recipient, S. faecalis strain JH2-2.
Organic chemicals jeopardize the health of freshwater ecosystems on the continental scale

Significance Protection of freshwater ecosystems from organic pollutants is important to preserve biodiversity and the goods they provide to society, such as clean drinking water and recreation. Organic chemicals have been shown to adversely impact freshwater ecosystems in local and regional studies. Nevertheless, due to paucity of studies on larger spatial scales, it remains unknown how widespread the risk from organic chemicals is. For the first time, to our knowledge, we provide strong evidence that chemicals threaten the ecological integrity and consequently the biodiversity of almost half of the water bodies on a continental scale, based on the analysis of governmental monitoring data from 4,000 European sites. Due to limitations associated with the monitoring programs, our results are likely to underestimate the actual risks. Organic chemicals can contribute to local and regional losses of freshwater biodiversity and ecosystem services. However, their overall relevance regarding larger spatial scales remains unknown. Here, we present, to our knowledge, the first risk assessment of organic chemicals on the continental scale comprising 4,000 European monitoring sites. Organic chemicals were likely to exert acute lethal and chronic long-term effects on sensitive fish, invertebrate, or algae species in 14% and 42% of the sites, respectively. Of the 223 chemicals monitored, pesticides, tributyltin, polycyclic aromatic hydrocarbons, and brominated flame retardants were the major contributors to the chemical risk. Their presence was related to agricultural and urban areas in the upstream catchment. The risk of potential acute lethal and chronic long-term effects increased with the number of ecotoxicologically relevant chemicals analyzed at each site. As most monitoring programs considered in this study only included a subset of these chemicals, our assessment likely underestimates the actual risk. Increasing chemical risk was associated with deterioration in the quality status of fish and invertebrate communities. Our results clearly indicate that chemical pollution is a large-scale environmental problem and requires far-reaching, holistic mitigation measures to preserve and restore ecosystem health.
Structured Machine Learning for Mapping Natural Language to Spatial Ontologies (Gestructureerd machinaal leren voor het omzetten van natuurlijke taal naar ruimtelijke ontologieën)

We propose a novel structured learning framework for mapping natural language to spatial ontologies. The applied spatial ontology contains spatial concepts, relations and their multiple semantic types based on qualitative spatial calculi models. To achieve a tractable structured learning model, we propose an ecient inference approach based on constraint optimization techniques. Particularly, we decompose the inference to subproblems, each of which is solved using LP-relaxation. This is done for both training-time and prediction-time inference. In this framework ontology components are learnt while taking into account the ontological constraints and linguistic dependencies among components. Particularly, we for- mulate complex relationships such as composed-of, is-a and mutual exclusivity during learning while the previous structured learning models in similar tasks do not go beyond hierarchical relationships. Our experimental results show that jointly learning the output components considering the above mentioned constraints and relationships improves the results compared to ignoring these. The application of the proposed learning model for mapping to ontologies is not limited to extraction of spatial semantics, it could be used to populate any ontology. We argue therefore that this work is an important step towards automatically describing text with semantic labels that form a structured ontological representation of the content.
Intravenous Lorazepam in Neuroleptic‐Indueed Catatonia

Presented here are four cases of catatonic reactions which were felt to be neuroleptic induced. Intravenous lorazepam was rapidly effective in reversing the catatonia and attendant symptoms. Lorazepam's previous uses and pharmacological profile are discussed. Reviewed briefly are catatonia and its neuroleptic-induced forms. Possible mechanisms responsible for the therapeutic effect of intravenous lorazepam in these cases are then examined.
A Theory of Liquidity and Risk Management Based on the Inalienability of Risky Human Capital

We formulate a dynamic nancial contracting problem with risky inalienable human capital. We show that the inalienability of the entrepreneur’s risky human capital not only gives rise to endogenous liquidity limits but also calls for dynamic liquidity and risk management policies via standard securities that rms routinely pursue in practice, such as retained earnings, possible line of credit draw-downs, and hedging via futures and insurance contracts.
Scheduling with multiple voltages under resource constraints

This paper presents a low power design technique at the behavioral synthesis stage. Multiple voltages are used to run the functional units. The input to our problem is an unscheduled data flow graph with a timing constraint and a resource constraint. The resource constraint is given as the number and type of each of the functional units to be used and the supply voltage at which it would run. The goal is to maximize the number of operation nodes in the flow graph that are mapped to functional units running at the lower supply voltage while still satisfying the timing constraints. We propose two algorithms, one is a dynamic programing algorithm that gives optimal (i.e., minimum power consumption) results for the two-voltage problem; the second, is a heuristic geometric scheduling algorithm. The experimental results for some high-level synthesis benchmarks show 13% to 32% reduction in the power consumption.
Genetic algorithm assisted joint multiuser symbol detection and fading channel estimation for synchronous CDMA systems

A novel multiuser code division multiple access (CDMA) receiver based on genetic algorithms is considered, which jointly estimates the transmitted symbols and fading channel coefficients of all the users. Using exhaustive search, the maximum likelihood (ML) receiver in synchronous CDMA systems has a computational complexity that is exponentially increasing with the number of users and, hence, is not a viable detection solution. Genetic algorithms (GAs) are well known for their robustness in solving complex optimization problems. Based on the ML rule, GAs are developed in order to jointly estimate the users' channel impulse response coefficients as well as the differentially encoded transmitted bit sequences on the basis of the statistics provided by a bank of matched filters at the receiver. Using computer simulations, we showed that the proposed receiver can achieve a near-optimum bit-error-rate (BER) performance upon assuming perfect channel estimation at a significantly lower computational complexity than that required by the ML optimum multiuser detector. Furthermore, channel estimation can be performed jointly with symbol detection without incurring any additional computational complexity and without requiring training symbols. Hence, our proposed joint channel estimator and symbol detector is capable of offering a higher throughput and a shorter detection delay than that of explicitly trained CDMA multiuser detectors.
Learning about individuals' health from aggregate data

There is growing awareness that user-generated social media content contains valuable health-related information and is more convenient to collect than typical health data. For example, Twitter has been employed to predict aggregate-level outcomes, such as regional rates of diabetes and child poverty, and to identify individual cases of depression and food poisoning. Models which make aggregate-level inferences can be induced from aggregate data, and consequently are straightforward to build. In contrast, learning models that produce individual-level (IL) predictions, which are more informative, usually requires a large number of difficult-to-acquire labeled IL examples. This paper presents a new machine learning method which achieves the best of both worlds, enabling IL models to be learned from aggregate labels. The algorithm makes predictions by combining unsupervised feature extraction, aggregate-based modeling, and optimal integration of aggregate-level and IL information. Two case studies illustrate how to learn health-relevant IL prediction models using only aggregate labels, and show that these models perform as well as state-of-the-art models trained on hundreds or thousands of labeled individuals.
Comparison of error bounds for non-parametric dominant point detection

This paper compares three error bounds that can be used to make dominant point detection methods non-parametric. The error bounds are based on the error in slope estimation due to digitization. However, each bound is derived from a different approach. This results into different natures of the three methods and different values. The error bounds can be incorporated in non-parametric framework for dominant point detection. Here, the impact of these error bounds is studied in the context of the non-parametric version of the widely used RDP method of dominant point detection. It is seen that the digital error bound (the third error bound), which depends on both the length and the slope of the line segment, provides the most balanced dominant point detection results for a variety of curves. This analysis is useful for optimal choice of error bound or termination condition in dominant point detection methods.
Spatial Conservation of Ecological Landscape-Hydrologic Responses to Land Use Change in Datu Tableland

Datu Tableland is located at the land of Taichung Basin and Taichung’s Western Coastal Plain in central Taiwan. It is a large Urban Green Corridors with multi-functions of Ecological Service. It was suffered by flooding happened by human-made activities such as the development of scientific industrial park in recent years. This research collects data from different government agencies, including remote sensed data, 2009 digital land use data, soil data to establish the model of analyzing the changes of Ecological Service. Through analyzing and interpreting these data this study produce the layer of vegetation coverage and water storage capacity. At the same time, this study use arcgis database to manage and analyze the files efficiently. Dividing Datu Tableland to three subwatersheds makes the distribution of the changing of land use meaningful. It indicates landscape effects hydrological index and ecological vegetation coverage actually. This paper also estimates potential maximum retention amount in three subwatersheds. The total water storage capacity are estimated along urbanization and industrial development. The process of quantifying the impacts of LULC changes on hydrology at different scales including geomorphological and hydrological scales provides quantitative information for making decisions for land and water resource management. This paper also links landscape ecological thinking and hydrological thinking to ecological service analysis. The anticipated benefits for flood reduction by water storage facilities may be also received.
An adaptive totally ordered multicast protocol that tolerates partitions

In this paper we present a novel algorithm that implements a totally ordered multicast primitive for a Totally Ordered Group Communication Service (TO-GCS). TO-GCS is a powerful infrastructure for building distributed fault-tolerant applications, such as totally ordered broadcast, consistent object replication, distributed shared memory, Computer Supported Cooperative Work (CSCW) applications and distributed monitoring and display applications. Our algorithm is adaptive, i.e., it is able to dynamically alter the message delivery order in response to changes in the transmission rates of the participating processes. This compensates for differences among participant transmission rates and therefore minimizes fluctuations in message delivery latency. Our algorithm is thus useful for soft real-time environments where sharp fluctuations in message delivery latency are not acceptable. Our solution provides well-defined message ordering semantics. These semantics are preserved even in the face of site and communication link failures.
Who Controls the off Switch?

We're about to acquire a significant new cyber- vulnerability. The world's energy utilities are starting to install hundreds of millions of 'smart meters' which contain a remote off switch. Its main purpose is to ensure that customers who default on their payments can be switched remotely to a prepay tariff; secondary purposes include supporting interruptible tariffs and implementing rolling power cuts at times of supply shortage. The off switch creates information security problems of a kind, and on a scale, that the energy companies have not had to face before. From the viewpoint of a cyber attacker - whether a hostile government agency, a terrorist organisation or even a militant environmental group - the ideal attack on a target country is to interrupt its citizens' electricity supply. This is the cyber equivalent of a nuclear strike; when electricity stops, then pretty soon everything else does too. Until now, the only plausible ways to do that involved attacks on critical generation, transmission and distribution assets, which are increasingly well defended. Smart meters change the game. The combination of commands that will cause meters to interrupt the supply, of applets and software upgrades that run in the meters, and of cryptographic keys that are used to authenticate these commands and software changes, create a new strategic vulnerability, which we discuss in this paper.
Synergistic activity between Bacillus thuringiensis Cry6Aa and Cry55Aa toxins against Meloidogyne incognita

Plant‐parasitic nematodes are the most destructive group of plant pathogens worldwide and are extremely challenging to control. Some Bacillus thuringiensis crystal proteins are highly toxic to the plant‐parasitic nematode Meloidogyne incognita. In this study, the nematicidal crystal proteins Cry6Aa, Cry5Ba and Cry55Aa were tested against M. incognita to select the best toxin combination for its management. The results showed that a combination of Cry6Aa and Cry55Aa showed significant synergistic toxicity against M. incognita, and the highest synergistic effect (five times the expected toxicity of the two toxins calculated from their separate toxicities) was observed when they were combined in a 1:1 ratio. Furthermore, ligand blot analyses of the interaction between total proteins of M. incognita and the three toxins showed many different signal bands, indicating that there is a range of host proteins with which the toxins can interact. One explanation of the observed synergism is that the toxins damage the host in diverse ways, and they may thus act cooperatively and thereby show greater toxicity in combination. Our discovery provides an effective strategy for controlling M. incognita by using a combination of Cry6Aa and Cry55Aa.
Pointer and escape analysis for multithreaded programs

This paper presents a new combined pointer and escape analysis for multithreaded programs. The algorithm uses a new abstraction called parallel interaction graphs to analyze the interactions between threads and extract precise points-to, escape, and action ordering information for objects accessed by multiple threads. The analysis is compositional, analyzing each method or thread once to extract a parameterized analysis result that can be specialized for use in any context. It is also capable of analyzing programs that use the unstructured form of multithreading present in languages such as Java and standard threads packages such as POSIX threads. We have implemented the analysis in the MIT Flex compiler for Java and used the extracted information to 1) verify that programs correctly use region-based allocation constructs, 2) eliminate dynamic checks associated with the use of regions, and 3) eliminate unnecessary synchronization. Our experimental results show that analyzing the interactions between threads significantly increases the effectiveness of the region analysis and region check elimination, but has little effect for synchronization elimination.
Dental arches multi-projection system with semantic descriptions

This paper presents the development of an multi-projection stereoscopic dental arches application with semantic descriptions. The first section presents the concepts of the used technologies. Applications and examples are demonstrated. Finally, is presented the physical structure and the developed system, where a 3D dental arch is used as a model and can be viewed in multi-projection, thereby, providing greater user's immersion.
Early application identification

The automatic detection of applications associated with network traffic is an essential step for network security and traffic engineering. Unfortunately, simple port-based classification methods are not always efficient and systematic analysis of packet payloads is too slow. Most recent research proposals use flow statistics to classify traffic flows once they are finished, which limit their applicability for online classification. In this paper, we evaluate the feasibility of application identification at the beginning of a TCP connection. Based on an analysis of packet traces collected on eight different networks, we find that it is possible to distinguish the behavior of an application from the observation of the size and the direction of the first few packets of the TCP connection. We apply three techniques to cluster TCP connections: K-Means, Gaussian Mixture Model and spectral clustering. Resulting clusters are used together with assignment and labeling heuristics to design classifiers. We evaluate these classifiers on different packet traces. Our results show that the first four packets of a TCP connection are sufficient to classify known applications with an accuracy over 90% and to identify new applications as unknown with a probability of 60%.