Miracle-dz/UniFAR-FTUs huggingface dataset:
Neuroendocrine differentiation factor, IA-1, is a transcriptional repressor and contains a specific DNA-binding domain: identification of consensus IA-1 binding sequence.

A novel cDNA, insulinoma-associated antigen-1 (IA-1), containing five zinc-finger DNA-binding motifs, was isolated from a human insulinoma subtraction library. IA-1 expression is restricted to fetal but not adult pancreatic and brain tissues as well as tumors of neuroendocrine origin. Using various GAL4 DNA binding domain (DBD)/IA-1 fusion protein constructs, we demonstrated that IA-1 functions as a transcriptional repressor and that the region between amino acids 168 and 263 contains the majority of the repressor activity. Using a selected and amplified random oligonucleotide binding assay and bacterially expressed GST-IA-1DBD fusion protein (257-510 a.a.), we identified the consensus IA-1 binding sequence, TG/TC/TC/TT/AGGGGG/TCG/A. Further experiments showed that zinc-fingers 2 and 3 of IA-1 are sufficient to demonstrate transcriptional activity using an IA-1 consensus site containing a reporter construct. A database search with the consensus IA-1 binding sequence revealed target sites in a number of pancreas- and brain-specific genes consistent with its restricted expression pattern. The most significant matches were for the 5'-flanking regions of IA-1 and NeuroD/beta2 genes. Co-transfection of cells with either the full-length IA-1 or hEgr-1AD/IA-1DBD construct and IA-1 or NeuroD/beta2 promoter/CAT construct modulated CAT activity. These findings suggest that the IA-1 protein may be auto-regulated and play a role in pancreas and neuronal development, specifically in the regulation of the NeuroD/beta2 gene.
Highly sensitive detection of protein biomarkers via nuclear magnetic resonance biosensor with magnetically engineered nanoferrite particles

Magnetic-based biosensors are attractive for on-site detection of biomarkers due to the low magnetic susceptibility of biological samples. Here, we report a highly sensitive magnetic-based biosensing system that is composed of a miniaturized nuclear magnetic resonance (NMR) device and magnetically engineered nanoferrite particles (NFPs). The sensing performance, also identified as the transverse relaxation (R2) rate, of the NMR device is directly related to the magnetic properties of the NFPs. Therefore, we developed magnetically engineered NFPs (MnMg-NFP) and used them as NMR agents to exhibit a significantly improved R2 rate. The magnetization of the MnMg-NFPs was increased by controlling the Mn and Mg cation concentration and distribution during the synthesis process. This modification of the Mn and Mg cation directly contributed to improving the R2 rate. The miniaturized NMR system, combined with the magnetically engineered MnMg-NFPs, successfully detected a small amount of infectious influenza A H1N1 nucleoprotein with high sensitivity and stability.
An Active-Set Quadratic Programming Method Based On Sequential Hot-Starts

A new method for solving sequences of quadratic programs (QPs) is presented. For each new QP in the sequence, the method utilizes hot-starts that employ information computed by an active-set QP solver during the solution of the first QP. This avoids the computation and factorization of the full matrices for all but the first problem in the sequence. The proposed algorithm can be seen as an extension of the iterative refinement procedure for linear systems to QP problems, coupled with the application of an accelerated linear solver method that employs hot-started QP solves as preconditioners. Local convergence results are presented. The practical performance of the proposed method is demonstrated on a sequence of QPs arising in nonlinear model predictive control and during the solution of a set of randomly generated nonlinear optimization problems using sequential quadratic programming. The results show a significant reduction in the computation time for large problems with dense constraint matrices, as well as in the number of matrix-vector products.
The ecological–evolutionary interplay: density-dependent sexual selection in a migratory songbird

Little is understood about how environmental heterogeneity influences the spatial dynamics of sexual selection. Within human-dominated systems, habitat modification creates environmental heterogeneity that could influence the adaptive value of individual phenotypes. Here, we used the gray catbird to examine if the ecological conditions experienced in the suburban matrix (SM) and embedded suburban parks (SP) influence reproductive strategies and the strength of sexual selection. Our results show that these habitats varied in a key ecological factor, breeding density. Moreover, this ecological factor was closely tied to reproductive strategies such that local breeding density predicted the probability that a nest would contain extra-pair offspring. Partitioning reproductive variance showed that while within-pair success was more important in both habitats, extra-pair success increased the opportunity for sexual selection by 39% at higher breeding densities. Body size was a strong predictor of relative reproductive success and was under directional selection in both habitats. Importantly, our results show that the strength of sexual selection did not differ among habitats at the landscape scale but rather that fine-scale variation in an ecological factor, breeding density, influenced sexual selection on male phenotypes. Here, we document density-dependent sexual selection in a migratory bird and hypothesize that coarse-scale environmental heterogeneity, in this case generated by anthropogenic habitat modification, changed the fine-scale ecological conditions that drove the spatial dynamics of sexual selection.
Lyapunov Methods for Time-Invariant Delay Difference Inclusions

Motivated by the fact that delay difference inclusions (DDIs) form a rich modeling class that includes, for example, uncertain time-delay systems and certain types of networked control systems, this paper provides a comprehensive collection of Lyapunov methods for DDIs. First, the Lyapunov-Krasovskii approach, which is an extension of the classical Lyapunov theory to time-delay systems, is considered. It is shown that a DDI is $\mathcal{KL}$-stable if and only if it admits a Lyapunov-Krasovskii function (LKF). Second, the Lyapunov-Razumikhin method, which is a type of small-gain approach for time-delay systems, is studied. It is proved that a DDI is $\mathcal{KL}$-stable if it admits a Lyapunov-Razumikhin function (LRF). Moreover, an example of a linear delay difference equation which is globally exponentially stable but does not admit an LRF is provided. Thus, it is established that the existence of an LRF is not a necessary condition for $\mathcal{KL}$-stability of a DDI. Then, it is shown that the existence of an LRF is a sufficient condition for the existence of an LKF and that only under certain additional assumptions is the converse true. Furthermore, it is shown that an LRF induces a family of sets with certain contraction properties that are particular to time-delay systems. On the other hand, an LKF is shown to induce a type of contractive set similar to those induced by a classical Lyapunov function. The class of quadratic candidate functions is used to illustrate the results derived in this paper in terms of both LKFs and LRFs, respectively. Both stability analysis and stabilizing controller synthesis methods for linear DDIs are proposed.
Leptin Increases Hypothalamic Pro-opiomelanocortin mRNA Expression in the Rostral Arcuate Nucleus

Melanocortins are peptides, cleaved from the pro-opiomelanocortin (POMC) precursor, that act in the brain to reduce food intake and are potential mediators of leptin action. In the forebrain, melanocortins are derived from POMC-containing neurons of the hypothalamic arcuate nucleus. To test the hypothesis that these POMC neurons are regulated by leptin, we used in situ hybridization to determine whether reduced leptin signaling (as occurs in fasting), genetic leptin deficiency (in obese ob/ob mice), or genetic leptin resistance (in obese db/db mice) lower expression of POMC mRNA. We further hypothesized that leptin administration would raise hypothalamic POMC mRNA levels in leptin-deficient animals, but not in mice with defective leptin receptors. In wild-type mice (n = 12), fasting for 48 h lowered POMC mRNA levels in the rostral arcuate nucleus by 53%, relative to values in fed controls (n = 8; P < 0.001). Similarly, arcuate nucleus POMC mRNA levels were reduced by 46 and 70% in genetically obese ob/ob (n = 6) and db/db mice (n = 6), respectively, as compared with wild-type mice (n = 5) (P < 0.01 for both comparisons). Five daily intraperitoneal injections of recombinant murine leptin (150 μg) raised levels of POMC mRNA in the rostral arcuate nucleus of ob/ob mice (n = 8) by 73% over saline-treated ob/ob control values (n = 8; P < 0.01), but was without effect in db/db mice (n = 6). In normal rats, two injections of a low dose of leptin (3.5 μg) into the third cerebral ventricle (n = 15) during a 40-h period of fasting also increased POMC mRNA levels in the rostral arcuate nucleus to values 39% greater than those in vehicle-treated controls (n = 14; P = 0.02). We conclude that reduced central nervous system leptin signaling owing to fasting or to genetic defects in leptin or its receptor lower POMC mRNA levels in the rostral arcuate nucleus. The finding that leptin reverses this effect in ob/ob, but not db/db, mice suggests that leptin stimulates arcuate nucleus POMC gene expression via a pathway involving leptin receptors. These findings support the hypothesis that leptin signaling in the brain involves activation of the hypothalamic melanocortin system.
Queue response to input correlation functions: discrete spectral analysis

A new concept of spectral characterization of the wideband input process in high-speed networks is examined. The eigenstructure technique, through the modeling of input Markov chains, helps localize wideband sources in a subspace, especially in a low frequency band. Simple periodic chains are used for the construction of the input rate process. The input power spectral distribution is defined in a discrete frequency domain. Each input traffic stream is characterized by an independent Markov modulated Poisson process (MMPP). The underlying Markov chain is used to reflect the time autocorrelation properties of the input process at a macro level. Expressions are derived for correlation and power spectral functions of the MMPP input. A queuing analysis with multiple periodic input functions is described. The queue response to input correlation functions is examined. The advantages of using the spectral domain to analyze and design network control and resource management are discussed. <<ETX>>
Systolic array realization of a neural network-based face recognition system

Automatic recognition of faces has wide applications ranging from security to man-machine interaction. A popular method for face recognition is using eigen faces. Eigen faces are obtained by doing principal component analysis (PCA) on the face database. A neural network that performs PCA is called principal component neural network (PCNN). This paper presents a systolic array design for principal component neural network-based face recognition system. Results of implementation of the design in an FPGA device of Xilinx confirm the suitability of the design for video surveillance in busy public places.
A hybrid batch job scheduling algorithm for grid environment

Grid computing is a collection of geographically heterogeneous distributed computational resources that enables users for sharing data and other computing resources. One of the major challenges in grid computing is how to schedule batch jobs across such an environment with minimum makespan (the finishing time of the last job) and flow time. In this study, a hybrid batch job scheduling method is proposed for grid environment that combines genetic and particle swarm optimization techniques to reduce makespan and flowtime. Experimental results show a reduction in makespan for 7 out of 12 instances of Braun workload comparing to minmin, maxmin, and discrete PSO algorithms.
Capture of the Late Phase of Long-Term Potentiation within and across the Apical and Basilar Dendritic Compartments of CA1 Pyramidal Neurons: Synaptic Tagging Is Compartment Restricted

Studies in the rodent hippocampus have demonstrated that when the late phase of long-term potentiation (L-LTP) is induced in a set of synapses by suprathreshold stimulation, L-LTP can also be expressed by other synapses receiving subthreshold stimulation, a phenomenon usually referred as “capture of L-LTP. ” Because the pyramidal neurons in the mammalian hippocampus have both apical and basal dendrites, we have now investigated whether capture of L-LTP, previously described only within the apical dendritic compartment, can also take place within the basilar dendritic compartment and, if so, whether capture can be accomplished from one dendritic compartment to the other. We found that capture of L-LTP can also occur within the basilar dendritic compartment and that the tagging signal that enables capture appears to be the same in both dendritic compartments. However, capture across compartments, between the apical and basilar dendrites, follows different rules and requires a stronger triggering stimulation than capture within a compartment. These results suggest that the tag appears specific to a compartment either apical or basilar and that an additional mechanism may be required to capture across compartments.
Enhanced Latent Semantic Analysis by considering mistyped words in automated essay scoring

In this paper, we present an approach to consider the mistyped words which frequently occur in an essay. Not all the mistyped words are totally wrong. If they are caused by human errors, we should look further to handle it. Thus, enhanced version of Latent Semantic Analysis (LSA) is proposed. LSA is enhanced by calculating the value of mistyped words before constructing the term document matrix. Essays which are scored consists of 119 English essays of student's writing assignment while several essays as gold standard are made by experts. All of the essays have been scored manually by the human grader. Enhanced LSA gives the closeness level of 0.242 to human judgment, while LSA indicates the closeness level of 0.244 to human judgment. Unfortunately, Enhanced LSA cannot outperform GLSA which is one of current methods. The experiment result indicates that Enhanced LSA by considering mistyped words has a better closeness value to human judgment compared with LSA
Convex Optimization Procedure for Clustering: Theoretical Revisit

In this paper, we present theoretical analysis of SON - a convex optimization procedure for clustering using a sum-of-norms (SON) regularization recently proposed in [8, 10, 11, 17]. In particular, we show if the samples are drawn from two cubes, each being one cluster, then SON can provably identify the cluster membership provided that the distance between the two cubes is larger than a threshold which (linearly) depends on the size of the cube and the ratio of numbers of samples in each cluster. To the best of our knowledge, this paper is the first to provide a rigorous analysis to understand why and when SON works. We believe this may provide important insights to develop novel convex optimization based algorithms for clustering.
Emerging Power Quality Challenges Due to Integration of Renewable Energy Sources

Renewable energy becomes a key contributor to our modern society, but their integration to power grid poses significant technical challenges. Power quality is an important aspect of renewable energy integration. The major power quality concerns are: 1) Voltage and frequency fluctuations, which are caused by noncontrollable variability of renewable energy resources. The intermittent nature of renewable energy resources due to ever-changing weather conditions leads to voltage and frequency fluctuations at the interconnected power grid. 2) Harmonics, which are introduced by power electronic devices utilized in renewable energy generation. When penetration level of renewable energy is high, the influence of harmonics could be significant. In this paper, an extensive literature review is conducted on emerging power quality challenges due to renewable energy integration. This paper consists of two sections: 1) Power quality problem definition. Wind turbines and solar photovoltaic systems and their power quality issues are summarized. 2) Existing approaches to improve power quality. Various methods are reviewed, and the control-technology-based power quality improvement is the major focus of this paper. The future research directions for emerging power quality challenges for renewable energy integration are recommended.
3D Analysis of chromosome architecture: advantages and limitations with SEM

Three-dimensional mitotic plant chromosome architecture can be investigated with the highest resolution with scanning electron microscopy compared to other microscopic techniques at present. Specific chromatin staining techniques making use of simultaneous detection of back-scattered electrons and secondary electrons have provided conclusive information on the distribution of DNA and protein in barley chromosomes through mitosis. Applied to investigate the structural effects of different preparative procedures, these techniques were the groundwork for the “dynamic matrix model” for chromosome condensation, which postulates an energy-dependent process of looping and bunching of chromatin coupled with attachment to a dynamic matrix of associated protein fibers. Data from SEM analysis shows basic higher order chromatin structures: chromomeres and matrix fibers. Visualization of nanogold-labeled phosphorylated histone H3 (ser10) with high resolution on chromomeres shows that functional modifications of chromatin can be located on structural elements in a 3D context.
A competing hazard model of household vehicle transaction behavior with discrete time intervals and unobserved heterogeneity

Abstract The analysis of household vehicle ownership behavior is considered as one of the most important elements of household travel behavior studies. Several different modeling frameworks have been employed to develop vehicle ownership models. Among these models, applications of hazard based duration models have drawn particular attention in recent years. Hazard models of vehicle transactions often have overlooked the effects of unobserved heterogeneity in the model. This study attempts to address this limitation by formulating and estimating a discrete competing hazard model of vehicle transaction with multiple outcomes that also accounts for unobserved heterogeneity. Three transaction decisions of purchasing, trading, or disposing of a vehicle are considered and competing duration models, with and without unobserved heterogeneity are estimated and compared. The modeling framework is flexible enough to consider transaction times that are not continuous and the failures that occur within discrete time intervals.
A New Hybrid Feature Selection using Natural Language Processing for Text Clustering

Text clustering is unsupervised machine learning method.It needs representation of objects and similarity measure. which compares distribution of features between objects. For the high dimensionality of feature space performance of clustering algorithms decreases.Two techniques are used to deal with this problem: feature extraction and feature selection.In this paper, we describe the hybrid method used for text clustering which is the combination of active feature selection,genetic algorithm and bisecting K-means.Internal quality measures computes the effectiveness of clustering.Our method is compared with K-means.
Microgrid economic operation and research on dispatch strategy

Microgrid could allow renewable and clean resources to penetrate into a controllable utility and achieve maximum utilization of existing energy and demand-side management. An optimization model of economic operated CHP microgrid system considering heating income is established. The microsources can provide both active and reactive power in the model. A typical microgrid consisting of wind turbine, photo voltaic, storage battery, micro turbine, fuel cell, thermal and electric loads is selected as the research object. Based on the conventional economic dispatch strategies of microgrid, improved dispatch strategies are proposed and comparative analysis of their impact on the optimization results is conducted. Improved genetic algorithm is chosen for the optimization of this system and comprehensive cost of one day in grid-connected mode considering spot price and islanded mode assuring power supply reliability of sensitive loads, results are comparatively analyzed in detail. So the validity of the proposed model, dispatch strategy and algorithm is proved.
Improving the Lookup Performance of Chord Network by Hashing Landmark Clusters

DHTs are efficient peer-to-peer systems which can locate objects within an bounded amount of overlay hops. Originally, those systems don't exploit network proximity in the underlying Internet and lead to high latency when searching a target. Recently, some approaches, such as random landmarking (RLM) and lookup-parasitic random sampling (LPRS), have been suggested to build topology-aware overlay to improve the lookup efficiency. Although these approaches help the nodes in P2P system to be aware of the underlying network, they do a limited improvement because of their proximity neighbor selection. In this paper, a hashing landmark clusters (HLC) method is proposed to improve the performance of chord by improving the accuracy of proximity mapping between overlay network and physical network. The analysis demonstrates that our approach can reduce the lookup latency better than RLM & LPRS can
A New Scheme for Range Queries over Encrypted Data

Cloud servers could provide secure services to data management for encrypted sensitive data, however, the difficulties of querying these data by data owners increase.To solve the problem,this paper proposes a new scheme for range queries over encrypted data. In particular, the indices and interval trapdoors of sensitive data are first created by using circle mapping. Then, these indices and interval trapdoors are encrypted. During range query processing, cloud servers can efficiently retrieve and return query results after completing an assertion. Moreover, the correctness, security and computation complexity are presented and analyzed in details. Compared with the existing approaches, this method is more efficient and secure.
Face recognition across age progression using block matching method

A method of automatic face recognition for personal authentication is proposed considering the age progression between the registered face image and the captured one. In face recognition, the features of the captured face image are compared with those of the registered faces in database and the closest registered face is selected as the person's. However, if age progression exists between the captured face and the registered one, the features changes with the person's growth. In this paper, a method of face recognition over age progression is proposed using the block matching method, especially for the case that the registered face is taken in the childhood and the person is to be authenticated in his youth. Since block matching compensates the position shift of face components, this method can avoid the harmful effect of the shape change of faces caused by growth. The experiment results show advantage of the proposed method to the conventional holistic one.