Unknown Region Research Articles

Torsin ATPases influence chromatin interaction of the Torsin regulator LAP1.

The inner nuclear membrane is functionalized by diverse transmembrane proteins that associate with nuclear lamins and/or chromatin. When cells enter mitosis, membrane-chromatin contacts must be broken to allow for proper chromosome segregation; yet how this occurs remains ill-understood. Unexpectedly, we observed that an imbalance in the levels of the lamina-associated polypeptide 1 (LAP1), an activator of ER-resident Torsin AAA+-ATPases, causes a failure in membrane removal from mitotic chromatin, accompanied by chromosome segregation errors and changes in post-mitotic nuclear morphology. These defects are dependent on a hitherto unknown chromatin-binding region of LAP1 that we have delineated. LAP1-induced NE abnormalities are efficiently suppressed by expression of wild-type but not ATPase-deficient Torsins. Furthermore, a dominant-negative Torsin induces chromosome segregation defects in a LAP1-dependent manner. These results indicate that association of LAP1 with chromatin in the nucleus can be modulated by Torsins in the perinuclear space, shedding new light on the LAP1-Torsin interplay.

Image matting trimap optimization by ant colony algorithm

In this paper, we present a way to create an accurate trimap. Since the matting problem is a serious under-constrained problem, the user is required to provide additional constraint information to estimate the alpha value of the mixed pixels. The smaller the area of the unknown region of the user-provided trimap, the more accurate the alpha value of the estimated mixed pixels. But manually creating the trimap is a complicated and time-consuming task, and it is even impractical to manually create trimaps for some tasks. We use the ant colony algorithm to determine the boundary information of the foreground objects, and fuse different pheromone images at the superpixel level to create an accurate trimap. The trimap generated by our method also has higher precision when the edge of the foreground object has a lot of fine hair. Experiments show that the high-quality trimap can be generated by the method of this paper, which can effectively improve the performance of the matting algorithm and achieve accurate alpha mask estimation.

Harran Üniversitesi Herbaryum (HARRAN)’undaki Fabaceae Familyasının Taksonları

The aim of this study, was carried out to re-evaluate the Fabaceae family collection in Harran University Herbarium (HARRAN) and to determine the number of taxa. As a result of the examination of 975 specimens belonging to Fabaceae family in HARRAN Herbarium, 200 taxa belonging to 25 genera were determined. 33 of these taxa are endemic to Turkey. Fabaceae family found in HARRAN Herbarium, in terms of taxon richness are Astragalus L. (48), Medicago L. (37), Trifolium L. (25), Trigonella L. (22) and Vicia L. (20). The distribution of taxa belonging to Fabaceae family according to phytogeographic regions in herbarium is Irano-Turanian 47%, Mediterranean 32.5%, Euro-Siberia 2.5% and those with unknown phytogeographic region are 18%. As a result of this study, it is determined that some of the taxa belong to genus Alhagi, Astragalus, Hedysarum, Onobrychis, Scorpiurus and Trigonella fall into synonym and valid names are given in this study.

AN AUTOMATIC SEMANTIC MAP GENERATION METHOD USING TRAJECTORY DATA

Abstract. It’s easily to obtain the geometric information of terrain features in a timely manner using advanced surveying and mapping methods, but it is impossible to obtain their semantic information with low latency due to the rapid development of cities. The popularity of GPS-enabled devices and technologies provide us a large number of personal location information. Moreover, it is possible to extract the personal or group behavior pattern due to the regularity of human behavior. Those conditions make it possible to extract and identify human behavior patterns from their trajectory data. In this paper, we present an automatic semantic map generation method that extract semantic patterns and take advantage of them to tagging spatial objects in an unknown region based on known semantic patterns. We study the regularity of trajectory data and build the semantic pattern based on the regularity of human behavior. Most importantly, we use known semantic patterns to identify the semantics of the stay points in the unknown region, and use this method to realize the semantic recognition of the stay points. Results of the experiments show the effectiveness of our proposed method.

An analytical method for determining the plastic regions around two circular holes in an infinite medium

The previous analytical methods mainly focused on the determination of the plastic region with only one circular hole in an infinite domain. However, there is no exact analytical method for the plastic regions when there are two circular holes. In this paper, the conformal transformation in the complex variable method is proposed to solve the elastoplastic problem of an infinite medium containing two equal circular holes. The research is based on the following preconditions: the two plastic regions formed are disconnected with each other, and each plastic region can completely surround each hole. Initially, the unknown elastic region in the physical plane is mapped onto an annular region in the image plane using the conformal transformation. Subsequently, the nonlinear equations containing the mapping function coefficients are established according to the stress continuity conditions along the two elastoplastic interfaces. The coefficients are set as the design variables and solved effectively by the differential-evolution algorithm. The presented solution is verified by comparing to the Finite Element analysis of ABAQUS software. Finally, the influences of the separation distance between the two holes and the loads on the plastic regions are analyzed.

Analysis of Cassini radio tracking data for the construction of INPOP19a: A new estimate of the Kuiper belt mass

Context. Recent discoveries of new trans-Neptunian objects have greatly increased the attention by the scientific community to this relatively unknown region of the solar system. The current level of precision achieved in the description of planet orbits has transformed modern ephemerides in the most updated tools for studying the gravitational interactions between solar system bodies. In this context, the orbit of Saturn plays a primary role, especially thanks to Cassini tracking data collected during its 13-year mission around the ringed planet. Planetary ephemerides are currently mainly built using radio data, in particular with normal points derived from range and Doppler observables exchanged between ground stations and interplanetary probes. Aims. We present an analysis of Cassini navigation data aimed at producing new normal points based on the most updated knowledge of the Saturnian system developed throughout the whole mission. We provide additional points from radio science dedicated passes of Grand Finale orbits and Titan flybys. An updated version of the INPOP planetary ephemerides based upon these normal points is presented, along with a new estimate of the mass of trans-Neptunian object rings located in the 2:1 and 3:2 mean motion resonances with Neptune. Methods. We describe in detail the orbit determination process performed to construct the normal points and their associated uncertainties and how we process those points to produce a new planetary ephemeris. Results. From the analysis, we obtained 623 new normal points for Saturn with metre-level accuracy. The ephemeris INPOP19a, including this new dataset, provides an estimated mass for the trans-Neptunian object rings of (0.061 ± 0.001)M⊕.

Clarifying the cryptogenic species Polydora neocaeca Williams & Radashevsky, 1999 (Annelida: Spionidae): a shell-boring invasive pest of molluscs from locations worldwide

Several studies noted similarities between Polydora neocaeca Williams & Radashevsky, 1999, described from the northeast coast of the USA, and shell-boring Polydora haswelli Blake & Kudenov, 1978, described from southeast Australia, and emphasised the need for molecular data to resolve the taxonomy of these species. This study investigates the potential conspecificity of these two species. We provide morphological evidence based on the shape of the modified spines of chaetiger 5 and methyl green staining patterns to support the hypothesis that sand tube-dwelling (habitat of the holotype, i.e. P. haswelli sensu stricto) and shell-boring species identified as P. haswelli are different species. We also provide molecular (nuclear 18S rRNA and mitochondrial COI) and morphological evidence that shows that a previously unidentified shell-boring Polydora species from South Africa and shell-boring species previously recorded as P. haswelli from Japan and China are conspecific with P. neocaeca from its type locality in Rhode Island. We therefore recommend that shell-boring species previously identified as P. haswelli be referred to P. neocaeca. Furthermore, population structure analyses based on COI show distinct geographical groupings, with one shared haplotype among samples for Japan and South Africa. Although limited sampling makes it difficult to draw conclusions regarding the native range for P. neocaeca, we argue that this species represents a cryptic invasion and may have moved with infested molluscs for aquaculture or by shipping. Although described from the east coast of the USA, P. neocaeca may be indigenous to Australia, Asia or an unknown region.

Control of the neuroprotective Lipocalin Apolipoprotein D expression by alternative promoter regions and differentially expressed mRNA 5’ UTR variants

The Lipocalin Apolipoprotein D (ApoD) is one of the few genes consistently overexpressed in the aging brain, and in most neurodegenerative and psychiatric diseases. Its functions include metabolism regulation, myelin management, neuroprotection, and longevity regulation. Knowledge of endogenous regulatory mechanisms controlling brain disease-triggered ApoD expression is relevant if we want to boost pharmacologically its neuroprotecting potential. In addition to classical transcriptional control, Lipocalins have a remarkable variability in mRNA 5'UTR-dependent translation efficiency. Using bioinformatic analyses, we uncover strong selective pressures preserving ApoD 5'UTR properties, indicating unexpected functional conservation. PCR amplifications demonstrate the production of five 5'UTR variants (A-E) in mouse ApoD, with diverse expression levels across tissues and developmental stages. Importantly, Variant E is specifically expressed in the oxidative stress-challenged brain. Predictive analyses of 5'UTR secondary structures and enrichment in elements restraining translation, point to Variant E as a tight regulator of ApoD expression. We find two genomic regions conserved in human and mouse ApoD: a canonical (α) promoter region and a previously unknown region upstream of Variant E that could function as an alternative mouse promoter (β). Luciferase assays demonstrate that both α and β promoter regions can drive expression in cultured mouse astrocytes, and that Promoter β activity responds proportionally to incremental doses of the oxidative stress generator Paraquat. We postulate that Promoter β works in association with Variant E 5'UTR as a regulatory tandem that organizes ApoD gene expression in the nervous system in response to oxidative stress, the most common factor in aging and neurodegeneration.

Rapidly-exploring Random Trees multi-robot map exploration under optimization framework

Rapidly-exploring Randomized Trees (RRT) is a kind of probabilistically complete exploration algorithm based on the tree structure. It has been widely used in the robotic navigation since it guarantees the complete discovery and the exploration of environment maps through robots. In the present study, the RRT algorithm is extended to propose an optimization-based map exploration strategy for multiple robots to actively explore and build environment maps. The present study adopts a market-based task allocation strategy, which to maximize the profit, for the coordination between robots. In the extension of the RRT, the cost function consists the unknown region and the passed unknown region. The unknown region is explored for a given frontier point, while the passed unknown region is the area, where the robot moves towards the target frontier point. When the robot moves from the start position to the target frontier point, the trajectory length is defined as a constraint for the optimization. The main contributions of the present study can be summarized in optimizing the frontier points, defining a new task allocation strategy and applying different evaluation rules, including the running time and the trajectory length. These rules are applied to explore the multi-robot map in simulated and practical environments. Then the Robot Operating System (ROS) is utilized to evaluate the application of the proposed exploration strategy on Turtlebots in a 270 m2 room. Obtained results from the simulation and the experiment demonstrate that the proposed method outperforms the Umari’s approach from both the running time and the trajectory length aspects.

MR-SimExCoverage: Multi-robot Simultaneous Exploration and Coverage

In this paper, we present a novel problem of simultaneous exploration and area coverage by multiple cooperating mobile robots. As the robots cover an initially unknown region, they perform intermittent exploration of the region and build a map, which in turn is used to plan the coverage path. We use a Voronoi partition based multi-robot coverage strategy using the Manhattan distance metric to solve the coverage problem and a frontier based exploration strategy for exploration mapping. We provide results of simulation using Matlab/V-rep environments to demonstrate the proposed multi-robot simultaneous exploration and coverage (MR-SimExCoverage) problem using the spanning tree based coverage (STC) algorithm.

A Novel Current Reconstruction Method Based on Elastic Net Regularization

Indirect current measurement method has played a pivotal role in power grid, owing to the development of inverse problem theory. In this article, a general current source reconstruction method based on the current element model was theoretically proposed. The method discretized the unknown current source region and aimed to reconstruct the actual current distribution according to the synthetical characteristics of discretized current elements. An approximated linear calculation model was derived to construct the inverse relationships between the current element and magnetic field distribution. To solve the ill-posed inverse problem, the elastic net regularization methods under non-Bayesian and Bayesian regimes were presented. The theoretical analysis was implemented under different noise levels. Four current distributions concerning the line current and volume current in practical applications were reconstructed by the two elastic net regularization methods. The results indicated that the methods were feasible in solving this ill-posed inverse problem, obtaining the current parameters and magnetic field distribution effectively. This novel regularization method can be utilized in the measurement of irregular current forms in electrical equipment, leading to an effective and robust approach in operation state acquisition and fault diagnosis of electrical equipment in power grid.

New regulatory mechanism-based inhibitors of aspartate transcarbamoylase for potential anticancer drug development.

Aspartate transcarbamoylase (ATCase) is a key enzyme which regulates and catalyzes the second step of denovo pyrimidine synthesis in all organisms. Escherichiacoli ATCase is a prototypic enzyme regulated by both product feedback and substrate cooperativity, whereas human ATCase is a potential anticancer target. Through structural and biochemical analyses, we revealed that R167/130's loop region in ATCase serves as a gatekeeper for the active site, playing a new and unappreciated regulatory role in the catalytic cycle of ATCase. Based on virtual compound screening simultaneously targeting the new regulatory region and active site of human ATCase, two compounds were identified to exhibit strong inhibition of ATCase activity, proliferation of multiple cancer cell lines, and growth of xenograft tumors. Our work has not only revealed a previously unknown regulatory region of ATCase that helps uncover the catalytic and regulatory mechanism of ATCase, but also successfully guided the identification of new ATCase inhibitors for anticancer drug development using a dual-targeting strategy. DATABASE: Structure data are available in Protein Data Bank under the accession numbers: 6KJ7 (G166P ecATCase), 6KJ8 (G166P ecATCase-holo), 6KJ9 (G128/130A ecATCase), and 6KJA (G128/130A ecATCase-holo).

3D Shared Matting Method for Directly Extracting Standard Organ Models from Human Body Color Volume Image.

In some medical applications (e.g., virtual surgery), standard human organ models are very important and useful. Now that real human body slice image sets have been collected by several countries, it is possible to obtain real standard organ models. Understanding how to abandon the traditional model construction method of Photoshop sketching slice by slice and directly extracting 3D models from volume images has been an interesting and challenging issue. In this paper, a 3D color volume image matting method has been proposed to segment human body organ models. First, the scope of the known area will be expanded by means of propagation. Next, neighborhood sampling to find the best sampling for voxels in an unknown region will be performed and then the preliminary opacity using the sampling results will be calculated. The final result will be obtained by applying local smoothing to the image. From the experimental results, it has been observed that our method is effective for real standard organ model extraction.

A New Method to Evaluate Yarn Appearance Qualities Based on Machine Vision and Image Processing

For improving the efficiency, flexibility and robustness of yarn defect system, a new method is proposed to evaluate the quality of yarn by testing the yarn diameter, defects and hairiness based on machine vision and image processing technology. Firstly, the diameter image processing unit (DIPU) is defined and a series of sampling points are selected from moving yarn, and the DIPU corresponding to each sampling point is segmented from the captured yarn images. The average diameter of DIPU is used to represent the yarn diameter of the test points. In the extraction of yarn images, the DIPU is divided into definite foreground region, definite background region and unknown region according to the characteristics of gray-level projection distribution, and the unknown region is further processed with Poisson matting method, in which the yarn image and background image are completely separated by a defined connectivity classifier. After the yarn core is extracted by the classifier, the hairiness is divided by using image subtraction. Finally, in order to further evaluate the quality of yarn, the yarn defects were analyzed by the method of statistical methods.

Applications of Clustering Techniques in Data Mining: A Comparative Study

In modern scientific research, data analyses are often used as a popular tool across computer science, communication science, and biological science. Clustering plays a significant role in the reference composition of data analysis. Clustering, recognized as an essential issue of unsupervised learning, deals with the segmentation of the data structure in an unknown region and is the basis for further understanding. Among many clustering algorithms, “more than 100 clustering algorithms known” because of its simplicity and rapid convergence, the K-means clustering algorithm is commonly used. This paper explains the different applications, literature, challenges, methodologies, considerations of clustering methods, and related key objectives to implement clustering with big data. Also, presents one of the most common clustering technique for identification of data patterns by performing an analysis of sample data.

Parallelisation of practical shared sampling alpha matting with OpenMP

In modern filmmaking industry, image matting has been one of the common tasks in video side effects and the necessary intermediate steps in computer vision. It pulls the foreground object from the background of an image by estimating the alpha values. However, the computational speed for matting high resolution images can be significantly slow due to its complexity and computation that is proportional to the size of unknown region. In order to improve the performance, we implement a parallel alpha matting code with OpenMP from existing sequential code for running on the multicore servers. We present and discuss the algorithm and experimentation results from the perspective of the parallel application developer. The development takes less effort, and the results show significant performance improvement of the entire program.

Parallelisation of practical shared sampling alpha matting with OpenMP

In modern filmmaking industry, image matting has been one of the common tasks in video side effects and the necessary intermediate steps in computer vision. It pulls the foreground object from the background of an image by estimating the alpha values. However, the computational speed for matting high resolution images can be significantly slow due to its complexity and computation that is proportional to the size of unknown region. In order to improve the performance, we implement a parallel alpha matting code with OpenMP from existing sequential code for running on the multicore servers. We present and discuss the algorithm and experimentation results from the perspective of the parallel application developer. The development takes less effort, and the results show significant performance improvement of the entire program.

On the code modernization of shared sampling alpha matting with OpenMP

Image matting separates the foreground object from a given image by estimating the alpha values. It has been one crucial step in computer vision, as well as an essential technique in visual effects in the modern filmmaking industry. Due to its time complexity being proportional to the size of the unknown region, the computational speed for processing image matting on high-resolution images has been traditionally slow. In this paper, we present the code modernization of Shared Sampling Alpha Matting (SSAM) algorithm with OpenMP to speed up the computation on multicore server platforms. Significant efforts are required due to the sequential nature of the code, with data dependencies and complex data structures in the original code. The implementation of migrating the SSAM code to multicore platforms and the experiments performed on several server platforms of different categories/specs are presented. Our experimental results demonstrate that the Alpha Matting using OpenMP increases performance significantly on Intel Xeon Phi Knights Landing (KNL) processors when processing high-resolution images. The code improvements we achieved can support a parallel application developer with high-performance goals.

Sequential model based optimization of partially defined functions under unknown constraints

This paper presents a sequential model based optimization framework for optimizing a black-box, multi-extremal and expensive objective function, which is also partially defined, that is it is undefined outside the feasible region. Furthermore, the constraints defining the feasible region within the search space are unknown. The approach proposed in this paper, namely SVM-CBO, is organized in two consecutive phases, the first uses a Support Vector Machine classifier to approximate the boundary of the unknown feasible region, the second uses Bayesian Optimization to find a globally optimal solution within the feasible region. In the first phase the next point to evaluate is chosen by dealing with the trade-off between improving the current estimate of the feasible region and discovering possible disconnected feasible sub-regions. In the second phase, the next point to evaluate is selected as the minimizer of the Lower Confidence Bound acquisition function but constrained to the current estimate of the feasible region. The main of the paper is a comparison with a Bayesian Optimization process which uses a fixed penalty value for infeasible function evaluations, under a limited budget (i.e., maximum number of function evaluations). Results are related to five 2D test functions from literature and 80 test functions, with increasing dimensionality and complexity, generated through the Emmental-type GKLS software. SVM-CBO proved to be significantly more effective as well as computationally efficient.

Kafka’s animals between mimicry and assimilation

Abstract In Kafka’s literary world, several animals emerge; they belong to an odd and enigmatic fauna, on the edge between violence and artistry but also between stillness and music; according to the writer, scripture represents both the fault and the punishment waiting for the solitary artist. Animals, especially depicted as hordes of small mice or other rodents, also hint to the heterogeneous structure of the Self, who doesn’t manage to keep under control all the divisions in his ambiguous dentity. Through opposition between the point of view of the subject, who considers his own isolation as indispensable to carry on writing, and the multitude of escaping small animals, Kafka also expresses and experiences his own impossibility of “description” (Beschreibung). In the meantime, Kafka’s animals embody the creatural and unconscious sources of imagination the writer draws from that constantly escape his own control and willingness, pushing forwards into an unknown and inhospitable region, towards the wasteland, the eternal winter that can be identified with scripture. In writing, a deep metamorphosis of the Self takes place. Kafka shares this belief with one of the writers he most admired and considered his master, Gustave Flaubert, who firmly thought that, while writing, one loses his previous identity, becoming someone else, even assuming the appearence of the “otherness”. We can state that Kafka’s imagery of animals takes to the extreme the paradox and ambiguity the idea of writing relies on, also reproducing, especially, in his hybrid creatures, the feeling of uncertainty and lack of safety of the assimilated Jewish artist.