Miracle-dz/UniFAR-FTUs huggingface dataset:
A middleware for collaborative research in experimental robotics

This paper presents the Robotics Service Bus (RSB), a new message-oriented, event-driven middleware based on a logically unified bus with hierarchical structure. Major goals for the development of RSB were openness and scalability in order to integrate diverse components in the context of robotics and intelligent systems. This includes the ability to operate on embedded platforms as well as desktop computers, reduction of framework lock-in, and the integration with other middlewares. We describe the design of the RSB middleware and explain how it meets requirements which lead to a scalable and open middleware concept. These requirements are based on several application scenarios which are used to verify the applicability of RSB. Furthermore, we relate RSB to other middlewares in the robotics domain.
Effect of Various Antiepileptic Drugs in Zebrafish PTZ-Seizure Model

Recently zebrafish larvae have emerged as a high-throughput model for screening pharmacological activities. The present study was undertaken to investigate the effect of established anticonvulsants, such as valproic acid, carbamazepine, gabapentin, diazepam, lacosamide and pregabalin against pentylenetetrazole (6 mM) seizures in adult zebrafish. Different phases of seizures (increase swim activity, rapid whirlpool-like circling swim behaviour and brief clonus-like seizures leading to loss of posture) were elicited in zebrafish on exposure for 15 min to 6 mM pentylenetetrazole. The exposure of zebrafish to an increasing concentration of the anticonvulsants alongside 6 mM pentylenetetrazole showed concentration-dependent elevation of seizure latency against pentylenetetrazole-induced seizures except for pregabalin, which failed to produce any anticonvulsant activity in zebrafish. Moreover the proconvulsant activity of caffeine was also evaluated using suboptimal concentration (4 mM) of pentylenetetrazole in adult zebrafish. Decrease in seizure latency of different phases of seizures was observed with increasing concentration of caffeine compared with its respective control group. In view of the above findings, the results of the present study suggested that adult zebrafish produce the expected anticonvulsive and proconvulsive effects and could potentially be used as a screen in future epilepsy research.
Conflict on a communication channel

Imagine that Alice wants to send a message m to Bob, and that Carol wants to prevent this. Assume there is a communication channel between Alice and Bob, but that Carol is capable of blocking this channel. Furthermore, there is a cost of S dollars to send on the channel, L dollars to listen on the channel and J to block the channel. How much will Alice and Bob need to spend in order to guarantee transmission of m? This problem abstracts many types of conflict in information networks including: jamming attacks in wireless networks and distributed denial-of-service (DDoS) attacks on the Internet, where the costs to Alice, Bob and Carol represent an expenditure of energy or network resources. The problem allows us to quantitatively analyze the economics of information exchange in an adversarial setting and ask: Is communication cheaper than censorship? We answer this question in the affirmative by showing that it is significantly more costly for Carol to block communication of m than for Alice to communicate it to Bob. Specifically, if S, L and J are fixed constants, and Carol spends a total of B dollars trying to block m, then Alice and Bob must spend only O(Bφ - 1 + 1)=O(B.62+1) dollars in expectation to transmit m, where φ = (1 + √5)/2 is the golden ratio. Surprisingly, this result holds even if (1) B is unknown to both Alice and Bob; (2) Carol knows the algorithms of Alice and Bob, but not their random bits; and (3) Carol has total knowledge of past actions of both players. Finally, we apply our work to two problems: (1) DoS attacks in wireless sensor networks and (2) application-level DDoS attacks in a wired client-server scenario. Our applications show how our results can provide an additional tool in mitigating such attacks.
Metabolic Impairment Induces Oxidative Stress, Compromises Inflammatory Responses, and Inactivates a Key Mitochondrial Enzyme in Microglia

Abstract: Microglial activation, oxidative stress, and dysfunctions in mitochondria, including the reduction of cytochrome oxidase activity, have been implicated in neurodegeneration. The current experiments tested the effects of reducing cytochrome oxidase activity on the ability of microglia to respond to inflammatory insults. Inhibition of cytochrome oxidase by azide reduced oxygen consumption and increased reactive oxygen species (ROS) production but did not affect cell viability. Azide also attenuated microglial activation, as measured by nitric oxide (NO*) production in response to lipopolysaccharide (LPS). It is surprising that the inhibition of cytochrome oxidase also diminished the activity of the α‐ketoglutarate dehydrogenase complex (KGDHC), a Krebs cycle enzyme. This reduction was exaggerated when the azide‐treated microglia were also treated with LPS. The combination of the azide‐stimulated ROS and LPS‐induced NO* would likely cause peroxynitrite formation in microglia. Thus, the possibility that KGDHC was inactivated by peroxynitrite was tested. Peroxynitrite inhibited the activity of isolated KGDHC, nitrated tyrosine residues of all three KGDHC subunits, and reduced immunoreactivity to antibodies against two KGDHC components. Thus, our data suggest that inhibition of the mitochondrial respiratory chain diminishes aerobic energy metabolism, interferes with microglial inflammatory responses, and compromises mitochondrial function, including KGDHC activity, which is vulnerable to NO* and peroxynitrite that result from microglial activation. Thus, activation of metabolically compromised microglia can further diminish their oxidative capacity, creating a deleterious spiral that may contribute to neurodegeneration.
ID-Match: A Hybrid Computer Vision and RFID System for Recognizing Individuals in Groups

Technologies that allow autonomous robots and computer systems to quickly recognize and interact with individuals in a group setting has the potential to enable a wide range of personalized experiences. However, existing solutions fail to both identify and locate individuals with enough speed to enable seamless interactions in very dynamic environments that require fast, implicit, non-intrusive, and ubiquitous recognition of users. In this work, we present a hybrid computer vision and RFID system that uses a novel reverse synthetic aperture technique to recover the relative motion paths of an RFID tags worn by people and correlate that to physical motion paths of individuals as measured with a 3D depth camera. Results show that our real-time system is capable of simultaneously recognizing and correctly assigning IDs to individuals within 4 seconds with 96.6% accuracy and groups of five people in 7 seconds with 95% accuracy. In order to test the effectiveness of this approach in realistic scenarios, groups of five participants play an interactive quiz game with an autonomous robot, resulting in an ID assignment accuracy of 93.3%.
A Neural Network Based Adaptive Algorithm for Multimedia Quality Fairness in WLAN Environments

This paper investigates multimedia quality fairness in wireless LAN environments where channel are error-prone due to mobility and fading. The experimental results show that using fixed MAC arguments for nodes in heterogeneous channel conditions leads to unequal throughput performance and that may incur the degradation of multimedia QoS. To overcome the unfairness problem for provisioning QoS, we propose a cross-layer adaptation scheme by on-line adapting the multidimensional MAC-layer backoff parameters depending on the application-layer QoS requirements and PHY-layer channel conditions. Our solution is based on an optimization approach which utilizes neural networks to learn the cross-layer function. Simulation results demonstrate that our adaptive scheme can tackle a variety of channel condition to provide fair throughput for nodes in heterogeneous channel conditions
Biomarker analysis from completely resected NSCLC patients enrolled in an adjuvant erlotinib clinical trial (RADIANT).

7520 Background: RADIANT is a phase III trial comparing erlotinib 150 mg daily vs. placebo (2:1) in stage IB-IIIA NSCLC patients with EGFR-positive tumors (IHC and/or FISH), following complete surgical resection. Planned accrual is 945 patients. Correlative biomarker studies on primary tumor tissue, whole blood, and serum are key components of the study. METHODS Tumor tissue was evaluated by IHC for protein expression of EGFR (PharmDx Kit, Dako with + ≥ 1% positive tumor cells), by FISH for gene expression of EGFR (Vysis, Abbott), and by Wave HS for EGFR and Kras mutations. RESULTS Among 655 tissue samples analyzed, 96% were EGFR IHC+ and 74% EGFR FISH+. Of the 476 tissue samples analyzed to date for mutations (mut), 12% had activating EGFR and 19% had Kras mut. Preliminary comparisons suggest that EGFR mut rate increased while Kras mut rate decreased with tumor stage. EGFR mut occurred in 29/178 (16%) females (F) vs. 23/271 (8%) males (M), 19/57 (33%) Asians (A) vs. 33/392 (8%) non-Asians (NA), and 29/66 (44%) never smokers (NS) vs. 23/383 (6%) current or former smokers (S). The differences between A vs. NA and NS vs. S were significantly different (P <0.0001) for all comparisons made. Kras mut occurred in 40/180 (22%) F vs. 48/280 (17%) M, 4/57 (7%) A vs. 84/403 (21%) NA, and 3/68 (4%) NS vs. 85/392 (22%) S. The difference between A vs. NA and NS vs. S were significantly different (P ≤0.0389) for all comparisons made. In adenoca, 51/254 (20%) had EGFR and 79/261 (30%) had Kras mut, and in SCC, 0/157 (0%) had EGFR and 3/162 (2%) had Kras mut. One tumor sample demonstrated both an activating EGFR and Kras mut. CONCLUSIONS Ongoing comprehensive biomarker assessment in the RADIANT population indicates successful sample collection and analysis. FISH positivity rates are greater than observed in late-stage tumors. Remaining results in this early-stage population are consistent with those observed in advanced-stage disease. Once complete, this study will provide a robust biomarker dataset on which to evaluate predictive and prognostic response markers, which may help determine who may benefit from treatment with erlotinib in the adjuvant setting. [Table: see text].
Chemical and Mechanical Removal of Staphylococcus Aureus from Orthodontic Retainers

Aim. Recent studies have indicated that non-oral microorganisms are able to colonise intraoral devices such as orthodontic retainers. The aim of this study was to evaluate the effectiveness of chemical and mechanical elimination of Staphylococcus aureus biofilms (MRSA) from thermoplastic discs which were used to represent orthodontic retainers. Methods and Results. Methicillin-resistant Staphylococcus aureus biofilms were grown on 5mm diameter thermoplastic copolyester discs in a Constant Depth Film Fermentor (CDFF). The efficacy of biofilm removal by Corsodyl, Steradent Active Plus, Endekay and Retainer Brite solutions were compared to a saline control against 1 and 2 day old biofilms. The discs containing the MRSA biofilms were exposed to each solution for 10, 30 and 60 minutes. All 4 antimicrobial agents were able to significantly reduce the numbers of viable organisms within the biofilm with Corsodyl showing the highest reduction in each case. Mechanical intervention was also investigated by using a commercially available sonicator. The combination of using both an antimicrobial agent and sonication showed no significant difference when compared tousing the antimicrobial agent alone. Conclusion. This study shows that clorhexidine based mouthwash is the best chemical agent at reducing biofilm on the vacuum formed retainer, however residual microorganism retention could lead to re-growth and recolonisation. Therefore, it is essential to look at other methods of cleaning such mechanical means to enhance the effect of chemical elimination of biofilm.
Phrase Structure Annotation and Parsing for Learner English

Learner English often contains grammatical errors with structural characteristics such as omissions, insertions, substitutions, and word order errors. These errors are not covered by the existing context-free grammar (CFG) rules. Therefore, it is not at all straightforward how to annotate learner English with phrase structures. Because of this limitation, there has been almost no work on phrase structure annotation for learner corpora despite its importance and usefulness. To address this issue, we propose a phrase structure annotation scheme for learner English, that consists of ﬁve principles. We apply the annotation scheme to two diﬀerent learner corpora and show (i) its eﬀectiveness at consistently annotating learner English with phrase structure (i.e., high inter-annotator agreement); (ii) the structural characteristics (CFG rules) of learner English obtained from the annotated corpora; and (iii) phrase structure parsing performance on learner English for the ﬁrst time. We also release the annotation guidelines, the annotated data, and the parser model to the public.
A Hybrid Machine Learning System for Stock Market Forecasting

In this paper, we propose a hybrid machine learning system based on Genetic Algorithm (GA) and Support Vector Machines (SVM) for stock market prediction. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators of highly correlated stocks, not only the stock to be predicted. The genetic algorithm is used to select the set of most informative input features from among all the technical indicators. The results show that the hybrid GA-SVM system outperforms the stand alone SVM system.
Application of the KINEROS2 site specific model to south-central NY and northeast PA : forecasting gaged and ungaged fast responding watersheds

Fast responding headwater river basins and small streams pose a significant threat to life and property throughout the eastern United States. This paper presents the results from the application of the real-time distributed model KINematic runoff and EROSion model (KINEROS2) to the complex terrain of the Binghamton County Warning Area. In operations, KINEROS2 uses the highest resolution radar data available, in both space and time, with the intent to improve flash flood warning lead time. KINEROS2 was originally developed for use in urban and semi-arid climates. This study demonstrates a proof of concept for applying KINEROS2 to a more humid regime. Key model parameters needed for manual calibration of peak flow generated by KINEROS2 in humid climates were identified. Saturated hydrologic conductivity of hillslopes had the greatest influence on the peak flow rate and channel length on the timing of the peak flow rate. It was found that optimal parameter values varied significantly between storms with respect to both basin average rainfall and maximum basin average rainfall intensity. This uncertainty in parameter estimation is addressed by running KINEROS2 with parameter sets that reflect the range obtained by calibration. An additional source of uncertainty is the selection of an appropriate Z-R relationship. KINEROS2 can be run for ungaged watersheds assuming some information can be collected on the timing and magnitude of several flow events. Running a distributed model on the small basin scale provides information on the magnitude and timing of a flash flood event which is not currently available using current NOAA/NWS flash flood forecasting methodologies. _______________________
Anti-progesterone monoclonal antibody affects early cleavage and implantation in the mouse by mechanisms that are influenced by genotype.

Pregnancy was blocked by anti-progesterone monoclonal antibody in two inbred (BALB/cJ, CBA/Ca) but to a lesser degree in an F1 hybrid (CBA/Ca male X BALB/cJ female) or an outbred (Tuck's no. 1) stock of mice when antibody was injected intraperitoneally (i.p.) at 32 h post coitum (p.c.) using a dosage of 9.5-10.9 nmol. This different antifertility effect could not be explained solely by altered tubal transport in inbred mice since the rate of transport was slightly accelerated in one stock (BALB/c) but not in another (CBA). In crossbred mice tubal transport was not significantly altered by antibody treatment. At Day 3 (54-58 h p.c.), the majority of embryos in control mice were at the 4-cell and 8-cell to morula stages in inbred and crossbred stock, respectively, but after antibody treatment they were mainly at the 4-cell stage in all 4 stocks. At Day 4 (78-82 h p.c.) the majority of embryos in control females had reached the blastocyst stage in all stocks, whereas after antibody treatment they had reached this stage in crossbred stock and relatively few had progressed so far in inbred stock. The results indicate that there are two events in early gestation which are susceptible to passive immunization with anti-progesterone monoclonal antibody. The first of these occurs during cleavage shortly after the 4-cell stage when embryo development was arrested in two inbred stocks of mice. Antibody effects on cleavage were not direct since embryos cultured in the presence of high concentrations of antibody, or antibody saturated with progesterone, continued to develop in the normal way and formed blastocysts. The second event is the onset of implantation, an effect also influenced by genotype. The decidual cell reaction induced by intraluminal oil injection was blocked by antibody injected at 8 or 32 h p.c. in BALB/c females, but only when injected at 8 h, and not at 32 h p.c. , in F1 hybrid females. The results show that there is a greater resistance in two crossbred stocks compared with two inbred stocks to the effects of passive immunization against progesterone in early pregnancy.
Deep Graphs - a general framework to represent and analyze heterogeneous complex systems across scales

Network theory has proven to be a powerful tool in describing and analyzing systems by modelling the relations between their constituent objects. Particularly in recent years, a great progress has been made by augmenting "traditional" network theory in order to account for the multiplex nature of many networks, multiple types of connections between objects, the time-evolution of networks, networks of networks and other intricacies. However, existing network representations still lack crucial features in order to serve as a general data analysis tool. These include, most importantly, an explicit association of information with possibly heterogeneous types of objects and relations, and a conclusive representation of the properties of groups of nodes as well as the interactions between such groups on different scales. In this paper, we introduce a collection of definitions resulting in a framework that, on the one hand, entails and unifies existing network representations (e.g., network of networks and multilayer networks), and on the other hand, generalizes and extends them by incorporating the above features. To implement these features, we first specify the nodes and edges of a finite graph as sets of properties (which are permitted to be arbitrary mathematical objects). Second, the mathematical concept of partition lattices is transferred to the network theory in order to demonstrate how partitioning the node and edge set of a graph into supernodes and superedges allows us to aggregate, compute, and allocate information on and between arbitrary groups of nodes. The derived partition lattice of a graph, which we denote by deep graph, constitutes a concise, yet comprehensive representation that enables the expression and analysis of heterogeneous properties, relations, and interactions on all scales of a complex system in a self-contained manner. Furthermore, to be able to utilize existing network-based methods and models, we derive different representations of multilayer networks from our framework and demonstrate the advantages of our representation. On the basis of the formal framework described here, we provide a rich, fully scalable (and self-explanatory) software package that integrates into the PyData ecosystem and offers interfaces to popular network packages, making it a powerful, general-purpose data analysis toolkit. We exemplify an application of deep graphs using a real world dataset, comprising 16 years of satellite-derived global precipitation measurements. We deduce a deep graph representation of these measurements in order to track and investigate local formations of spatio-temporal clusters of extreme precipitation events.
Capecitabine: fulfilling the promise of oral chemotherapy

Capecitabine is a synthetic oral fluoropyrimidine carbamate that is sequentially activated in a three-step process, which results in the preferential production of 5-fluorouracil in tumours, rather than in the normal surrounding tissue. Capecitabine is proven to be as effective as the combination of 5-fluorouracil and leucovorin administered on the Mayo clinic schedule in patients with metastatic colorectal cancer. It has also been proven to be effective in patients with metastatic breast cancer that has progressed despite prior anthracyclines and taxoids. More recently, it has also been shown to increase survival in combination with docetaxel in patients with metastatic breast cancer in comparison to docetaxel alone. This article reviews the pharmacology and clinical activity of capecitabine, as well as combinations of capecitabine with other chemotherapeutic agents and future directions of investigation with this convenient and widely active antitumour therapy.
Factors associated with depression and anxiety of hospitalized patients with heart failure.

INTRODUCTION Heart failure (HF) is a chronic disease associated with significant prevalence, mortality, and health care expenditure. The aim of the present study was to explore the factors associated with anxiety and depression experienced by patients hospitalized with HF. METHODS We evaluated 190 hospitalized HF patients in four public hospitals. The data were collected using a specific questionnaire, which, apart from demographic and clinical variables, included the Hospital Anxiety and Depression Scale (HADS). RESULTS In the total sample, 24.7% and 32.6% of patients were found to have moderate or high levels of anxiety, respectively. Simple multinomial logistic regression showed that no characteristic was significantly associated with anxiety. It also appeared that 17.4% of patients had minor and 24.2% major depression. Among the factors examined, longer disease duration was associated with a higher probability of being depressed (p=0.041). Moreover, single/divorced and widowed patients were more likely to have major depression compared to married patients (35.6% vs. 19.2%, p=0.046). Simple multinomial logistic regression revealed that married patients were 59% less likely to have major depression compared to their unmarried counterparts (OR: 0.41). Moreover, patients with short (<1 year) and moderate (2-5 years) disease duration were 69% (OR: 0.31) and 61% (OR: 0.39) less likely to have major depression compared to those with a long disease duration. Multiple multinomial logistic regression showed that only the disease duration remained statistically significantly associated with depression after controlling for the rest of factors found to be significant at a univariate level. CONCLUSIONS Nurses and physicians must take measures for the identification, assessment and management of anxiety and depression in this clinical population.