Miracle-dz/UniFAR-FTUs huggingface dataset:
Ovarian steroids increase PSD‐95 expression and dendritic spines in the dorsal raphe of ovariectomized macaques

Estradiol (E) and progesterone (P) promote spinogenesis in several brain areas. Intracellular signaling cascades that promote spinogenesis involve RhoGTPases, glutamate signaling and synapse assembly. We found that in serotonin neurons, E ± P administration increases (a) gene and protein expression of RhoGTPases, (b) gene expression of glutamate receptors, and (c) gene expression of pivotal synapse assembly proteins. Therefore, in this study we determined whether structural changes in dendritic spines in the dorsal raphe follow the observed changes in gene and protein expression. Dendritic spines were examined with immunogold silver staining of a spine marker protein, postsynaptic density‐95 (PSD‐95) and with Golgi staining. In the PSD‐95 study, adult Ovx monkeys received placebo, E, P, or E + P for 1 month (n = 3/group). Sections were immunostained for PSD‐95 and the number of PSD‐95‐positive puncta was determined with stereology. E, P, and E + P treatment significantly increased the total number of PSD‐95‐positive puncta (ANOVA, P = 0.04). In the golgi study, adult Ovx monkeys received placebo, E or E + P for 1 month (n = 3–4) and the midbrain was golgi‐stained. A total of 80 neurons were analyzed with Neurolucida software. There was a significant difference in spine density that depended on branch order (two‐way ANOVA). E + P treatment significantly increased spine density in higher‐order (3°–5°) dendritic branches relative to Ovx group (Bonferroni, P < 0.05). In summary, E + P leads to the elaboration of dendritic spines on dorsal raphe neurons. The ability of E to induce PSD‐95, but not actual spines, suggests either a sampling or time lag issue. Increased spinogenesis on serotonin dendrites would facilitate excitatory glutamatergic input and, in turn, increase serotonin neurotransmission throughout the brain. Synapse 67:897–908, 2013. © 2013 Wiley Periodicals, Inc.
FEASIBLE PRODUCTION OF FIBRE-REINFORCED COMPOSITES THROUGH INLINE INSPECTION WITH MACHINE VISION

The application of Fibre-Reinforced Plastics is important in many engineering areas, but vital for the aerospace industry. This work shows how inline metrology can contribute to a feasible production of composite parts. An inspection system based on a Machine Vision System will be presented as a key-component for the automatic construction of complex Preform structures.
Multi-scale 3D deep convolutional neural network for hyperspectral image classification

Research in deep neural network (DNN) and deep learning has great progress for 1D (speech), 2D (image) and 3D (3D-object) recognition/classification problems. As HSI that with 2D spatial and 1D spectral information is quite different from 3D object image, the existing DNN cannot be directly extended to hyperspectral image (HSI) classification. A Multiscale 3D deep convolutional neural network (M3D-DCNN) is proposed for HSI classification, which could jointly learn both 2D Multi-scale spatial feature and 1D spectral feature from HSI data in an end-to-end approach, promising to achieve better results with large-scale dataset. Although without any hand-craft features or pre/post-processing like PCA, sparse coding etc, we achieve the state-of-the-art results on the standard datasets, which shows the technical validity and advancement of our method.
Gut microbial markers are associated with diabetes onset, regulatory imbalance, and IFN-γ level in NOD Mice

Gut microbiota regulated imbalances in the host's immune profile seem to be an important factor in the etiology of type 1 diabetes (T1D), and identifying bacterial markers for T1D may therefore be useful in diagnosis and prevention of T1D. The aim of the present study was to investigate the link between the early gut microbiota and immune parameters of non-obese diabetic (NOD) mice in order to select alleged bacterial markers of T1D. Gut microbial composition in feces was analyzed with 454/FLX Titanium (Roche) pyro-sequencing and correlated with diabetes onset age and immune cell populations measured in diabetic and non-diabetic mice at 30 weeks of age. The early gut microbiota composition was found to be different between NOD mice that later in life were classified as diabetic or non-diabetic. Those differences were further associated with changes in FoxP3+ regulatory T cells, CD11b+ dendritic cells, and IFN-γ production. The model proposed in this work suggests that operational taxonomic units classified to S24–7, Prevotella, and an unknown Bacteriodales (all Bacteroidetes) act in favor of diabetes protection whereas members of Lachnospiraceae, Ruminococcus, and Oscillospira (all Firmicutes) promote pathogenesis.
Frequency response and bandwidth analysis of multi-layer graphene nanoribbon and multi-walled carbon nanotube interconnects

This reported research analyses and compares the bandwidth and absolute frequency response of a multi-layer graphene nanoribbon (MLGNR) and a multi-walled carbon nanotube (MWCNT) at local, semi-global and global interconnect lengths. The transfer function of the driver-interconnect-load system is obtained by representing the interconnect line with an equivalent single conductor model of either a MLGNR or a MWCNT. Using absolute frequency response, it is observed that the bandwidth of the MLGNR is higher by almost ten times and four times in comparison to the MWCNT for local and global interconnect lengths, respectively.
Bacterial glycosyltransferase toxins

Mono‐glycosylation of host proteins is a common mechanism by which bacterial protein toxins manipulate cellular functions of eukaryotic target host cells. Prototypic for this group of glycosyltransferase toxins are Clostridium difficile toxins A and B, which modify guanine nucleotide‐binding proteins of the Rho family. However, toxin‐induced glycosylation is not restricted to the Clostridia. Various types of bacterial pathogens including Escherichia coli, Yersinia, Photorhabdus and Legionella species produce glycosyltransferase toxins. Recent studies discovered novel unexpected variations in host protein targets and amino acid acceptors of toxin‐catalysed glycosylation. These findings open new perspectives in toxin as well as in carbohydrate research.
Efficient Content-based Image Retrieval for Position Estimation on GPU

We propose an efficient content-based image retrieval (CBIR) method for position estimation of mobile devices. The idea is to use videos of first-person vision associated with geographical position information as the database. When a user sends a current subjective image, the system estimates the position using CBIR. Since features extracted from images are in general high-dimensional vectors, thousands of vectors are extracted even from a single image, resulting in high processing cost. To tackle this problem, we have proposed a method in which features are compressed using LSH, and GPU is used for accelerating CBIR. Nevertheless, it suffered from performance degradation due to write conflicts among different threads. This paper presents an improved method which avoids write conflicts and modifies LSH algorithm to improve accuracy. Also, we demonstrate the efficiency and accuracy of the proposed scheme through experiments using a video dataset.
SLFL Genes Participate in the Ubiquitination and Degradation Reaction of S-RNase in Self-compatible Peach

It has been proved that the gametophytic self-incompatibility (GSI), mainly exists in Rosaceae and Solanaceae, is controlled by S genes, which are two tightly linked genes located at highly polymorphic S-locus: the S-RNase for pistil specificity and the F-box gene (SFB/SLF) for pollen specificity, respectively. However, the roles of those genes in SI of peach are still a subject of extensive debate. In our study, we selected 37 representative varieties according to the evolution route of peach and identified their S genotypes. We cloned pollen determinant genes mutated PperSFB1m, PperSFB2m, PperSFB4m, and normal PperSFB2, and style determinant genes PperS1-RNase, PperS2-RNase, PperS2m-RNase, and PperS4-RNase. The mutated PperSFBs encode truncated SFB proteins due to a fragment insertion. The truncated PperSFBs and normal PperSFB2 interacted with PperS-RNases demonstrated by Y2H. Normal PperSFB2 was divided into four parts: box, box-V1, V1-V2, and HVa-HVb. The box domain of PperSFB2 did not interact with PperS-RNases, both of the box-V1 and V1-V2 had interactions with PperS-RNases, while the hypervariable region of PperSFB2 HVa-HVb only interacted with PperS2-RNase showed by Y2H and BiFC assay. Bioinformatics analysis of peach genome revealed that there were other F-box genes located at S-locus, and of which three F-box genes were specifically expressed in pollen, named as PperSLFL1, PperSLFL2, and PperSLFL3, respectively. In phylogenetic analysis PperSLFLs clustered with Maloideae SFBB genes, and PperSFB genes were clustered into the other group with other SFB genes of Prunus. Protein interaction analysis revealed that the three PperSLFLs interacted with PperSSK1 and PperS-RNases with no allelic specificity. In vitro ubiquitination assay showed that PperSLFLs could tag ubiquitin molecules onto PperS-RNases. The above results suggest that three PperSLFLs are the appropriate candidates for the “general inhibitor,” which would inactivate the S-RNases in pollen tubes, involved in the self-incompatibility of peach.
Accuracy of size distributions obtained from single particle static digital image analysis.

Image analysis has made decisive breakthrough in the recent years to become a reference technique in the field of combined size and shape analysis of particles. Thanks to full automation of the particle dispersion and the individual particle imaging process, productivity of the instrument is no longer a major drawback. However the question of representativity of analyses has not been discussed in statistical terms as should be. Because image analysis is based on the measurement of each individual particle, the accuracy of a size distribution has to be formulated in number of particles and not in terms of sample weight or duration of the analysis. The adequate particle number is linked to the shape of the distribution curve and its extension or range. In this paper, the accuracy of size distributions from image analysis is discussed from a theoretical and practical point of view.
On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization

According to The Brazilian Electricity Regulatory Agency, Brazil reached a loss of approximately U.S.$ 4 billion in commercial losses during 2011, which correspond to more than 27 000 GWh. The strengthening of the smart grid has brought a considerable amount of research that can be noticed, mainly with respect to the application of several artificial intelligence techniques in order to automatically detect commercial losses, but the problem of selecting the most representative features has not been widely discussed. In this paper, we make a parallel among the problem of commercial losses in Brazil and the task of irregular consumers characterization by means of a recent meta-heuristic optimization technique called Black Hole Algorithm. The experimental setup is conducted over two private datasets (commercial and industrial) provided by a Brazilian electric utility, and it shows the importance of selecting the most relevant features in the context of theft characterization.
Stab Resistance of Shear Thickening Fluid (STF)-Kevlar Composites for Body Armor Applications

Abstract : The stab resistance of shear thickening fluid (STF)-Kevlar and STF-Nylon fabric composites are investigated and found to exhibit significant improvements over neat fabric targets of equivalent areal density. Specifically, dramatic improvements in puncture resistance (spike threat) are observed under high and low speed loading conditions, while slight increases in cut protection are also observed. These results, combined with improvements in ballistic properties reported in earlier studies (Lee et al., 2002, Lee et al. 2003), indicate that these novel materials could be used to fabricate flexible body armors which provide improved protection against both stab and ballistic threats.
Stasis and Adaptation

Many of our most pressing societal challenges arise from our inability to move on from present practices and structures and do what is needed. Healthcare struggles to improve safety and quality. It resists adoption of best practices and persists in high levels of unwarranted variation in care delivery, and clings to financially unsustainable models of care. One explanation for this state of affairs is not a lack of will, but that we are experiencing system inertia--a consequence of the increasing complexity of our human systems. In this paper I explore three possible system level interventions that may help design systems that are less likely to approach inertia, as well as help change our current systems so that they again become adaptive, and move to the outcomes we desire. Firstly, I question our religious belief in the power of standards, an intervention designed to minimise adaptation and almost from first principles designed to lead to inertia. Next I explore the power of apoptosis, a process that sees existing structures and practices programmatically removed to free up resource for adaptation. Finally I explore a flexible but controversial approach to system management called market-based control. Whether any of these, together or in tandem, are a way out of inertia is an open question. However, it is time for us to engage with the challenge of system inertia, and find a way out.
Mitigating pilot contamination in massive MIMO system — 5G: An overview

5G, a future generation cellular networks is expected to meet the increasing demand of continuous growing mobile users and the emergence of services such as internet of things (IoT), machine-to-machine communication (M2M). Massive MIMO has been identified as a promising technology that offers orders-of-magnitude improvements in spectral-efficiency over 4G technologies. However, one of the major limiting factors is pilot contamination when TDD protocol is considered. Different methods have been proposed in literature to mitigate effect of pilot contamination in multi-cellular systems. This paper provides an overview of the proposed approaches in mitigating the effect of pilot contamination in massive MIMO systems. First, we classify the methods proposed under two categories: pilot-based approach and subspace-based approach. We examine the problems of pilot contamination and open issues are discussed.
Biochemical characterization of the GTP-sensing protein, CodY of Bacillus anthracis

The pleiotropism of the GTP-sensing transcriptional regulator CodY is evident by the gamut of processes that it regulates in almost all low G+C Gram-positive bacteria, including general metabolism, biosynthesis of some amino acids and transport systems, nitrogen uptake, sporulation, biofilm formation, motility and virulence. The role of CodY in virulence has been established in Bacillus anthracis, the top rated bioterrorism agent. In this study, we investigated the biochemical attributes of this global regulator. Homology modeling and sequence/structure analysis revealed putative GTP-binding residues in CodY of B. anthracis. CodY exhibited an interaction with the GTP as tested by ultraviolet cross-linking experiments. It could autophosphorylate itself at a conserved Ser215 residue. This was further corroborated by the impairment of autophosphorylation activity in the CodYS215A mutant. Autophosphorylation may be speculated as an additional mechanism regulating CodY activity in the cell. The protein could also hydrolyze GTP, albeit weakly, as indicated by thin- layer chromatography and spectrophotometric quantification of its kinetic parameters. Altogether, these observations provide us an insight into the mechanism of action of this global regulator and a better understanding of its functional regulation.
Lattice Codes Achieve the Capacity of Common Message Gaussian Broadcast Channels With Coded Side Information

Lattices possess elegant mathematical properties which have been previously used in the literature to show that structured codes can be efficient in a variety of communication scenarios, including coding for the additive white Gaussian noise channel, dirty-paper channel, Wyner–Ziv coding, coding for relay networks, and so forth. We consider the family of single-transmitter multiple-receiver Gaussian channels, where the source transmits a set of common messages to all the receivers (multicast scenario), and each receiver has <italic>coded side information</italic>, i.e., prior information in the form of linear combinations of the messages. This channel model is motivated by applications to multi-terminal networks, where the nodes may have access to coded versions of the messages from previous signal hops or through orthogonal channels. The capacity of this channel is known and follows from the work of Tuncel (2006), which is based on random coding arguments. In this paper, following the approach of Erez and Zamir, we design lattice codes for this family of channels when the source messages are symbols from a finite field <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{p}$ </tex-math></inline-formula> of prime size. Our coding scheme utilizes Construction A lattices designed over the same prime field <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{p}$ </tex-math></inline-formula>, and uses <italic>algebraic binning</italic> at the decoders to expurgate the channel code and obtain good lattice subcodes, for every possible set of linear combinations available as side information. The achievable rate of our coding scheme is a function of the size <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> of underlying prime field, and approaches the capacity as <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> tends to infinity.
Learned Categorical Perception for Natural Faces

We present an experiment involving learned categorical perception for natural faces, using four diﬀerent category learning tasks. In the four tasks, participants learned to classify faces divided on the basis of gender, hair color, a subjective determined level of ‘trust’, or at random. After category learning, participants rated the similarity of each pair of faces, and their judg-ments were compared to a previously collected ‘base rate’ set of similarities for the same stimuli. Evidence for learned categorical perception was then sought, in the form of increased diﬀerences between intra-category and inter-category similarities. Over the four conditions, we observed no learned categorical perception when categories were based on obvious properties (gender and hair color), nor when the category structure was intentionally random. However, when the loosely deﬁned category structure of trust was employed, a learned categorical perception eﬀect emerged.
Model-driven Development of Virtual Network Embedding Algorithms with Model Transformation and Linear Optimization Techniques

Enhancing the scalability and utilization of data centers, virtualization is a promising technology to manage, develop and operate network functions in a flexible way. For the placement of virtual networks in the data center, many approaches and algorithms are discussed in the literature to optimize the virtual network embedding problem with respect to various optimization goals. This paper presents a new approach for the model-driven specification, simulation-based evaluation, and implementation of possible mappings that respect a set of given constraints and using linear optimization solving techniques to select one optimal mapping. It will be shown that specifying algorithms for the virtual network embedding problem is possible by using a high abstraction level and that the search space to solve the problem can be significantly reduced by defining attribute or structural constraints.
Analyzing the scalability of a social network of agents

Social networks are ever-growing systems by inheritance. The increase in the number nodes in these systems often brings forth the need to add additional functionalities. However due to the distributed nature of social networks, system growth can be a challenging task. Therefore scalability of the system is of vital importance in the design of social networks. This research attempts to establish a comprehensive framework for analysis and validation of requirements and design documents for software systems. In previous work, we applied this framework to analyze the requirements of a social network of agents; expressed using scenario-based specifications. Scenarios are appealing because of their expressive power and simplicity. Moreover due to the clear and concise notation of scenarios, they can be used to analyze the system requirements for general validity, lack of deadlock, and existence of emergent behavior. In this paper a methodology to analyze the scalability of social networks is presented. This methodology is devised to indicate whether or not the new requirements of the system are consistent with the current requirements in place. A larger prototype of a social network of MSA for semantic search is utilized to illustrate the developed methodology.
Travel Time Estimation on a Link without Real-Time Data by Correlated Links

This paper presents a novel method to estimate travel time on a road segment using information from other road segments. This method is useful especially in the case that real-time traffic on such road segment is not available. The proposed method is based on the correlation between the road segment itself and the most related road segment. We measure the relation between road segments by dynamic time warping algorithm and apply the K-Nearest-Neighbor algorithm to select the best neighbor segment to estimate the travel time on the target road segment. We found that the best attributes set that can measure the correlation between road sections consists of location of the road segments, day of the week, and current time. The link correlation results can be used as reference data to determine the travel time on the roads that are related.
Mobile Learning Adoption in Developing Countries

Mobile devices have become attractive learning devices. They provide learners with flexibility and ubiquity to learn anytime and anywhere via wireless Internet. The majority of the existing research on mobile learning adoption has focused primarily on developed countries, with developing countries being largely underexplored. However, the high mobile phone penetration rates in developing countries have provided important opportunities for broadening access to mobile learning in those societies. This paper provides insights into mobile learning adoption in developing countries. It uncovers key issues including the role of social influences, culture, expectations around effort and performance and how the use of M-learning can contribute to innovation skills which are pertinent for the future. It offers valuable implications to educators in developing communities.
Two-stage absorbing Markov chain for salient object detection

We propose a simple but effective approach to detect salient objects by exploring both patch-level and object-level cues under the framework of absorbing Markov chain. Saliency detection is carried out in a two-stage scheme. In the first stage, we conduct random walk on absorbing Markov chain with coarsely selected background seeds in the boundary. The result is integrated with a objectness map which is generated by finding potential object candidates to boost the saliency detection for object completeness. And in the second stage, we use the refined background seeds computed by the first stage as absorbing nodes for the absorbing Markov chain to obtain the final saliency map. Experimental results on four publicly available datasets demonstrate the robustness and efficiency of our proposed approach against 8 state-of-the-art methods in terms of five performance criterions.