Miracle-dz/UniFAR-FTUs huggingface dataset:
The Nutrient-Responsive Hormone CCHamide-2 Controls Growth by Regulating Insulin-like Peptides in the Brain of Drosophila melanogaster

The coordination of growth with nutritional status is essential for proper development and physiology. Nutritional information is mostly perceived by peripheral organs before being relayed to the brain, which modulates physiological responses. Hormonal signaling ensures this organ-to-organ communication, and the failure of endocrine regulation in humans can cause diseases including obesity and diabetes. In Drosophila melanogaster, the fat body (adipose tissue) has been suggested to play an important role in coupling growth with nutritional status. Here, we show that the peripheral tissue-derived peptide hormone CCHamide-2 (CCHa2) acts as a nutrient-dependent regulator of Drosophila insulin-like peptides (Dilps). A BAC-based transgenic reporter revealed strong expression of CCHa2 receptor (CCHa2-R) in insulin-producing cells (IPCs) in the brain. Calcium imaging of brain explants and IPC-specific CCHa2-R knockdown demonstrated that peripheral-tissue derived CCHa2 directly activates IPCs. Interestingly, genetic disruption of either CCHa2 or CCHa2-R caused almost identical defects in larval growth and developmental timing. Consistent with these phenotypes, the expression of dilp5, and the release of both Dilp2 and Dilp5, were severely reduced. Furthermore, transcription of CCHa2 is altered in response to nutritional levels, particularly of glucose. These findings demonstrate that CCHa2 and CCHa2-R form a direct link between peripheral tissues and the brain, and that this pathway is essential for the coordination of systemic growth with nutritional availability. A mammalian homologue of CCHa2-R, Bombesin receptor subtype-3 (Brs3), is an orphan receptor that is expressed in the islet β-cells; however, the role of Brs3 in insulin regulation remains elusive. Our genetic approach in Drosophila melanogaster provides the first evidence, to our knowledge, that bombesin receptor signaling with its endogenous ligand promotes insulin production.
Implementation of Particle Filters for Single Target Tracking Using CUDA

In order to implement Sequential Bayesian estimator using Monte carlo simulation and to get rid of limitations of Kalman filter, Particle filtering techniques plays a very crucial role for target tracking applications in state space where Importance sampling approximately distributed by posterior distribution with multimodel feature and robustness to noise. However as the particles becomes very large, the Monte Carlo representation becomes nearly equivalent to analytical description characterization for posterior distributions and has some deficiencies such as high computational cost and low sampling efficiency. Therefore, emerging computing platform, CUDA may be regarded as most appealing platform for such implementaion. Representation provided with set of samples for target distribution of state leads to increase in sampling efficiency so that graphics processing unit (GPUPU) becomes more appealing to use in Particle filter. The modification on mapping architecture are evaluated with qualitative analysis. The proposed design will be 3.5 times faster than direct design.
Identification of genes involved in low aminoglycoside-induced SOS response in Vibrio cholerae: a role for transcription stalling and Mfd helicase

Sub-inhibitory concentrations (sub-MIC) of antibiotics play a very important role in selection and development of resistances. Unlike Escherichia coli, Vibrio cholerae induces its SOS response in presence of sub-MIC aminoglycosides. A role for oxidized guanine residues was observed, but the mechanisms of this induction remained unclear. To select for V. cholerae mutants that do not induce low aminoglycoside-mediated SOS induction, we developed a genetic screen that renders induction of SOS lethal. We identified genes involved in this pathway using two strategies, inactivation by transposition and gene overexpression. Interestingly, we obtained mutants inactivated for the expression of proteins known to destabilize the RNA polymerase complex. Reconstruction of the corresponding mutants confirmed their specific involvement in induction of SOS by low aminoglycoside concentrations. We propose that DNA lesions formed on aminoglycoside treatment are repaired through the formation of single-stranded DNA intermediates, inducing SOS. Inactivation of functions that dislodge RNA polymerase leads to prolonged stalling on these lesions, which hampers SOS induction and repair and reduces viability under antibiotic stress. The importance of these mechanisms is illustrated by a reduction of aminoglycoside sub-MIC. Our results point to a central role for transcription blocking at DNA lesions in SOS induction, so far underestimated.
A Novel Probabilistic Model for Object Recognition and Pose Estimation

In this paper we consider the problem of object recognition and localization in a probabilistic framework. An object is represented by a parametric probability density, and the computation of pose parameters is implemented as a nonlinear parameter estimation problem. The presence of a probabilistic model allows for recognition according to Bayes rule. The introduced probabilistic model requires no prior segmentation but characterizes the statistical properties of observed intensity values in the image plane. A detailed discussion of the applied theoretical framework is followed by a concise experimental evaluation which demonstrates the benefit of the proposed approach.
An Atomic-Multicast Service for Scalable In-Memory Transaction Systems

Atomic Multicasts are central to the management of replicated data in distributed systems. Previous work has proven the effectiveness of utilising atomic multicasts, opposed to the classic two-phase commit, to coordinate transactions in in-memory databases. However, the current family of protocols utilised by such systems do not scale as the number of destinations increases. We propose that atomic multicasts should not occur between database nodes, instead transaction ordering should be exposed as a service that is provided by a dedicated set of nodes. Our performance study shows a clear improvement in transaction throughput and latency as the number of participants in a transaction increases.
Massive-Scale Experimentation of Location Based Video Streaming on Content Distribution Networks

We propose a GENI experiment to study the effectiveness of adjusting the caching policy on video streaming content distribution networks based on user behavior and location information derived from social networks. We plan to conduct a massive-scale experiment on GENI to study the feasibility of our approach using simulated, emulated and physical network entities. We also plan to conduct a long-term experiment to measure the practical aspects of our approach.
Saving the calculating time of the TCNN with nonchaotic simulated annealing strategy

The Transient Chaotic Neural Network (TCNN) and the Noisy Chaotic Neural Network(NCNN) have been proved their searching abilities for solving combinatorial optimization problems(COPs). The chaotic dynamics of the TCNN and the NCNN are believed to be important for their searching abilities. However, in this paper, we propose a strategy which cuts off the rich dynamics such as periodic and chaotic attractors in the TCNN and just utilizes the nonchaotic converge dynamics of the TCNN to save the time needed for computation. The strategy is named as nonchaotic simulated annealing (NCSA). Experiments on the traveling salesman problems exibit the effectiveness of NCSA. The NCSA saves over half of the time needed for the computation while maintaining the searching ability of the TCNN.
Hand motion detection from EMG signals by using ANN based classifier for human computer interaction

Today's advanced muscular sensing and processing technologies have made the acquisition of electromyography (EMG) signal which is valuable. EMG signal is the measurement of electrical potentials generated by muscle cells which is an indicator of muscle activity. Other than rehabilitation engineering and clinical applications, EMG signals can also be employed in the field of human computer interaction (HCI) system. In this work, the detection of different hand movements (left, right, up and down) was obtained using artificial neural network (ANN). A back-propagation (BP) network with Levenberg-Marquardt training algorithm was utilized. The conventional time and time-frequency based feature sets have been chosen to train the neural network. The simulation results show that the designed network is able to recognize hand movements with satisfied classification efficiency in average of 88.4%.
A Prominent Accretion Disk in the Low-Hard State of the Black Hole Candidate SWIFT J1753.5–0127

We report on simultaneous XMM-Newton and RXTE observations of the stellar mass black hole candidate SWIFT J1753.5-0127. The source was observed in the "low-hard" state, during the decline of a hard outburst. The inner accretion disk is commonly assumed to be radially truncated in the low-hard state, and it has been suggested that this property may be tied to the production of steady, compact jets. Fits to the X-ray spectra of SWIFT J1753.5-0127 with a number of simple models clearly reveal a cool (kT ≃ 0.2 keV) accretion disk. The disk component is required at more than the 8 σ level of confidence. Although estimates of inner disk radii based on continuum spectroscopy are subject to considerable uncertainty, fits with a number of models suggest that the disk is observed at or close to the innermost stable circular orbit. Recently, an observation of GX 339-4 revealed a disk extending to the innermost stable circular orbit at LX/LEdd ≃ 0.05; our results from SWIFT J1753.5-0127 may extend this finding down to LX/LEdd ≃ 0.003(d/8.5 kpc)2(M/10 M☉). We discuss our results within the context of low-luminosity accretion flow models and disk-jet connections.
Optimal Control and Guidance for Homing and Docking Tasks using an Autonomous Underwater Vehicle

This paper presents the results of a control and guidance strategy for homing and docking tasks using an autonomous underwater vehicle. An optimal high-order sliding mode control via state-dependent Riccati equation approach is introduced providing a robustness of motion control including elimination of chattering effect for decoupled systems of an AUV. Motion planning for a docking is introduced. The average vector field based on an artificial potential field method gives a desired trajectory using existing information from ocean network sensors. It provides a guidance for an AUV to follow the path to a required position with final desired orientation. A line-of-sight method is used for an AUV to follow the predefined path. In order to improve a docking manoeuver, a switched weight technique is proposed for controlling a vehicle's path and final stage docking.
LAYERED MULTIPLE DESCRIPTION CODING

Multiple description codes address the problem of unreliable channels by means of independent descriptions, while layered codes address the problems of heterogeneous client bandwidths and dynamic network congestion by means of sequences of layers. With the goal of achieving both robustness to unreliable channels and adaptivity to client bandwidth heterogeneity and network congestion, we explore constructions forlayered multiple description codes , where base layer descriptions can be transmitted to low bandwidth clients, while both base and enhancement layer descriptions can be transmitted to high bandwidth clients. The low bandwidth client quality is an increasing function of the number of base layer descriptions received, while the high bandwidth client quality is a bivariate increasing function of both the number of base layer descriptions and the number of enhancement-layer descriptions received. By optimizing the base layer descriptions for the low bandwidth client, in our construction, the high bandwidth client pays a penalty of 1.4 dB relative to a non-layered multiple description code optimized to the high bandwidth client.
Method to Enhance the Recognition Performance of an SVM Based on the Altered Datasets

The class label of each feature vector in the dataset is respectively added in the corresponding feature vector as a feature value, which build a new vector called altered feature vector, all of which compose the altered dataset. It is demostrated that an SVM based on the altered dataset has advantages such as high generilization performance and little structure risk, compared with an SVM based on the original dataset. When predicting the unkown feature vector, different class labels (1 and -1) are respectively added to the unkown feature vector, and 2 altered feature vectors are got. Two hyper plane function values are obtained by substituting the 2 altered feature vectors into the hyper plane function respectively, and the symol (1 or-1) of the function value with larger absolute value is conducted as the class label of the unkown feature vector. Experiments results show that the proposed mehtod can enhance the recognition performance of an SVM effectively.
Multidimensional data mining for healthcare service portfolio management

Data Mining is one of the most significant tools for discovering association patterns that are useful in for health services, Customer Relationship Management (CRM) etc. Yet, there are some drawbacks in conventional mining techniques. Since most of them perform the plain mining based on predefined schemata through the data warehouse as a whole, a re-scan must be done whenever new attributes are added. Secondly, an association rule may be true on a certain granularity but fail on a smaller one and vise verse. Last but not least, they are usually designed specifically to find either frequent or infrequent rules. With regard to healthcare service management, this research aims at providing a novel data schema and an algorithm to solve the aforementioned problems. A forest of concept taxonomies is used as the data structure for representing healthcare associations patterns that consist of concepts picked up from various taxonomies. Then, the mining process is formulated as a combination of finding the large itemsets, generating, updating and output the association patterns. Crucial mechanisms in each step will be clarified. At last, this paper presents experimental results regarding efficiency, scalability, information loss, etc. of the proposed approach to prove the advents of the approach.
A countermeasure for EM attack of a wireless PDA

Future wireless embedded systems will be increasingly powerful, supporting many more applications including one of the most crucial, security. Although many wireless devices offer more resistance to bus probing and power analysis attacks due to their compact size, susceptibility to electromagnetic (EM) attacks must be analyzed. This paper demonstrates, for the first time, a real EM attack on a PDA. A new low energy countermeasure and a new first order differential frequency analysis (DFA) is presented. Real energy measurements are also used to compare the energy overheads of different countermeasures. Results show that the low energy countermeasure thwarts first order differential analysis without large overheads of table regeneration or excessive storage. With the emergence of security applications in PDAs, cellphones, etc., low energy countermeasures for resistance to DFA are crucial for supporting future secure wireless embedded systems.
A Subset of Protective γ9δ2 T Cells Is Activated by Novel Mycobacterial Glycolipid Components

ABSTRACT γ9δ2 T cells provide a natural bridge between innate and adaptive immunity, rapidly and potently respond to pathogen infection in mucosal tissues, and are prominently induced by both tuberculosis (TB) infection and bacillus Calmette Guérin (BCG) vaccination. Mycobacterium-expanded γ9δ2 T cells represent only a subset of the phosphoantigen {isopentenyl pyrophosphate [IPP] and (E)-4-hydroxy-3-methyl-but-2-enylpyrophosphate [HMBPP]}-responsive γ9δ2 T cells, expressing an oligoclonal set of T cell receptor (TCR) sequences which more efficiently recognize and inhibit intracellular Mycobacterium tuberculosis infection. Based on this premise, we have been searching for M. tuberculosis antigens specifically capable of inducing a unique subset of mycobacterium-protective γ9δ2 T cells. Our screening strategy includes the identification of M. tuberculosis fractions that expand γ9δ2 T cells with biological functions capable of inhibiting intracellular mycobacterial replication. Chemical treatments of M. tuberculosis whole-cell lysates (MtbWL) ruled out protein, nucleic acid, and nonpolar lipids as the M. tuberculosis antigens inducing protective γ9δ2 T cells. Mild acid hydrolysis, which transforms complex carbohydrate to monomeric residues, abrogated the specific activity of M. tuberculosis whole-cell lysates, suggesting that a polysaccharide was required for biological activity. Extraction of MtbWL with chloroform-methanol-water (10:10:3) resulted in a polar lipid fraction with highly enriched specific activity; this activity was further enriched by silica gel chromatography. A combination of mass spectrometry and nuclear magnetic resonance analysis of bioactive fractions indicated that 6-O-methylglucose-containing lipopolysaccharides (mGLP) are predominant components present in this active fraction. These results have important implications for the development of new immunotherapeutic approaches for prevention and treatment of TB.
Presented at the Annual Conference 2009 of the British Educational Research Association, University of Manchester. Lesson and Learning Study: a globalizing form of teacher research.

The state of Teacher Research in the UK If a teacher wants to learn how to teach a particular topic what body of knowledge can s (he) turn to? For example, is there no body of professional scholarship on how to teach „fractions‟ or „rotation and revolution of the moon and the earth‟? It is now 40 years or more since Lawrence Stenhouse coined the idea of „the teacher as a researcher‟ who engaged in systematic self-study “through the study of the work of other teachers and through the testing of ideas by classroom research procedures. ” (1975, Ch 10) Many would claim that the idea is alive and well as a well established approach to school improvement and on postgraduate CPD programmes.
Teaming Up with Virtual Humans: How Other People Change Our Perceptions of and Behavior with Virtual Teammates

In this paper we present a study exploring whether the physical presence of another human changes how people perceive and behave with virtual teammates. We conducted a study (n = 69) in which nurses worked with a simulated health care team to prepare a patient for surgery. The agency of participants' teammates was varied between conditions; participants either worked with a virtual surgeon and a virtual anesthesiologist, a human confederate playing a surgeon and a virtual anesthesiologist, or a virtual surgeon and a human confederate playing an anesthesiologist. While participants perceived the human confederates to have more social presence (p <; 0.01), participants did not preferentially agree with their human team members. We also observed an interaction effect between agency and behavioral realism. Participants experienced less social presence from the virtual anesthesiologist, whose behavior was less in line with participants' expectations, when a human surgeon was present.
An Adaptive TCP Delayed Acknowledgment Strategy in Interaction with MAC Layer over Multi-hop Ad Hoc Networks

The acknowledgment strategy has great potential to increase the TCP throughput when it runs over 802.11 MAC protocol. In particular, TCP acknowledgments carry out an extensive number of medium accesses as they compete in the same route as data packets for media. As the load increases, the well-known hidden terminal effects caused by interference between ACK and data packets in MAC layer can degrade TCP performance dramatically if TCP acknowledges every incoming data packets. In this paper a dynamic TCP-MAC interaction strategy has been proposed which tries to reduce the number of induced ACKs by monitoring the channel condition. To this end, the total collision probability collected along the path from sender to receiver in MAC layer has been used to properly set the number of delayed ACKs (DA) in TCP. Based on the measured collision probability, TCP sender dynamically adjusts itself to the channel condition by delaying less ACKs in high traffics and more in low traffic conditions. The simulation results show a throughput improvement up to 15% over the existing method called dynamic adaptive acknowledgment (TCP-DAA) and much more over the regular TCP in the scenarios dealing with a dynamic loss rate.
A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

Flux balance analysis (FBA) in combination with the decomposition of metabolic networks into elementary modes has provided a route to modeling cellular metabolism. It is dependent, however, on the availability of external fluxes such as substrate uptake or growth rate before estimates can become available of intracellular fluxes. The framework classically does not allow modeling of metabolic regulation or the formulation of dynamic models except through dynamic measurement of external fluxes. The cybernetic modeling approach of Ramkrishna and coworkers provides a dynamic framework for modeling metabolic systems because of its focus on describing regulatory processes based on cybernetic arguments and hence has the capacity to describe both external and internal fluxes. In this article, we explore the alternative of developing hybrid models combining cybernetic models for the external fluxes with the flux balance approach for estimation of the internal fluxes. The approach has the merit of the simplicity of the early cybernetic models and hence computationally facile while also providing detailed information on intracellular fluxes. The hybrid model of this article is based on elementary mode decomposition of the metabolic network. The uptake rates for the various elementary modes are combined using global cybernetic variables based on maximizing substrate uptake rates. Estimation of intracellular metabolism is based on its stoichiometric coupling with the external fluxes under the assumption of (pseudo‐) steady state conditions. The set of parameters of the hybrid model was estimated with the aid of nonlinear optimization routine, by fitting simulations with dynamic experimental data on concentrations of biomass, substrate, and fermentation products. The hybrid model estimations were tested with FBA (based on measured substrate uptake rate) for two different metabolic networks (one is a reduced network which fixes ATP contribution to the biomass and maintenance requirement of ATP, and the other network is a more complex network which has a separate reaction for maintenance.) for the same experiment involving anaerobic growth of E. coli GJT001. The hybrid model estimated glucose consumption and all fermentation byproducts to better than 10%. The FBA makes similar estimations of fermentation products, however, with the exception of succinate. The simulation results show that the global cybernetic variables alone can regulate the metabolic reactions obtaining a very satisfactory fit to the measured fermentation byproducts. In view of the hybrid model's ability to predict biomass growth and fermentation byproducts of anaerobic E. coli GJT001, this reduced order model offers a computationally efficient alternative to more detailed models of metabolism and hence useful for the simulation of bioreactors.