Miracle-dz/UniFAR-FTUs huggingface dataset:
A Conserved Motif in the Linker Domain of STAT1 Transcription Factor Is Required for Both Recognition and Release from High-Affinity DNA-Binding Sites

Binding to specific palindromic sequences termed gamma-activated sites (GAS) is a hallmark of gene activation by members of the STAT (signal transducer and activator of transcription) family of cytokine-inducible transcription factors. However, the precise molecular mechanisms involved in the signal-dependent finding of target genes by STAT dimers have not yet been very well studied. In this study, we have characterized a sequence motif in the STAT1 linker domain which is highly conserved among the seven human STAT proteins and includes surface-exposed residues in close proximity to the bound DNA. Using site-directed mutagenesis, we have demonstrated that a lysine residue in position 567 of the full-length molecule is required for GAS recognition. The substitution of alanine for this residue completely abolished both binding to high-affinity GAS elements and transcriptional activation of endogenous target genes in cells stimulated with interferon-γ (IFNγ), while the time course of transient nuclear accumulation and tyrosine phosphorylation were virtually unchanged. In contrast, two glutamic acid residues (E559 and E563) on each monomer are important for the dissociation of dimeric STAT1 from DNA and, when mutated to alanine, result in elevated levels of tyrosine-phosphorylated STAT1 as well as prolonged IFNγ-stimulated nuclear accumulation. In conclusion, our data indicate that the kinetics of signal-dependent GAS binding is determined by an array of glutamic acid residues located at the interior surface of the STAT1 dimer. These negatively charged residues appear to align the long axis of the STAT1 dimer in a position perpendicular to the DNA, thereby facilitating the interaction between lysine 567 and the phosphodiester backbone of a bound GAS element, which is a prerequisite for transient gene induction.
A vascular intervention assisted robot based on master-slave control mode

The traditional vascular interventional surgery requires the surgeon to have high proficiency and skills to complete the surgery, also it may cause the surgeons and patients exposed in the radiation state for a long time and the stability and accuracy of guide wire and catheter are relatively lower. To overcome the drawbacks of traditional interventional surgery, a vascular intervention assisted robot was developed which was based on master-slave control mode to assist the surgeon to complete catheter and guide wire operations. To validate the performance of robot, relative experiments were performed on the silicon cardiovascular model. Experiments show that the developed vascular intervention assisted robot has good operating performance, could help surgeon to achieve the operation of catheter and guide wire steadily, furthermore, with the assist of the robot, the positioning accuracy of catheter and guide wire is improved.
A low phase noise CMOS quadrature VCO for 2.4 GHz bluetooth/WLAN applications

This paper presents a quadrature voltage-controlled oscillator (QVCO) based on the coupling of two LC-tank VCOs. The QVCO could be tuned between 2.41 and 2.64 GHz, and showed a phase noise of -139 dBc/Hz at 1 MHz offset frequency from the carrier across the whole tuning range, for a current consumption of 20 mA with 2.5-V power supply. A figure-of- merit of -190 dBc/Hz is observed.
A Systematic Review and Meta-Analysis of Randomized Trials and Prospective Studies Comparing Covered and Bare Self-Expandable Metal Stents for the Treatment of Malignant Obstruction in the Digestive Tract

Background: Self-expandable metal stents (SEMS) are widely used for the palliative treatment of malignant gastrointestinal obstruction. Our aim was to evaluate the evidence comparing covered and bare SEMS in the digestive tract using meta-analytical techniques. Methods: A literature search was performed using PubMed, Cochrane Library, and Embase databases for comparative studies assessing the two types of stents. The primary outcomes of interest were stent patency and patient survival; second outcomes included technical success, clinical success, tumor ingrowth, tumor overgrowth, and stent migration. A random-effects model was conducted. Pooled analysis was done separately based on the different segments of the digestive tract. Results: Eleven studies (8 randomized controlled trials and 3 prospective cohort studies) including a total of 1376 patients were identified. Covered SEMS were equivalent to bare SEMS in terms of technical success, clinical success, stent patency (gastroduodenal obstruction: HR =0.87, 95% CI 0.53-1.42; colorectal obstruction: HR =0.89, 95% CI 0.18-4.45; biliary obstruction: HR =0.73, 95% CI 0.41-1.32) and survival rates (esophageal obstruction: HR =1.80, 95% CI 0.73-4.44; gastroduodenal obstruction: HR =0.83, 95% CI 0.55-1.26; biliary obstruction: HR =0.99, 95% CI 0.77-1.28), although bare stents were more prone to tumor ingrowth (esophageal obstruction: RR =0.10, 95% CI 0.01-0.77; gastroduodenal obstruction: RR =0.12, 95% CI 0.03-0.55; colorectal obstruction: RR =0.21, 95% CI 0.06-0.70; biliary obstruction: RR =0.21, 95% CI 0.06-0.69), whereas covered stents had the higher risk of stent migration (gastroduodenal obstruction: RR =5.01, 95% CI 1.53-16.43; colorectal obstruction: RR =11.70, 95% CI 2.84-48.27; biliary obstruction: RR =8.11, 95% CI 1.47-44.76) and tumor overgrowth (biliary obstruction: RR =2.03, 95% CI 1.08-3.78). Conclusion: Both covered and bare SEMS are comparable in efficacy for the palliative treatment of malignant obstruction in the digestive tract. Each type of the stents has its own merit and demerit relatively.
Medical Image Feature , Extraction , Selection And Classification

Breast cancer is the most common type of cancer found in women. It is the most frequent form of cancer and one in 22 women in India is likely to suffer from breast cancer. This paper proposes a image classifier to classify the mammogram images. Mammogram image is classified into normal image, benign image and malignant image. Totally 26 features including histogram intensity features and GLCM features are extracted from mammogram image. A hybrid approach of feature selection is proposed in this paper which reduces 75% of the features. Decision tree algorithms are applied to mammography classification by using these reduced features. Experimental results have been obtained for a data set of 113 images taken from MIAS of different types. This technique of classification has not been attempted before and it reveals the potential of Data mining in medical treatment.
Segmentation of Pen Strokes Using Pen Speed

We present a technique for segmenting pen strokes into lines and arcs. The technique uses pen speed information to help infer the segmentation intended by the drawer. To begin, a set of candidate segment points is identified. This set includes speed minima below a threshold computed from the average pen speed. It also includes curvature maxima at which the pen speed is again below a threshold. The ink between each pair of consecutive segment points is then classified as either a line or arc, depending on which fits best. Finally, a feedback process is employed, and segments are judiciously merged and split as necessary to improve the quality of the segmentation. Formal user studies were conducted, and our system was observed to perform accurately, even for new users.
Increasing production and eliminating waste through lean tools and techniques for Halal food companies

Major businesses around the world have been trying to reduce the total cost and wastes across their supply chain to remain competitive in the expanding global market. Hence, a collection of reliable tools and techniques are vital for decreasing costs and wastes and for providing effective services for customer demands. Given that lean concept attempts to decrease costs and wastes, this study aims to identify the effective lean tools required for eliminating wastes in the supply chain. Because of the large number of consumers and the global market of Halal products, this study focuses on Halal food supply chains. Out of the questionnaires distributed to 300 Halal food firms in Malaysia, only 61 usable replies were obtained. The results showed that demand collaboration, continuous improvement, and inventory management practices are the most important tools in Lean Supply Chain (LSC) implementation. In addition, the results indicated that only a small percentage of Halal food companies are implementing LSC. Malaysia is an important Halal food producer in the word, but has low level of LSC implementation among its Halal food producers. LSC is a new concept that needs more effort to be understood and implemented.
Optimal Schemes for Discrete Distribution Estimation Under Locally Differential Privacy

We consider the minimax estimation problem of a discrete distribution with support size <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> under privacy constraints. A privatization scheme is applied to each raw sample independently, and we need to estimate the distribution of the raw samples from the privatized samples. A positive number <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> measures the privacy level of a privatization scheme. For a given <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>, we consider the problem of constructing optimal privatization schemes with <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-privacy level, i.e., schemes that minimize the expected estimation loss for the worst-case distribution. Two schemes known in the literature provide order optimal performance in the high privacy regime where <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> is very close to 0, and in the low privacy regime where <inline-formula> <tex-math notation="LaTeX">$e^{\epsilon }\approx k$ </tex-math></inline-formula>, respectively. In this paper, we propose a new family of schemes which substantially improve the performance of the existing schemes in the medium privacy regime when <inline-formula> <tex-math notation="LaTeX">$1\ll e^{\epsilon } \ll k$ </tex-math></inline-formula>. More concretely, we prove that when <inline-formula> <tex-math notation="LaTeX">$3.8 < \epsilon <\ln (k/9) $ </tex-math></inline-formula>, our schemes reduce the expected estimation loss by 50% under <inline-formula> <tex-math notation="LaTeX">$\ell _{2}^{2}$ </tex-math></inline-formula> metric and by 30% under <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> metric over the existing schemes. We also prove a lower bound for the region <inline-formula> <tex-math notation="LaTeX">$e^{\epsilon } \ll k$ </tex-math></inline-formula>, which implies that our schemes are order optimal in this regime.
End to End Digitisation and Analysis of Three-Dimensional Coral Models, from Communities to Corallites

Coral reefs hosts nearly 25% of all marine species and provide food sources for half a billion people worldwide while only a very small percentage have been surveyed. Advances in technology and processing along with affordable underwater cameras and Internet availability gives us the possibility to provide tools and softwares to survey entire coral reefs. Holistic ecological analyses of corals require not only the community view (10s to 100s of meters), but also the single colony analysis as well as corallite identification. As corals are three-dimensional, classical approaches to determine percent cover and structural complexity across spatial scales are inefficient, time-consuming and limited to experts. Here we propose an end-to-end approach to estimate these parameters using low-cost equipment (GoPro, Canon) and freeware (123D Catch, Meshmixer and Netfabb), allowing every community to participate in surveys and monitoring of their coral ecosystem. We demonstrate our approach on 9 species of underwater colonies in ranging size and morphology. 3D models of underwater colonies, fresh samples and bleached skeletons with high quality texture mapping and detailed topographic morphology were produced, and Surface Area and Volume measurements (parameters widely used for ecological and coral health studies) were calculated and analysed. Moreover, we integrated collected sample models with micro-photogrammetry models of individual corallites to aid identification and colony and polyp scale analysis.
A Multi-Agent Framework for Operation of a Smart Grid

This paper presents the operation of a Multi-agent system (MAS) for the control of a smart grid. The proposed Multi-agent system consists of seven types of agents: Single Smart Grid Controller (SGC), Load Agents (LAGs), a Wind Turbine Agent (WTAG), Photo-Voltaic Agents (PVAGs), a Micro-Hydro Turbine Agent (MHTAG), Diesel Agents (DGAGs) and a Battery Agent (BAG). In a smart grid LAGs act as consumers or buyers, WTAG, PVAGs, MHTAG & DGAGs acts as producers or sellers and BAG act as producer/consumer or seller/buyer. The paper demonstrates the use of a Multi-agent system to control the smart grid in a simulated environment. In order to validate the performance of the proposed system, it has been applied to a simple model system with different time zone i.e. day time and night time and when power is available from the grid and when there is power shedding. Simulation results show that the proposed Multi-agent system can perform the operation of the smart grid efficiently.
Dynamic Backoff for IEEE 802.15.4 Beaconless Networks

IEEE 802.15. 4 [1] is a leading standard for low power and low data rate wireless sensor networks. IEEE 802.15. 4 based wireless technology offers lower installation and maintenance costs and hence is increasingly replacing the existing wired technologies in applications such as building automation, home/environment monitoring, industrial control and smart metering. Deployed/future IEEE 802.15. 4 based networks may range from few nodes in a room to several thousand nodes spread sporadically or densely over a large geographical area [3]. The nodes may generate traffic infrequently or at a steady rate or in occasional bursts. The overall traffic load on an IEEE 802.15. 4 network may be fairly static or vary unpredictably over a wide range. Clearly, proper configuration is important for successful operation of an IEEE 802.15. 4 network in given operating conditions. In this paper, we examine the impact of macMinBE/ macMaxBE and macMaxCSMABackoffs parameters on the performance of beaconless operation of IEEE 802.15. 4 MAC layer under different traffic loads. The performance is measured in terms of the packet loss probability and the packet latency. We also develop a dynamic scheme to automatically configure the macMinBE, macMaxBE and macMaxCSMABackoffs parameters. Our dynamic scheme estimates the traffic load on the network through examination of packet loss rates as observed at a particular node. Based on the perceived network traffic load, maxMinBE, macMaxBE, and maxMaxCSMABackoffs are modified. We present data that shows how this dynamic algorithm outperforms the default standard configuration. The rest of the paper is organized as follows. Section 2 provides an overview of the packet transmission process in beaconless IEEE 802.15. 4 MAC operation as well as describes different collision scenarios. Section 3 describes the simulation setup as well as the network performance metrics used for this study. Sections 4 and 5 present simulation results analyzing the impact of macMinBE/macMaxBE and macMaxCSMABackofffs parameters respectively and make recommendations regarding suitable values for these parameters under different traffic loads. Section 6 presents a dynamic backoff algorithm and simulation results for the new algorithm. Section 7 presents a brief survey of previous work on configuring IEEE 802.15. 4 networks. Finally, Section 8 concludes the paper.
Holaaa!! writin like u talk is kewl but kinda hard 4 NLP

We present work in progress aiming to build tools for the normalization of User-Generated Content (UGC). As we will see, the task requires the revisiting of the initial steps of NLP processing, since UGC (micro-blog, blog, and, generally, Web 2.0 user texts) presents a number of non-standard communicative and linguistic characteristics, and is in fact much closer to oral and colloquial language than to edited text. We present and characterize a corpus of UGC text in Spanish from three different sources: Twitter, consumer reviews and blogs. We motivate the need for UGC text normalization by analyzing the problems found when processing this type of text through a conventional language processing pipeline, particularly in the tasks of lemmatization and morphosyntactic tagging, and finally we propose a strategy for automatically normalizing UGC using a selector of correct forms on top of a pre-existing spell-checker.
Non-rigid point set registration with multiple features

We present a new method for non-rigid registration with multiple features in this work. The proposed method is based on an alternating two-step process: correspondence estimation and transformation updating. We first define two vector features for measuring global and local structural differences between two point sets, respectively. We then combine the two features to build a multi-feature based energy function which provides a novel way to estimate correspondences by minimizing global or local feature differences using a linear assignment solution. To enhance the interaction between the two steps, we design an annealing scheme to gradually change the energy minimization from local to global feature differences and the thin plate spline transformation from rigid to non-rigid during the registration process. The registration results demonstrate that our method outperforms four state-of-the-art methods in most experiments.
Toward real-time indoor semantic segmentation using depth information

This work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on handcrafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. Using a frame by frame labeling, we obtain nearly state-of-the-art performance on the NYU-v2 depth dataset with an accuracy of 64.5%. We then show that the labeling can be further improved by exploiting the temporal consistency in the video sequence of the scene. To that goal, we present a method producing temporally consistent superpixels from a streaming video. Among the different methods producing superpixel segmentations of an image, the graph-based approach of Felzenszwalb and Huttenlocher is broadly employed. One of its interesting properties is that the regions are computed in a greedy manner in quasi-linear time by using a minimum spanning tree. In a framework exploiting minimum spanning trees all along, we propose an efficient video segmentation approach that computes temporally consistent pixels in a causal manner, filling the need for causal and real-time applications. We illustrate the labeling of indoor scenes in video sequences that could be processed in real-time using appropriate hardware such as an FPGA.
High accurate 3D reconstruction method using binocular stereo based on multiple constraints

In order to make an improvement of accuracy, completeness and robustness of the image based 3D reconstruction algorithm, we present a stereo matching approach under the condition of multi-resolution combined with multi-constraints to compute the 3D model of an object in this paper. In the first step, we build the image pyramid and stereo matching happens from the top level to the lowest level. In each level, we execute the following procedures: NCC matching, multi-constraints checking, re-matching and weighted median filter, disparity refinement. At last, we acquire an accurate and smooth disparity map in the bottom level. Combine with the calibration parameters, we can compute the 3D point cloud and use the Possion reconstruction method to generate the mesh model. The contributions of this paper are the proposal of choosing the pixel in the eight-neighborhood which has the highest match cost to guide the matching of center pixel during re-match, and design an anisotropic smoothing kernel to smooth the depth map and keep the detail at the same time in the disparity refinement. Experimental results show that the algorithm has the advantages of high precision, strong robustness and good model integrity.
Isolation and Characterization of Conditional-Lethal Mutations in the TUBl a-Tubulin Gene of the Yeast Saccharomyces cerevisiae

Microtubules in yeast are functional components of the mitotic and meiotic spindles and are essential for nuclear movement during cell division and mating. We have isolated 70 conditional-lethal mutations in the TUBl a-tubulin gene of the yeast Saccharomyces cerevisiae using a plasmid replacement technique. Of the 70 mutations isolated, 67 resulted in cold-sensitivity, one resulted in temperature-sensitivity, and two resulted in both. Fine-structure mapping revealed that the mutations were located throughout the TUBl gene. We characterized the phenotypes caused by 38 of the mutations after shifts of mutants to the nonpermissive temperature. Populations of temperature-shifted mutant cells contained an excess of large-budded cells with undivided nuclei, consistent with the previously determined role of microtubules in yeast mitosis. Several of the mutants arrested growth with a sufficiently uniform morphology to indicate that TUBI has at least one specific role in the progression of the yeast cell cycle. A number of the mutants had gross defects in microtubule assembly at the restrictive temperature, some with no microtubules and some with excess microtubules. Other mutants contained disorganized microtubules and nuclei. There were no obvious correlations between these phenotypes and the map positions of the mutations. Greater than 90% of the mutants examined were hypersensitive to the antimicrotubule drug benomyl. Mutations that suppressed the cold-sensitive phenotypes of two of the TUBl alleles occurred in TUBZ, the single structural gene I _ specifying @-tubulin.
Stock Price Forecasting by Hybrid Machine Learning Techniques

Networks (ANN) can provide relatively good performances in forecasting stock price but it cannot explain the forecasting rules clearly. On the other hand, a decision tree (DT) model can generate some rules to describe the forecasting decisions. This paper focuses on combining ANN and decision trees to create a stock price forecasting model. The experimental result shows that the combined DT+ANN model has 77% accuracy, which is higher than the single ANN and DT models over the electronic industry.
Approximation algorithms for sequencing problems

This thesis presents approximation algorithms for some sequencing problems, with an emphasis on vehicle routing. Vehicle Routing Problems (VRPs) form a rich class of variants of the basic Traveling Salesman Problem, that are also practically motivated. The VRPs considered in this thesis include single and multiple vehicle Dial a Ride, VRP with Stochastic Demands, Directed Orienteering and Directed Minimum Latency. Other sequencing problems studied in this thesis are Permutation Flowshop Scheduling and Maximum Quadratic Assignment.