Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Precision Function Prediction using Conserved Interactions

The recent availability of large data sets of proteinprotein-interactions (PPIs) from various species offers new opportunities for functional genomics and proteomics. We describe a method for exploiting conserved and connected subgraphs (CCSs) in the PPI networks of multiple species for the prediction of protein function. Structural conservation is combined with functional conservation using a GeneOntologybased scoring scheme. We applied our method to the PPI networks of five species, i.e., E. coli, D. melanogaster, M. musculus, H. sapiens and S. cerevisiae. We detected surprisingly large CCSs for groups of three species but not beyond. A manual analysis of the biological coherence of exemplary subgraphs strongly supports a close relationship between structural and functional conservation. Based on this observation, we devised an algorithm for function prediction based on CCS. Using our method, for instance, we predict new functional annotations for human based on mouse proteins with a precision of 70%.
The Rocky 7 Mars rover prototype

This paper provides a system overview of a new Mars rover prototype, Rocky 7. We describe all system aspects: mechanical and electrical design, computer and software infrastructure, algorithms for navigation and manipulation, science data acquisition, and outdoor rover testing. In each area, the improved or added functionality is explained in a context of its path to flight, and need within the constraints of desired science missions.
Multiagent Adjustable Autonomy Framework (MAAF) for multi-robot, multi-human teams

This paper describes the ongoing development of a multiagent adjustable autonomy framework (MAAF) for multi-robot, multi-human teams performing tactical maneuvers. The challenge being addressed in this SBIR Phase I R&D project is how to exploit fully the unique capabilities of heterogeneous teams composed of a mixture of robots, agents or persons (RAPs): that is, how to improve the safety, efficiency, reliability and cost of achieving mission goals while maintaining dynamic adaptation to the unique limitations and contingencies of a real-world operating environment. Our response to this challenge is the creation of a new infrastructure that will facilitate cooperative and collaborative performance of human and robots as equal team partners through the application of advances in goal-oriented, multiagent planning and coordination technology. At the heart of our approach is the USC Teamcore Group's Machinetta, a state-of-the-art robot proxy framework with adjustable autonomy. Machinetta facilitates robot-human role allocation decisions and collaborative sharing of team tasks in the non-deterministic and unpredictable military environment through the use of a domain-independent teamwork model that supports flexible teamwork. This paper presents our innovative proxy architecture and its constituent algorithms, and also describes our initial demonstration of technical feasibility in a realistic simulation scenario.
Safety and Efficacy of Paliperidone Extended-Release in Acute and Maintenance Treatment of Schizophrenia

Paliperidone, the major active metabolite of risperidone, is a second-generation antipsychotic that has been developed as an extended-release (ER) tablet formulation that minimizes peak-trough fluctuations in plasma concentrations, allowing once-daily administration and constant drug delivery. Paliperidone ER has demonstrated efficacy in the reduction of acute schizophrenia symptoms in 6-week, placebo-controlled, double-blind trials and clinical benefits were maintained in the longer-term according to extension studies of up to 52 weeks in duration. Compared with quetiapine, paliperidone ER was associated with a more rapid symptom improvement. In addition, it was more effective than placebo in the prevention of symptom recurrence. Paliperidone ER is generally well tolerated with a predictable adverse event profile. Like risperidone, it is associated with a dose-dependent risk of extrapyramidal symptoms and prolactin elevation. Short-and longer-term studies have indicated a low liability for paliperidone ER to cause metabolic (ie, weight gain, hyperglycaemia and lipid dysregulation) or cardiovascular adverse effects. Available safety data from elderly patients appear to be promising. Due to negligible hepatic biotransformation, paliperidone ER is unlikely to be involved in clinically significant metabolic drug-drug interactions. Additional active comparator trials evaluating efficacy, tolerability and cost-effectiveness are required to better define the role of paliperidone ER in the treatment of schizophrenia in relation to other currently available second-generation antipsychotics, particularly risperidone.
Baculoviruses as Vectors for Gene Therapy against Human Prostate Cancer

Current curative strategies for prostate cancer are restricted to the primary tumour, and the effect of treatments to control metastatic disease is not sustained. Therefore, the application of gene therapy to prostate cancer is an attractive alternative. Baculoviruses are highly restricted insect viruses, which can enter, but not replicate in mammalian cells. Baculoviruses can incorporate large amounts of extra genetic material, and will express transgenes in mammalian cells when under the control of a mammalian or strong viral promoter. Successful gene delivery has been achieved both in vitro and in vivo and into both dividing and nondividing cells, which is important since prostate cancers divide relatively slowly. In addition, the envelope protein gp64 is sufficiently mutable to allow targeted transduction of particular cell types. In this review, the advantages of using baculoviruses for prostate cancer gene therapy are explored, and the mechanisms of viral entry and transgene expression are described.
Thinking objectively: management in the small

Managing the software engineering process is challenging under any circumstances. But are there significant differences between managing software development in small companies and large ones? The goals are broadly the same: delivering software as specified on time and free of defects. A written job description for a software engineering management position will likely describe the same duties and responsibilities whether it is in a large corporation or a small startup. And, on any given day the development group in a well-run startup might be indistinguishable from one in a well-run department of a large corporation. In reality most organizations of differing sizes are not run as well as they might be and show plenty of room for the kind of improvement that can be gained by skilled management. In this column, we argue that successful management in the small requires recognizing how a number of issues common to all companies present particular problems and opportunities in small companies.
A Benchmarking Platform for Analyzing Corpora of Traces: The Recognition of the Users' Involvement in Fields of Competencies

Nowadays more and more organisations use the collaborative environments, such as the social networks, to identify profiles of competencies, which are usually declared by the users themselves. We postulate that the analysis of the computer-supported collaborative activities may provide information about the users' competencies in specific domains. In this research work, we present a trace-based approach for dealing with the issue of the analysis of these activities. Our objective is to lay the foundations of an evaluation system, which is based on the exploitation of sharable digital traces by means of various analysis tools. We expect that the profiles of competencies may be enriched by taking into account the users' activities. We illustrate this research work through a case study: the recognition of users' involvement in a practical course of Java programming activity in order to define different working teams according to competencies of members.
CURRENT VERSION : May 21 , 2010 Why are Prices Sticky ? Evidence from Business Survey Data

This paper analyzes the price setting behavior of German retail and wholesale firms using data from a large panel of monthly business surveys from 1990-2006 with the aim to assess different price setting theories. The firm-level data allows matching changes in firms’ prices to several other idiosyncratic variables. Using an Ordered Probit specification, a menu cost model in the spirit of Cecchetti (1986) is estimated relating the price adjustment probability to both timeand statedependent variables. Evaluating the relative importance of the different theories is important due to their divergent implications concerning the real effect of monetary policy. First, results suggest an important role for state-dependence; macroeconomic variables characterizing the state of the economy show large and highly significant effects. Moreover, firmspecific factors such as changes in orders or the expected business development significantly change the repricing probability. Thus, purely time-dependent models are not supported by the results. Second, the data promote pricing models with intermediate inputs in which stickiness ”accumulates” through the production chain. The variability of input costs is among the most important determinants for the repricing probability. Moreover, retail prices adjust fast to changes in input costs suggesting a lack of additional stickiness at the retail level.
Biosynthesis of Natural Flavanones in Saccharomyces cerevisiae

ABSTRACT A four-step flavanone biosynthetic pathway was constructed and introduced into Saccharomyces cerevisiae. The recombinant yeast strain was fed with phenylpropanoid acids and produced the flavanones naringenin and pinocembrin 62 and 22 times more efficiently compared to previously reported recombinant prokaryotic strains. Microbial biosynthesis of the flavanone eriodictyol was also achieved.
Allogeneic clonal mesenchymal stem cell therapy for refractory graft-versus-host disease to standard treatment: a phase I study

Severe graft-versus-host disease (GVHD) is an often lethal complication of allogeneic hematopoietic stem cell transplantation (HSCT). The safety of clinical-grade mesenchymal stem cells (MSCs) has been validated, but mixed results have been obtained due to heterogeneity of the MSCs. In this phase I study, the safety of bone marrow-derived homogeneous clonal MSCs (cMSCs) isolated by a new subfractionation culturing method was evaluated. cMSCs were produced in a GMP facility and intravenously administered to patients who had refractory GVHD to standard treatment resulting after allogeneic HSCT for hematologic malignancies. After administration of a single dose (1×106 cells/kg), 11 patients were evaluated for cMSC treatment safety and efficacy. During the trial, nine patients had 85 total adverse events and the rate of serious adverse events was 27.3% (3/11 patients). The only one adverse drug reaction related to cMSC administration was grade 2 myalgia in one patient. Treatment response was observed in four patients: one with acute GVHD (partial response) and three with chronic GVHD. The other chronic patients maintained stable disease during the observation period. This study demonstrates single cMSC infusion to have an acceptable safety profile and promising efficacy, suggesting that we can proceed with the next stage of the clinical trial.
Efficient normalization based upon GPD [generalized probabilistic descent]

We propose a simple but powerful method for normalizing various sources of mismatch between training and testing conditions in speech recognizers, based on a training methodology called the generalized probabilistic descent method (GPD). In this new framework, a gradient based method is used to adapt the parameters of the feature extraction process in order to minimize the distortion between new speech data and existing classifier models, while most conventional normalization/adaptation methods attempt to adapt classification parameters. The GPD was proposed as a general discriminative training method for pattern recognizers such as neural networks. Up until now this has been used only for classifier design, sometimes in combination with the design of a non adaptive feature extractor. This paper, in contrast, studies the adaptive training benefits of GPD in the framework of normalizing the feature extractor to a new pattern environment. Experiments which use this technique to improve Japanese vowel classification were conducted and demonstrate the ability to reduce error rates by as much as 40%.
Bayesian Analysis in Data Cubes

In the last few years, there has been active research on aggregating advanced statistical measures in multi-dimensional data cubes [10] from partitioned subsets of data. In this paper, we propose a compression and aggregation scheme to support Bayesian estimations in data cubes based on the asymptotic properties of Bayesian statistics. The main application of the technique developed in this paper is data warehousing and the associated on-line analytical processing (OLAP) computing. OLAP allows for interactive analysis of multidimensional data to facilitate effective data mining at multiple levels of abstraction. Earlier work in data cubes [10] supports aggregation of simple measures such as sum() and average(). However, the fast development of OLAP technology has led to high demand for more sophisticated data analyzing capabilities, such as prediction, trend monitoring, and exception detection of multidimensional data. Oftentimes, existing simple measures such as sum() and average() become insufficient, and more sophisticated statistical models are desired to be supported in OLAP. Recently, some researchers developed aggregation schemes for more advanced statistical analysis including parametric models such as linear regression [6, 11] general multiple linear regression [5, 14] logistic regression analysis [23] and predictive filters [5], as well as nonparametric statistical models such as naive Bayesian classifiers [4] and linear discriminant analysis [15]. Along this line, we develop an aggregation scheme support Bayesian estimations in data cubes. Bayesian methods are statistical approaches to parameter estimation and statistical inference which use prior distributions over parameters. Bayesian methods have been successfully applied in many different fields such business, computer science, economics, epidemiology, genetics, imaging and political science. The premise of Bayesian statistics is to incorporate prior knowledge, along with a given set of
Ziegler-Nichols-Based Intelligent Fuzzy PID Controller Design for Antenna Tracking System

—This research is to augment the intelligent fuzzy controller with two kinds of traditional Ziegler-Nichols-based PID controller for a antenna tracking system design. The nonlinear parameter variation effect, such as gimbal angle hysteresis, is also taken into consideration. One can see that the system performances obtained by augmenting the intelligent fuzzy controller with Ziegler-Nichols-based method, especially the first type were quite better than the traditional ones.
A Graph Theoretic Model for Complex Network Failure Scenarios

If the network topology is modeled by an undirected graph and we consider link failures, then a natural graph theoretic measure of vulnerability is the size of a minimum cut , that is, the minimum number of edges that have to be removed to disconnect the graph, which is called the (edge-) connectivityof the graph. This number, at the same time, is the guaranteed number of edge disjoint paths between any given pair of nodes (by the classical theorem of Menger, see, e.g., [6]).
Cross-platform Interference: Locating wifi devices with ZigBee sensor networks

ZigBee is a popular short-range wireless communication protocol and has been widely adopted by wireless sensor networks, which runs on the same 2.4G band with WiFi. When wireless sensor networks with WiFi nodes run on overlapped bands, their signals could generate interferences with each other, and then wireless sensor networks could detect WiFi signal in this situation. In this paper, we propose an effective way to detect WiFi signal in wireless sensor networks and build signal attenuation model of WiFi signal to locate WiFi nodes. In our experiments, we set up wireless sensor network of 49 nodes to evaluate our methods. The evaluation shows that our WiFi signal detection and localization methods work and achieve relative high accuracy.
Quadratic Optimal Control of Wireless Sensor Network Deployment

This paper presents a method for an optimal wireless sensor network deployment with a quadratic cost function. It is assumed that the network is symmetric with an equal radio transmission range across the network. The optimization problem is formulated using a convex quadratic cost function that considers distances between mobile nodes and both static nodes and the target(s). The network constraints related to the network/graph connectivity conditions are presented. It is assumed that the base station can directly communicate with nodes that are to be deployed and with the mobile nodes. Simulation results are presented using a newly developed simulation platform for sensor network suboptimal deployment. Key words—wireless sensor networks, optimal control, deployment, coverage.
Stimulation of Nitric Oxide–cGMP Pathway Excites Striatal Cholinergic Interneurons via Protein Kinase G Activation

Conflicting data have been collected so far on the action of nitric oxide (NO) on cholinergic interneurons of the striatum. In the presentin vitro electrophysiological study, we reported that intracellularly recorded striatal cholinergic interneurons are excited by both hydroxylamine andS-nitroso-N-acetylpenicillamine, two NO donors. This excitation persisted unchanged in the presence of glutamate, dopamine, and substance P receptor antagonists as well as after blockade of tetrodotoxin (TTX)- and calcium channel-sensitive transmitter release, suggesting that NO produces its effects by modulating directly resting ion conductances in the somatodendritic region of striatal cholinergic cells. The depolarizing effect of hydroxylamine was greatly reduced by lowering external concentrations of sodium ions (from 126 to 38 mm) and did not reverse polarity in the voltage range from −120 to −40 mV. The sodium transporter blockers bepridil and 3′,4′-dichlorobenzamil were conversely ineffective in preventing NO-induced membrane depolarization. Intracellular cGMP elevation is required for the action of hydroxylamine on striatal cholinergic cells, as demonstrated by the findings that the membrane depolarization produced by this pharmacological agent was prevented by bath and intracellular application of two inhibitors of soluble guanylyl cyclase and was mimicked and occluded by zaprinast, a cGMP phosphodiesterase inhibitor. Finally, intracellular Rp-8-Br-cGMPS, a protein kinase G (PKG) inhibitor, blocked the hydroxylamine-induced membrane depolarization of cholinergic interneurons, whereas both okadaic acid and calyculin A, two protein phosphatase inhibitors, enhanced it, indicating that intracellular PKG and phosphatases oppositely regulate the sensitivity of striatal cholinergic interneurons to NO. The characterization of the cellular mechanisms involved in the regulation of striatal interneuron activity is a key step for the understanding of the role of these cells in striatal microcircuitry.
Coordination of Clusters for Inter-Cell Scheduling

Coordination of base stations is essential in the mitigation of inter-cell interference in cellular communication systems. One of practical solutions is a cluster-based intercell scheduling where base stations in each cluster determine their transmissions in cooperative fashion. In spite of potential performance improvement, the inter-cell scheduling may result in conflict between clusters. However, a dynamic coordination in the cluster level is difficult due to the prohibitive burden of intercluster communication. In this paper, we address the coordination of clusters in the downlink of cellular networks. We propose a rule of inter-cluster coordination, namely nominal scheduling order with penalty (NSOP) which does not require communication between the clusters. Dynamic system level simulations are performed to examine the performance of the NSOP in various interference scenarios. It is observed that the proposed NSOP effectively strikes a balance between the inter-cluster interference avoidance and the intra-cluster multi-user diversity by means of a simple penalty factor.
PerturbationRank : A Non-monotone Ranking Algorithm

We introduce a new approach for ranking Web pages to capture the extent to which the whole Web depends on an individual Web page. The importance of a Web page is measured by how much the Web changes when the page is disconnected from the Web. While there are potentially many useful ways to quantify the change, in this work we focus on the following: represent the state of the Web by the output of a known ranking algorithm, and measure the change by an appropriate metric on the state space. More specifically, we present PerturbationRank, as we have termed our class of ranking algorithms, in combination with PageRank and HITS. While both base algorithms, together with several others, view an incoming link as a positive endorsement and are in essence methods to account for the total endorsement, PerturbationRank represents a fundamental departure from this paradigm. As a consequence, it is able to capture usefulness signals missed previously. We evaluate the performance of the new algorithms in comparison with the base algorithms, following the methodology of Borodin et al. (ACM Transactions on Internet Technology, 5(1), 231–297, 2005). Our experiments demonstrate that the new and the base rankings are considerably correlated, have comparable effectiveness, and are at the same time substantially different. We conclude that PerturbationRank algorithms give alternative and useful rankings of the Web. Finally, we discuss how to improve the computation efficiency of PerturbationRank, as well as its other potential applications such as in network security and in combating spamdexing.
Argus: End-to-end service anomaly detection and localization from an ISP's point of view

Recent trends in the networked services industry (e.g., CDN, VPN, VoIP, IPTV) see Internet Service Providers (ISPs) leveraging their existing network connectivity to provide an end-to-end solution. Consequently, new opportunities are available to monitor and improve the end-to-end service quality by leveraging the information from inside the network. We propose a new approach to detect and localize end-to-end service quality issues in such ISP-managed networked services by utilizing traffic data passively monitored at the ISP side, the ISP network topology, routing tables and geographic information. This paper presents the design of a generic service quality monitoring system “Argus”. Argus has been successfully deployed in a tier-1 ISP to monitor millions of users of its CDN service and assist operators to detect and localize end-to-end service quality issues. This operational experience demonstrates that Argus is effective in accurate, quick detection and localization of important service quality issues.