Miracle-dz/UniFAR-FTUs huggingface dataset:
Isocitrate Dehydrogenase from Streptococcus mutans: Biochemical Properties and Evaluation of a Putative Phosphorylation Site at Ser102

Isocitrate deyhdrogenase (IDH) is a reversible enzyme in the tricarboxylic acid cycle that catalyzes the NAD(P)+-dependent oxidative decarboxylation of isocitrate to α-ketoglutarate (αKG) and the NAD(P)H/CO2-dependent reductive carboxylation of αKG to isocitrate. The IDH gene from Streptococcus mutans was fused with the icd gene promoter from Escherichia coli to initiate its expression in the glutamate auxotrophic strain E. coli Δicd::kanr of which the icd gene has been replaced by kanamycin resistance gene. The expression of S. mutans IDH (SmIDH) may restore the wild-type phenotype of the icd-defective strain on minimal medium without glutamate. The molecular weight of SmIDH was estimated to be 70 kDa by gel filtration chromatography, suggesting a homodimeric structure. SmIDH was divalent cation-dependent and Mn2+ was found to be the most effective cation. The optimal pH of SmIDH was 7.8 and the maximum activity was around 45°C. SmIDH was completely NAD+ dependent and its apparent K m for NAD+ was 137 μM. In order to evaluate the role of the putative phosphorylation site at Ser102 in catalysis, two “stably phosphorylated” mutants were constructed by converting Ser102 into Glu102 or Asp102 in SmIDH to mimick a constitutively phosphorylated state. Meanwhile, the functional roles of another four amino acids (threonine, glycine, alanine and tyrosine) containing variant size of side chains were investigated. The replacement of Asp102 or Glu102 totally inactivated the enzyme, while the S102T, S102G, S102A and S102Y mutants decreased the affinity to isocitrate and only retained 16.0%, 2.8%, 3.3% and 1.1% of the original activity, respectively. These results reveal that Ser102 plays important role in substrate binding and is required for the enzyme function. Also, Ser102 in SmIDH is a potential phosphorylation site, indicating that the ancient NAD-dependent IDHs might be the underlying origin of “phosphorylation mechanism” used by their bacterial NADP-dependent homologs.
Outside-in: An Alternative Approach to Forest Map Digitizing

ABSTRACT Abstract. This paper examines the problem of polygon digitizing, and suggests an inversion of the traditional approach for polygons of the environmental type, where each individual polygon, rather than its boundaries, is of primary interest. Many workers in geology, forestry, soil science, etc. have found the tedious specification of boundary arcs and their topological linkage and editing to be a major block to productivity. An alternative digitizing approach is described using Thiessen/Voronoi polygons to generate topology automatically and to reduce map digitizing times significantly. The digitizing of representative points within each polygon is performed using the GIS. The stand-alone Voronoi polygon building and boundary extraction module then generates linked arcs that are returned to the GIS for further analysis. Examples of the productivity enhancement in a production environment are given, along with the algorithms used.
Minimal coverage set detecting algorithm for barrier coverage in wireless sensor networks

Barrier coverage has attracted much attention in the past few years for its various applications, such as border surveillance and intrusion detection. In these applications, it is critical to achieve a strong barrier to ensure detecting any penetration of intruders in an energy-efficient manner so that the barrier can be covered with as few active sensors as possible. In this paper, we study the target detection problem through collaborative detecting under probabilistic sensing model, and propose a MCSD (minimal coverage set detecting) based on Graph theory, which seeks to achieve a strong barrier with minimal active sensors, prolonging the network lifetime. Finally, comprehensive simulations demonstrate that the proposed strategy improves network lifetime effectively.
The Insulin-Like Growth Factor 1 Receptor Is Essential for Axonal Regeneration in Adult Central Nervous System Neurons

Axonal regeneration is an essential condition to re-establish functional neuronal connections in the injured adult central nervous system (CNS), but efficient regrowth of severed axons has proven to be very difficult to achieve. Although significant progress has been made in identifying the intrinsic and extrinsic mechanisms involved, many aspects remain unresolved. Axonal development in embryonic CNS (hippocampus) requires the obligate activation of the insulin-like growth factor 1 receptor (IGF-1R). Based on known similarities between axonal growth in fetal compared to mature CNS, we decided to examine the expression of the IGF-1R, using an antibody to the βgc subunit or a polyclonal anti-peptide antibody directed to the IGF-R (C20), in an in vitro model of adult CNS axonal regeneration, namely retinal ganglion cells (RGC) derived from adult rat retinas. Expression of both βgc and the β subunit recognized by C20 antibody were low in freshly isolated adult RGC, but increased significantly after 4 days in vitro. As in embryonic axons, βgc was localised to distal regions and leading growth cones in RGC. IGF-1R-βgc co-localised with activated p85 involved in the phosphatidylinositol-3 kinase (PI3K) signaling pathway, upon stimulation with IGF-1. Blocking experiments using either an antibody which neutralises IGF-1R activation, shRNA designed against the IGF-1R sequence, or the PI3K pathway inhibitor LY294002, all significantly reduced axon regeneration from adult RGC in vitro (∼40% RGC possessed axons in controls vs 2–8% in the different blocking studies). Finally, co-transfection of RGC with shRNA to silence IGF-1R together with a vector containing a constitutively active form of downstream PI3K (p110), fully restored axonal outgrowth in vitro. Hence these data demonstrate that axonal regeneration in adult CNS neurons requires re-expression and activation of IGF-1R, and targeting this system may offer new therapeutic approaches to enhancing axonal regeneration following trauma.
Falkirk Wheel: Rollback Recovery for Dataflow Systems

We present a new model for rollback recovery in distributed dataflow systems. We explain existing rollback schemes by assigning a logical time to each event such as a message delivery. If some processors fail during an execution, the system rolls back by selecting a set of logical times for each processor. The effect of events at times within the set is retained or restored from saved state, while the effect of other events is undone and re-executed. We show that, by adopting different logical time "domains" at different processors, an application can adopt appropriate checkpointing schemes for different parts of its computation. We illustrate with an example of an application that combines batch processing with low-latency streaming updates. We show rules, and an algorithm, to determine a globally consistent state for rollback in a system that uses multiple logical time domains. We also introduce selective rollback at a processor, which can selectively preserve the effect of events at some logical times and not others, independent of the original order of execution of those events. Selective rollback permits new checkpointing policies that are particularly well suited to iterative streaming algorithms. We report on an implementation of our new framework in the context of the Naiad system.
Toward fast policy search for learning legged locomotion

Legged locomotion is one of the most versatile forms of mobility. However, despite the importance of legged locomotion and the large number of legged robotics studies, no biped or quadruped matches the agility and versatility of their biological counterparts to date. Approaches to designing controllers for legged locomotion systems are often based on either the assumption of perfectly known dynamics or mechanical designs that substantially reduce the dimensionality of the problem. The few existing approaches for learning controllers for legged systems either require exhaustive real-world data or they improve controllers only conservatively, leading to slow learning. We present a data-efficient approach to learning feedback controllers for legged locomotive systems, based on learned probabilistic forward models for generating walking policies. On a compass walker, we show that our approach allows for learning gait policies from very little data. Moreover, we analyze learned locomotion models of a biomechanically inspired biped. Our approach has the potential to scale to high-dimensional humanoid robots with little loss in efficiency.
Composed Bisimulation for Tree Automata FIT BUT Technical Report Series

We address the problem of reducing the size of (nondeterministic, bottom-up) tree automata (TA) using suitable, languagepreserving equivalences on the states of the automata. In particular, we propose the so-called composed bisimulation equivalence as a new language preserving equivalence. A composed bisimulation equivalence is defined in terms of two different relations, namely the upward and downward bisimulation equivalence. We provide simple and efficient algorithms for computing these relations. The notion of composed bisimulation equivalence is motivated by an attempt to obtain an equivalence that can provide better reductions than what currently known bisimulationbased approaches can offer, but which is not significantly more difficult to compute (and hence stays below the computational requirements of simulation-based reductions). The experimental results we present in the paper show that our composed bisimulation equivalence meets such requirements, and hence provides users of TA with a finer way to resolve the trade-off between the available degree of reduction and its cost.
How Team Efficacy Beliefs Impact Project Performance: An Empirical Investigation of Team Potency in Capital Projects in the Process Industries

Team efficacy beliefs show promise in enhancing team performance. Using a model-based quantitative research design, we investigated the antecedents and performance consequences of generalized team efficacy (potency) in a sample of 56 capital projects executed by 15 Fortune 500 companies in the process industries. Empirical analysis of our field survey identified that generalized team efficacy beliefs were positively associated with an objective measure of project cost performance. Regression analysis revealed that team competence, empowering leadership, and performance feedback all predicted generalized team efficacy beliefs. Tests of mediation revealed that generalized team efficacy fully mediated between these three inputs and project cost performance. Keywords—Team efficacy, Potency, Leadership, Feedback, Project cost.
Bayesian Networks (id Eqr089)

Bayesian networks are probabilistic models based on direct acyclic graphs. These models enable a direct representation of causal relations between variables. Their structure is ideal for combining prior knowledge, which often comes in causal form, and observed data. This article gives a short description on the concepts of this important class of models that became extremely popular in recent years.
Agent-based Exploration of Wirings of Biological Neural Networks: Position Paper

The understanding of human central nervous system depends on knowledge of its wiring. However, there are still gaps in our understanding of its wiring due to technical difficulties. While some information is coming out from human experiments, medical research is lacking of simulation models to put current findings together to obtain the global picture and to predict hypotheses to lead future experiments. Agent-based modeling and simulation (ABMS) is a strong candidate for the simulation model. In this position paper, we discuss the current status of "neural wiring" and "ABMS in biological systems". In particular, we discuss that the ABMS context provides features required for exploration of biological neural wiring.
Permutation estimation and minimax matching thresholds

The problem of matching two sets of features appears in various tasks of computer vision and can be often formalized as a problem of permutation estimation. We address this problem from a statistical point of view and provide a theoretical analysis of the accuracy of several natural estimators. To this end, the notion of the minimax matching threshold is introduced and its expression is obtained as a function of the sample size, noise level and dimensionality. We consider the cases of homoscedastic and heteroscedastic noise and carry out, in each case, upper bounds on the matching threshold of several estimators. This upper bounds are shown to be unimprovable in the homoscedastic setting. We also discuss the computational aspects of the estimators and provide empirical evidence of their consistency on synthetic data.
α‐Synuclein Accumulates in Purkinje Cells in Lewy Body Disease but not in Multiple System Atrophy

α-Synuclein has an important role in the pathogenesis of Parkinson disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA), comprising a new disease concept, that of α-synucleinopathies. Cerebellar degeneration with Purkinje cell depletion is present in the majority of MSA cases. By contrast, cerebellar pathology has not been demonstrated unequivocally in either PD or DLB. Recent immunohistochemical studies using anti-α-synuclein antibodies have shown that LB-type degeneration in PD and DLB is more widespread than previously recognized. To determine whether cerebellar Purkinje cells might be involved in α-synuclein pathology, we carried out immunohistochemical examinations of the cerebella of patients with PD (n = 10), DLB (n = 7), MSA (n = 10), Alzheimer disease and other tauopathies (n = 9), and age-matched control subjects (n = 10), using antibodies specific for α-synuclein. Although no abnormal accumulation of α-synuclein was noted in the Purkinje cell somata, numerous α-synuclein-positive, round inclusions were found in the cerebellar white matter in all the patients with PD and DLB. Immunohistochemical and ultrastructural examinations revealed that the majority of these inclusions was located in the Purkinje cell axons and consisted of granulo-filamentous structures. No such inclusions were observed in MSA, tauopathies, or controls. These findings indicate that Purkinje cells are also the victims of α-synuclein pathology in PD and DLB, but not in MSA.
User-Centric Plug-And-Play Functionality for IPv6-Enabled Wireless Sensor Networks

Smart nodes that sense the environment and communicate wirelessly to reach a sink node create wireless sensor networks. One of the main research challenges regarding wireless sensor networks is user deployment, namely in terms of configuration and management. On non-commercial solutions the user typically must be aware of the underlying technology to obtain sensing services. Internet connectivity is also desirable, so future deployments must take into consideration this feature, enabling realistic ubiquitous computing. This paper presents a user-centric solution for IPv6-enabled wireless sensor networks, using the Contiki operating system and Crossbow TelosB motes, featuring a Plug-and-Play like experience. One of the motes provides sink node capability to the network, through USB connection with a personal computer, which sends and receives data, presenting it to the user. A dedicated serial protocol for USB communication with the sink was developed and extensively debugged, featuring sink querying and network configuration. The current testbed uses User Datagram Protocol over IPv6, with 6LoWPAN, and IEEE 802.15. 4 wireless communication between the sensor network motes and the sink device. A Plug-and-Play like operation is achieved through zero-user configuration, since the user only needs to plug in the sink and give power to the remote motes.
A Framework for Malware Detection Using Combination Technique and Signature Generation

Malware detection must apply sophisticated technique to minimize malware thread that can break computer operation. Nowadays malware writers try to avoid detection by using several techniques such as polymorphic, hiding and also zero day of attack. However, commercial anti-virus or anti-spyware that used signature-based matching to detects malware cannot solve that kind of attack. In order to overcome this issue, we propose a new framework for malware detection that combines signature-based technique and genetic algorithm technique. This framework consists of three main components such as s-based detection, GA detection and signature generator. These three main components will work together as interrelated process in our propose framework. Result from this study is the new framework that design to solve new launce malware and also to generate signature automatically that can be used on signature-based detection.
Applying Social Network Analysis to the Information in CVS Repositories

The huge quantities of data available in the CVS repositories of large, long-lived libre (free, open source) software projects, and the many interrelationships among those data offer opportunities for extracting large amounts of valuable information about their structure, evolution and internal processes. Unfortunately, the sheer volume of that information renders it almost unusable without applying methodologies which highlight the relevant information for a given aspect of the project. In this paper, we propose the use of a well known set of methodologies (social network analysis) for characterizing libre software projects, their evolution over time and their internal structure. In addition, we show how we have applied such methodologies to real cases, and extract some preliminary conclusions from that experience.
Timing Intervals Using Population Synchrony and Spike Timing Dependent Plasticity

We present a computational model by which ensembles of regularly spiking neurons can encode different time intervals through synchronous firing. We show that a neuron responding to a large population of convergent inputs has the potential to learn to produce an appropriately-timed output via spike-time dependent plasticity. We explain why temporal variability of this population synchrony increases with increasing time intervals. We also show that the scalar property of timing and its violation at short intervals can be explained by the spike-wise accumulation of jitter in the inter-spike intervals of timing neurons. We explore how the challenge of encoding longer time intervals can be overcome and conclude that this may involve a switch to a different population of neurons with lower firing rate, with the added effect of producing an earlier bias in response. Experimental data on human timing performance show features in agreement with the model's output.
Extracting Generic Statements for the Semantic Web

Much of the natural language text found on the web contains various kinds of common sense knowledge and such information is potentially an important supplement to more formal approaches to building knowledge bases for Semantic Web applications. The common sense knowledge is often expressed in the form of generic statements such as “Elephants are mammals. ” The generic statement refers to a sentence that talks about kinds rather than individuals. In this thesis, we develop methods of automatically extracting generic statements from unrestricted natural language text and mapping them into an appropriate logical formalism for Semantic Web. The extraction process utilises cascaded transduction rules for identifying generic sentences and extracting relations from them. NLP pipeline and a suite of XML tools designed for generic manipulation of XML were used to carry out the series of tasks. The Wikipedia XML corpus was adopted for development, as a rich source of generic statements, and we used existing annotations of the ACE 2005 corpus for testing the identification of generic terms. For identifying generic terms, we use a set of morpho-syntactic features coded into definite transduction rules and apply the rules to the noun groups resulting from chunking. Next, relations are extracted with those identified terms as arguments. The semantic interpretation for the relation extraction was based on what can be called semantic chunking. Finally, we show how these extracted relations can be converted to RDF(S) statements as a knowledge representation for Semantic Web.
An unsupervised method to extract Chinese comment targets automatically

With the Internet developing, the quantity of comments accumulates rapidly, and sentiment analysis becomes a hot research task. A key preparation for sentiment analysis is to extract comment targets. In this paper an unsupervised method is proposed to judging the degree of their importance. We separate the method into two parts. One part uses explicit phrases' cooccurrence, calculating conditional entropy. We take nouns and adjectives into consideration, and make full use of their relevance. The other part mines the relationship between clusters and terms based on LDA result, using a modified PageRank method. The two algorithms are combined to rescore these candidate targets. In the end, we use two ways to test our LPCE (LDA, PageRank, and Condition Entropy) model's reliability, including word aspect and sentence aspect. Experiment results prove LPCE model is reasonable and effective.
Model-based PID autotuning enhanced by neural structural identification

This work presents an autotuning method for industrial PID controllers in the 1-dof ISA form. The major feature of the method is that the model structure employed for the process is selected on-line based on a step response record, by means of a multilayer perceptron neural network. Thanks to the exclusive use of normalized I/O data, the network can be trained off-line with simulated data, therefore simplifying the method's implementation. Once the model structure is selected and its parameters are identified, the IMC approach is used for synthesizing a regulator that is then approximated with a PID. Simulation and experimental results are reported to show the effectiveness of the proposed tuning method and its advantages with respect to IMC-based PID tuning with the model structure fixed a priori.
Sampling rate impact on energy consumption of biomedical signal processing systems

Long battery runtime is one of the most wanted properties of wearable sensor systems. The sampling rate has an high impact on the power consumption. However, defining a sufficient sampling rate, especially for cutting edge mobile sensors is difficult. Often, a high sampling rate, up to four times higher than necessary, is chosen as a precaution. Especially for biomedical sensor applications many contradictory recommendations exist, how to select the appropriate sample rate. They all are motivated from one point of view - the signal quality. In this paper we motivate to keep the sampling rate as low as possible. Therefore we reviewed common algorithms for biomedical signal processing. For each algorithm the number of operations depending on the data rate has been estimated. The Bachmann-Landau notation has been used to evaluate the computational complexity in dependency of the sampling rate. We found linear, logarithmic, quadratic and cubic dependencies.