Miracle-dz/UniFAR-FTUs huggingface dataset:
DNA Interaction and Phosphotransfer of the C4-Dicarboxylate- Responsive DcuS-DcuR Two-Component Regulatory System from Escherichia coli

ABSTRACT The DcuS-DcuR system of Escherichia coli is a two-component sensor-regulator that controls gene expression in response to external C4-dicarboxylates and citrate. The DcuS protein is particularly interesting since it contains two PAS domains, namely a periplasmic C4-dicarboxylate-sensing PAS domain (PASp) and a cytosolic PAS domain (PASc) of uncertain function. For a study of the role of the PASc domain, three different fragments of DcuS were overproduced and examined: they were PASc-kinase, PASc, and kinase. The two kinase-domain-containing fragments were autophosphorylated by [γ-32P]ATP. The rate was not affected by fumarate or succinate, supporting the role of the PASp domain in C4-dicarboxylate sensing. Both of the phosphorylated DcuS constructs were able to rapidly pass their phosphoryl groups to DcuR, and after phosphorylation, DcuR dephosphorylated rapidly. No prosthetic group or significant quantity of metal was found associated with either of the PASc-containing proteins. The DNA-binding specificity of DcuR was studied by use of the pure protein. It was found to be converted from a monomer to a dimer upon acetylphosphate treatment, and native polyacrylamide gel electrophoresis suggested that it can oligomerize. DcuR specifically bound to the promoters of the three known DcuSR-regulated genes (dctA, dcuB, and frdA), with apparent KDs of 6 to 32 μM for untreated DcuR and ≤1 to 2 μM for the acetylphosphate-treated form. The binding sites were located by DNase I footprinting, allowing a putative DcuR-binding motif [tandemly repeated (T/A)(A/T)(T/C)(A/T)AA sequences] to be identified. The DcuR-binding sites of the dcuB, dctA, and frdA genes were located 27, 94, and 86 bp, respectively, upstream of the corresponding +1 sites, and a new promoter was identified for dcuB that responds to DcuR.
Combining multi-wavelet and CNN for palmprint recognition against noise and misalignment

A palmprint recognition approach based on multi-wavelet and convolutional neural network (CNN) against noise and misalignment is given. CNN method has high robustness in biometrics, but a large number of training samples are necessary. Moreover, the gathered palmprint images should be cropped to obtain their region of interest (ROI) and noise pollution and misalignment are not well solved. Therefore, the original training database is augmented to reduce the effects of noise and misalignment. First, an original training palmprint image is split into five new images, and every new image is decomposed once by multi-wavelet. Three lower frequency bands in the low-frequency multi-wavelet component corresponding to pre-filters are extracted as three samples. Furthermore, the split image is downsampled as a new sample. Second, the CNN model is constructed based on the augmented database by experiments. Third, the softmax method is used to classify the test samples. At last, the final result is obtained from 20 results by using the voting method. The experimental results based on PolyU, CASIA, and IIT Delhi Touchless Palmprint Database palmprint databases show that the proposed method can effectively recognise palmprint with high robustness while there is noise and misalignment, and has a generalisation to other palmprint databases.
On the Enforcement of Quality of Service Policies for IPTV Service Offerings

This paper discusses both Quality of Service (QoS) information signaling and IPTV traffic engineering options that may serve as key components of IPTV service-specific QoS policies, which are designed to accommodate with customers' requirements and constraints (such as access capabilities) as well as ever-changing networking conditions that may possibly affect the level of quality associated to the delivery of such services.
What are the current limits of the Kinect sensor

The Kinect sensor offers new perspectives for the development and application of affordable, portable and easy-to-use markerless motion capture (MMC) technology. However, at the moment, accuracy of this device is still not known. In this study we compare results from Kinect (MMC) with those of a stereophotogrammetric system (marker based system [MBS]). 27 subjects performed a deep squatting motion. Parameters studied were segments lengths and joint angles. Results varied significantly depending on the joint or segment analysed. For segment length MMC shows poor results when subjects were performing movement. Differences were also found concerning joint angles, but regression equations were computed for each joint that produced the same results for MMC and MBS after correction.
Fuzzy Control of Inverted Robot Arm with Perturbed Time-Delay Affine Takagi-Sugeno Fuzzy Model

A stability analysis and controller synthesis methodology for an inverted robot arm system is proposed in this paper. This uncertain system is modeled by a state space Takagi-Sugeno (T-S) fuzzy model with linear nominal part and structure bounded parameter uncertainties in the state equations of each fuzzy rule. First, a sufficient condition on robust stability of the continuous perturbed time-delay affine T-S (CPTDATS) fuzzy models of inverted robot arm is proposed. Then, Hinfin-disturbance attenuation performance of the fuzzy models is analyzed. At last, a numerical example shows the use of the proposed approach on the stabilization and Hinfin-disturbance attenuation for the inverted robot arm systems.
A Ras-induced conformational switch in the Ras activator Son of sevenless

The Ras-specific guanine nucleotide-exchange factors Son of sevenless (Sos) and Ras guanine nucleotide-releasing factor 1 (RasGRF1) transduce extracellular stimuli into Ras activation by catalyzing the exchange of Ras-bound GDP for GTP. A truncated form of RasGRF1 containing only the core catalytic Cdc25 domain is sufficient for stimulating Ras nucleotide exchange, whereas the isolated Cdc25 domain of Sos is inactive. At a site distal to the catalytic site, nucleotide-bound Ras binds to Sos, making contacts with the Cdc25 domain and with a Ras exchanger motif (Rem) domain. This allosteric Ras binding stimulates nucleotide exchange by Sos, but the mechanism by which this stimulation occurs has not been defined. We present a crystal structure of the Rem and Cdc25 domains of Sos determined at 2.0-Å resolution in the absence of Ras. Differences between this structure and that of Sos bound to two Ras molecules show that allosteric activation of Sos by Ras occurs through a rotation of the Rem domain that is coupled to a rotation of a helical hairpin at the Sos catalytic site. This motion relieves steric occlusion of the catalytic site, allowing substrate Ras binding and nucleotide exchange. A structure of the isolated RasGRF1 Cdc25 domain determined at 2.2-Å resolution, combined with computational analyses, suggests that the Cdc25 domain of RasGRF1 is able to maintain an active conformation in isolation because the helical hairpin has strengthened interactions with the Cdc25 domain core. These results indicate that RasGRF1 lacks the allosteric activation switch that is crucial for Sos activity.
Microphase Separation: Enabling Isosorbide-Based Polycarbonates with Improved Property Profile

Microphase separation of bio-based soft blocks in a hard isosorbide polycarbonate enabled the preparation of a transparent bio-based engineering plastic with improved mechanical properties and processability at milder conditions. The ability to process these isosorbide-containing polycarbonates at lower temperatures in combination with a lower polymerization temperature due to the use of the activated bis(methyl salicyl) carbonate as the carbonate source avoided the undesired elimination of β-hydrogens, which is commonly observed in isosorbide-containing polymers. Preparation of a wide range of custom samples with varying combinations of soft blocks, followed by characterization and statistical analysis, enabled the identification of the correlations between composition and mechanical and thermal properties, resulting in an optimized engineering plastic with facile processing, transparency, and ductility combined with >84% renewable content.
Prevention of ARP spoofing: A probe packet based technique

In today's competitive world consideration of maintaining the security of information is must. Nowadays LAN is generally the most common and popular network domain. Network domains are always prone to number of network attacks. One of the most dangerous form of such network attacks is ARP Cache Poisoning also known as ARP Spoofing. ARP is a stateless protocol and ARP Spoofing takes place mainly because it lacks any mechanism of verifying the identity of sending host. It has been seen that most of the LAN attacks results from ARP Spoofing. So prevention, detection and mitigation of this problem can stop number of network attacks. ARP Spoofing is the act of vindictively changing the IP,MAC associations stored in ARP cache of any network host. In this paper we have proposed a probe based technique with an Enhanced Spoof Detection Engine (E- SDE) which not only detects ARP Spoofing but also identifies the genuine IP,MAC association. ARP and ICMP packets have been used as probe packets. Working of E-SDE is explained with the help of algorithm. We have also proposed an attacking model to clearly understand the incremental development of E-SDE to work effectively against most of the type of attackers. We have also measured the network traffic added by the proposed technique.
Approximation and streaming algorithms for histogram construction problems

Histograms and related synopsis structures are popular techniques for approximating data distributions. These have been successful in query optimization and a variety of applications, including approximate querying, similarity searching, and data mining, to name a few. Histograms were a few of the earliest synopsis structures proposed and continue to be used widely. The histogram construction problem is to construct the best histogram restricted to a space bound that reflects the data distribution most accurately under a given error measure.The histograms are used as quick and easy estimates. Thus, a slight loss of accuracy, compared to the optimal histogram under the given error measure, can be offset by fast histogram construction algorithms. A natural question arises in this context: Can we find a fast near optimal approximation algorithm for the histogram construction problem? In this article, we give the first linear time (1+ε)-factor approximation algorithms (for any ε > 0) for a large number of histogram construction problems including the use of piecewise small degree polynomials to approximate data, workloads, etc. Several of our algorithms extend to data streams.Using synthetic and real-life data sets, we demonstrate that in many scenarios the approximate histograms are almost identical to optimal histograms in quality and are significantly faster to construct.
Radio-Wave-Propagation Modeling in the Presence of Multiple Knife Edges by the Bidirectional Parabolic-Equation Method

Fast full-wave computation of fields is the main reason for a radio system planner to apply the parabolic-equation method (PEM) for radio-coverage prediction. In this paper, the capability of PEM for the determination of backward electromagnetic-field scattering by single and multiple knife edges is investigated. For the wave equation, a suitable solution is considered. Contrary to the available formulations for the PEM, the proposed method includes a backward-propagating term. For both the forward and backward terms, the split-step algorithm is employed. Similar to the terrain-masking method, the effect of edges is exerted by the Fresnel-Kirchhof approximation into the forward-propagating wave. For the backward field, a similar algorithm is derived. Then, an iterative marching algorithm is developed for modeling radio wave propagation over multiple knife edges. Numerical results are compared with those of Uniform Theory of Diffraction (UTD) and finite-difference time-domain methods for the single-knife-edge problem. In addition, the effect of the backward-propagating field on radio wave propagation is investigated. The effect of edge locations and heights on the backward scattered field and number of computational iterations is studied. It is seen that, for the single knife edge, the backward scattered field depends on the height and location of the edge. The intensity of the backscattered field is a monotonic increasing function of the edge height. In addition, by increasing the edge height, the beam width of the backward scattered field increases. In ranges far from the knife edge in front of it, the field spreads, and its amplitude decreases. For multiple knife edges, it is seen that, in addition to the single backward scattered field, radio-wave-propagation modeling requires multiple forward-backward terms between successive edges. The geometry of the problem determines the required number of terms for sufficient accuracy
TECHNOLOGY An Approach to Glaucoma using Image Segmentation Techniques

In wireless networks, mobile node frequently perfo rms handoff. The handoff may occur due to many factors like signal strength, load balancing, numbe r of connections, network status and frequencies en gaged etc. This frequent handoff may disturb the services and creat e few milliseconds of interruption. This delay and number of unwanted handoffs should be minimized for break less performance. Increasing in packet loss rates and heavy traffic will initiate incorrect handoff. In this paper we p roposed a numerical method to calculate the network status for avoiding unbeneficial handoffs and to eliminate unwanted traffic.
Decoupling capacitance allocation and its application topower-supply noise-aware floorplanning

We investigate the problem of decoupling capacitance (decap) allocation for power supply noise suppression at floorplan level. First, we assume that a floorplan is given and consider the decap placement as a postfloorplan step. Second, we consider the decap placement as an integral part of a floorplanning methodology (noise-aware floorplanning). In both cases, the objective is to minimize the floorplan area while suppressing the power supply noise below the specified limit. Experimental results on MCNC benchmark circuits show that, for postfloorplan decap placement, the white space allocated for decap is about 6%-9% of the chip area for the 0.25-/spl mu/m technology. The power-supply noise is kept below the specified limit. Compared to postfloorplan approach, the peak power-supply noise can be reduced by as much as 40% and the decap budget can be reduced by as much as 21% by using noise-aware floorplanning methodology. The total area is also reduced due to the reduced total decap budget gained from reduced power supply noise.
Preimaginal learning determines adult response to chemical stimuli in a parasitic wasp

The behavioural responses of parasitic wasps to chemical cues from their hosts and host plants are known to be affected by genetic and environmental components. In a previous study of the codling moth ectoparasitoid Hyssopus pallidus, we found that the response of adult parasitoids to the frass of their host caterpillars depended on a learning process involving plant cues. In the present study, we investigated how and when learning takes place. A series of experiments was conducted involving exposure of parasitoids to fruit cues at different developmental stages. While parasitoids were not able to learn the fruit cues in the adult stage, exposure to fruit odour at early preimaginal stages significantly increased the adult response to frass from fruit–fed caterpillars. The olfactory memory persisted through metamorphosis, with a retention time of 14 days. Preimaginal learning was not confined to fruit cues but was also demonstrated for a host– and fruit–independent cue, menthol. Parasitoids exposed to menthol odour at the egg and larval stages no longer showed negative responses as adults. Sensitization to fruit cues and habituation to menthol are considered to be the mechanisms involved. This study provides evidence of true preimaginal learning of olfactory cues in a parasitic wasp.
Analysis of drafting effects in swimming using computational fluid dynamics.

The purpose of this study was to determine the effect of drafting distance on the drag coefficient in swimming. A k-epsilon turbulent model was implemented in the commercial code Fluent(®) and applied to the fluid flow around two swimmers in a drafting situation. Numerical simulations were conducted for various distances between swimmers (0.5-8.0 m) and swimming velocities (1.6-2.0 m.s(-1)). Drag coefficient (Cd) was computed for each one of the distances and velocities. We found that the drag coefficient of the leading swimmer decreased as the flow velocity increased. The relative drag coefficient of the back swimmer was lower (about 56% of the leading swimmer) for the smallest inter-swimmer distance (0.5 m). This value increased progressively until the distance between swimmers reached 6.0 m, where the relative drag coefficient of the back swimmer was about 84% of the leading swimmer. The results indicated that the Cd of the back swimmer was equal to that of the leading swimmer at distances ranging from 6.45 to 8. 90 m. We conclude that these distances allow the swimmers to be in the same hydrodynamic conditions during training and competitions. Key pointsThe drag coefficient of the leading swimmer decreased as the flow velocity increased.The relative drag coefficient of the back swimmer was least (about 56% of the leading swimmer) for the smallest inter-swimmer distance (0.5 m). The drag coefficient values of both swimmers in drafting were equal to distances ranging between 6.45 m and 8.90 m, considering the different flow velocities.The numerical simulation techniques could be a good approach to enable the analysis of the fluid forces around objects in water, as it happens in swimming.
Universal outlying sequence detection for continuous observations

The following detection problem is studied, in which there are M sequences of samples out of which one outlier sequence needs to be detected. Each typical sequence contains n independent and identically distributed (i.i.d.) continuous observations from a known distribution π, and the outlier sequence contains n i.i. d. observations from an outlier distribution μ, which is distinct from n, but otherwise unknown. A universal test based on Kullback-Leibler (KL) divergence is built to approximate the maximum likelihood test, with known π and unknown μ. A KL divergence estimator based on data-dependent partitions is employed, and is shown to converge to its true value exponentially fast when the density ratio satisfies 0 <; Kl ≤ dμ/dπ ≤ K2, where K1 and K2 are positive constants. The performance of such a KL divergence estimator further implies that the outlier detection test is exponentially consistent. The detection performance of the KL divergence based test is compared with that of a recently introduced test for this problem based on the machine learning approach of maximum mean discrepancy (MMD). Regimes in which the KL divergence based test is better than the MMD based test are identified.
Generating feasible trajectories for autonomous on-orbit grasping of spinning debris in a useful time

The grasping and stabilization of a spinning, noncooperative target satellite by means of a free-flying robot is addressed. A method for computing feasible robot trajectories for grasping a target with known geometry in a useful time is presented, based on nonlinear optimization and a look-up table. An off-line computation provides a data base for a mapping between a four-dimensional input space, to characterize the target motion, and an N-dimensional output space, representing the family of time-parameterized optimal robot trajectories. Simulation results show the effectiveness of the data base for computing grasping maneuvers in a useful time, for a sample range of spinning motions. The debris object consists of a satellite with solar appendages in Low Earth Orbit, which presents collision avoidance and timing challenges for executing the task.
National Center for Ecological Analysis and Synthesis 2004 Report to the National Science Foundation

Each year, North American monarch butterflies undergo a spectacular two-way migration from breeding locations in Canada and the US to overwintering sites in Mexico. Throughout their annual cycle, monarchs utilize habitats in three different countries and require strikingly different resources and habitats at each life stage. Like other migratory animals, this shifting spatial distribution poses challenges for identifying key determinants of monarch population dynamics and assessing their conservation status. Monarchs are an incredibly popular insect that has been exceptionally well studied; multiple long-term monitoring programs exist within North America that span timescales of 3 to over 30 years. This virtually unprecedented wealth of data on a single animal species represents a rare scientific resource for understanding how natural and anthropogenic factors affect the population dynamics and movement patterns of migratory species. At the same time, a great need exists to integrate existing data sets for analysis and interpretation of both within-season and longer term population trends. Our proposed working group includes a team of experts in monarch ecology, migration biology, statistical model building, climate modeling and geography to answer the most fundamental questions of monarch biology throughout their range in North America. We will explore data from throughout the monarchs‘ annual life cycle to identify major ecological mechanisms that shape large-scale patterns of abundance and movement, and to predict the consequences of human activities, including shifting agricultural practices, deforestation and climate change, for long-term dynamics. We will also work directly with NCEAS informatics staff to develop a web-based portal that allows public access to and use of monarch butterfly observational data, much of which has been
Identification of a Core miRNA-Pathway Regulatory Network in Glioma by Therapeutically Targeting miR-181d, miR-21, miR-23b, β-Catenin, CBP, and STAT3

The application of microRNAs (miRNAs) in the therapeutics of glioma and other human diseases is an area of intense interest. However, it’s still a great challenge to interpret the functional consequences of using miRNAs in glioma therapy. Here, we examined paired deep sequencing expression profiles of miRNAs and mRNAs from human glioma cell lines after manipulating the levels of miRNAs miR-181d, -21, and -23b, as well as transcriptional regulators β-catenin, CBP, and STAT3. An integrated approach was used to identify functional miRNA-pathway regulatory networks (MPRNs) responding to each manipulation. MiRNAs were identified to regulate glioma related biological pathways collaboratively after manipulating the level of either post-transcriptional or transcriptional regulators, and functional synergy and crosstalk was observed between different MPRNs. MPRNs responsive to multiple interventions were found to occupy central positions in the comprehensive MPRN (cMPRN) generated by integrating all the six MPRNs. Finally, we identified a core module comprising 14 miRNAs and five pathways that could predict the survival of glioma patients and represent potential targets for glioma therapy. Our results provided novel insight into miRNA regulatory mechanisms implicated in therapeutic interventions and could offer more inspiration to miRNA-based glioma therapy.