Miracle-dz/UniFAR-FTUs huggingface dataset:
Local connections of excitatory neurons in motor-associated cortical areas of the rat

In spite of recent progress in brain sciences, the local circuit of the cerebral neocortex, including motor areas, still remains elusive. Morphological works on excitatory cortical circuitry from thalamocortical (TC) afferents to corticospinal neurons (CSNs) in motor-associated areas are reviewed here. First, TC axons of motor thalamic nuclei have been re-examined by the single-neuron labeling method. There are middle layer (ML)-targeting and layer (L) 1-preferring TC axon types in motor-associated areas, being analogous to core and matrix types, respectively, of Jones (1998) in sensory areas. However, the arborization of core-like motor TC axons spreads widely and disregards the columnar structure that is the basis of information processing in sensory areas, suggesting that motor areas adopt a different information-processing framework such as area-wide laminar organization. Second, L5 CSNs receive local excitatory inputs not only from L2/3 pyramidal neurons but also from ML spiny neurons, the latter directly processing cerebellar information of core-like TC neurons (TCNs). In contrast, basal ganglia information is targeted to apical dendrites of L2/3 and L5 pyramidal neurons through matrix TCNs. Third, L6 corticothalamic neurons (CTNs) are most densely innervated by ML spiny neurons located just above CTNs. Since CTNs receive only weak connections from L2/3 and L5 pyramidal neurons, the TC recurrent circuit composed of TCNs, ML spiny neurons and CTNs appears relatively independent of the results of processing in L2/3 and L5. It is proposed that two circuits sharing the same TC projection and ML neurons are embedded in the neocortex: one includes L2/3 and L5 neurons, processes afferent information in a feedforward way and sends the processed information to other cortical areas and subcortical regions; and the other circuit participates in a dynamical system of the TC recurrent circuit and may serve as the basis of autonomous activity of the neocortex.
Global Transcriptional Changes Following Statin Treatment in Breast Cancer

Background: Statins purportedly exert antitumoral effects, but the underlying mechanisms are currently not fully elucidated. The aim of this study was to explore potential statin-induced effects on global gene expression profiles in primary breast cancer. Experimental Design: This window-of-opportunity phase II trial enrolled 50 newly diagnosed breast cancer patients prescribed atorvastatin (80 mg/day) for 2 weeks presurgically. Pre- and posttreatment tumor samples were analyzed using Significance Analysis of Microarrays (SAM) to identify differentially expressed genes. Similarly, SAM and gene ontology analyses were applied to gene expression data derived from atorvastatin-treated breast cancer cell lines (MCF7, BT474, SKBR3, and MDAMB231) comparing treated and untreated cells. The Systematic Motif Analysis Retrieval Tool (SMART) was used to identify enriched transcription factor-binding sites. Literature Vector Analysis (LitVAn) identified gene module functionality, and pathway analysis was performed using GeneGo Pathways Software (MetaCore; https://portal.genego.com/). Results: Comparative analysis of gene expression profiles in paired clinical samples revealed 407 significantly differentially expressed genes (FDR = 0); 32 upregulated and 375 downregulated genes. Restricted filtration (fold change ≥1.49) resulted in 21 upregulated and 46 downregulated genes. Significantly upregulated genes included DUSP1, RHOB1, GADD45B, and RGS1. Pooled results from gene ontology, LitVAn and SMART analyses identified statin-induced effects on the apoptotic and MAPK pathways among others. Comparative analyses of gene expression profiles in breast cancer cell lines showed significant upregulation of the mevalonate and proapoptotic pathways following atorvastatin treatment. Conclusions: We report potential statin-induced changes in global tumor gene expression profiles, indicating MAPK pathway inhibition and proapoptotic events. Clin Cancer Res; 21(15); 3402–11. ©2015 AACR.
Brain Network Dynamics Underlying Visuospatial Judgment: An fMRI Connectivity Study

Previous functional imaging research has consistently indicated involvement of bilateral fronto-parietal networks during the execution of visuospatial tasks. Studies with TMS have suggested that the right hemispheric network, but not the left, is functionally relevant for visuospatial judgments. However, very little is still known about the interactions within these fronto-parietal networks underlying visuospatial processing. In the current study, we investigated task modulation of functional connectivity (instantaneous correlations of regional time courses), and task-specific effective connectivity (direction of influences), within the right fronto-parietal network activated during visuospatial judgments. Ten healthy volunteers performed a behaviorally controlled visuospatial judgment task (ANGLE) or a control task (COLOR) in an fMRI experiment. Visuospatial task-specific activations were found in posterior parietal cortex (PPC) and middle/inferior frontal gyrus (MFG). Functional connectivity within this network was task-modulated, with significantly higher connectivity between PPC and MFG during ANGLE than during COLOR. Effective connectivity analysis for directed influence revealed that visuospatial task-specific projections within this network were predominantly in a frontal-to-parietal direction. Moreover, ANGLE-specific influences from thalamic nuclei to PPC were identified. Exploratory effective connectivity analysis revealed that closely neighboring clusters, within visuospatial regions, were differentially involved in the network. These neighboring clusters had opposite effective connectivity patterns to other nodes of the fronto-parietal network. Our data thus reveal that visuospatial judgments are supported by massive fronto-parietal backprojections, thalamo-parietal influence, and multiple stages, or loops, of information flow within the visuospatial network. We speculate on possible functional contributions of the various network nodes and informational loops in a neurocognitive model.
A Highly Accurate Prediction Algorithm for Unknown Web Service QoS Values

Quality of service (QoS) guarantee is an important component of service recommendation. Generally, some QoS values of a service are unknown to its users who has never invoked it before, and therefore the accurate prediction of unknown QoS values is significant for the successful deployment of web service-based applications. Collaborative filtering is an important method for predicting missing values, and has thus been widely adopted in the prediction of unknown QoS values. However, collaborative filtering originated from the processing of subjective data, such as movie scores. The QoS data of web services are usually objective, meaning that existing collaborative filtering-based approaches are not always applicable for unknown QoS values. Based on real world web service QoS data and a number of experiments, in this paper, we determine some important characteristics of objective QoS datasets that have never been found before. We propose a prediction algorithm to realize these characteristics, allowing the unknown QoS values to be predicted accurately. Experimental results show that the proposed algorithm predicts unknown web service QoS values more accurately than other existing approaches.
The Arabidopsis Mutant jason Produces Unreduced First Division Restitution Male Gametes through a Parallel/Fused Spindle Mechanism in Meiosis II1[W][OA]

In plants, whole-genome doubling (polyploidization) is a widely occurring process largely contributing to plant evolution and diversification. The generation and fusion of diploid gametes is now considered the major route of plant polyploidization. The parallel arrangement or fusion of meiosis II MII spindles (ps) is one of the most frequently reported mechanisms generating triploid offspring. Through a forward genetics screen of an Arabidopsis (Arabidopsis thaliana) ethyl methanesulfonate population, we identified Arabidopsis thaliana Parallel Spindles1 (AtPS1), which was recently reported as a major gene implicated in the control of the ps meiotic defect. In addition, we describe the isolation and characterization of a novel allele of JASON, involved in male gametophytic ploidy regulation in plants. Similar to atps1 mutants, jason produces more than 25% 2n pollen grains and spontaneously forms triploid offspring. By combining both cytological and genetic approaches, we demonstrate that loss of JASON causes the formation of parallel arranged and fused spindles in male MII, resulting in the production of unreduced first division restitution 2n spores. Although JASON encodes a protein of unknown function, we additionally show that the meiotic ps defect in jason is caused by a reduction in AtPS1 transcript levels, indicating that JASON positively regulates AtPS1 expression, allowing the proper organization and orientation of metaphase II spindle plates in MII.
Neural network–based adaptive composite dynamic surface control for electro-hydraulic system with very low velocity

This article presents a neural network–based adaptive composite dynamic surface control strategy for high-accuracy tracking control of electro-hydraulic system with very low velocity. The input saturation of the servo-proportional valve is tackled by designing a smooth function. Since the unknown nonlinear friction mainly centralizes in the Stribeck effect area, and acts as the dominant factor affecting the control performance, the very low–velocity trajectory is always difficult to be tracked in the usual electro-hydraulic system. To efficiently estimate and compensate the unknown nonlinear friction of the electro-hydraulic system, a serial–parallel estimation model and radial basis function neural network are developed. Dynamic surface control technique is utilized to design the composite controller, and the “explosion of complexity” problem which is inherent in the traditional backstepping method is avoided. Compensating signals are designed to eliminate the effect of the known error caused by the first-order filter in traditional dynamic surface control method. And, the corresponding compensated tracking errors are integrated into the adaptive law to improve the estimation capability of the radial basis function neural network. Simulation and experimental results are given to demonstrate the effectiveness of the proposed control method, and this control method can be extended to other strict-feedback systems with unknown nonlinearity.
The scalable commutativity rule: designing scalable software for multicore processors

What fundamental opportunities for scalability are latent in interfaces, such as system call APIs? Can scalability opportunities be identified even before any implementation exists, simply by considering interface specifications? To answer these questions this paper introduces the following rule: Whenever interface operations commute, they can be implemented in a way that scales. This rule aids developers in building more scalable software starting from interface design and carrying on through implementation, testing, and evaluation. To help developers apply the rule, a new tool named Commuter accepts high-level interface models and generates tests of operations that commute and hence could scale. Using these tests, Commuter can evaluate the scalability of an implementation. We apply Commuter to 18 POSIX calls and use the results to guide the implementation of a new research operating system kernel called sv6. Linux scales for 68% of the 13,664 tests generated by Commuter for these calls, and Commuter finds many problems that have been observed to limit application scalability. sv6 scales for 99% of the tests.
Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks

MOTIVATION Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes; (ii) allows the systematic study of global network dynamics; (iii) is able to cope with uncertainty, both in the data and the model selection; and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes. RESULTS We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases. Then, we discuss the relationship between PBNs and Bayesian networks--a family of graphical models that explicitly represent probabilistic relationships between variables. We show how probabilistic dependencies between a gene and its parent genes, constituting the basic building blocks of Bayesian networks, can be obtained from PBNs. Finally, we present methods for quantifying the influence of genes on other genes, within the context of PBNs. Examples illustrating the above concepts are presented throughout the paper.
Application of a Loop-Type Laboratory Biofilm Reactor to the Evaluation of Biofilm for Some Metallic Materials and Polymers such as Urinary Stents and Catheters

A laboratory biofilm reactor (LBR) was modified to a new loop-type closed system in order to evaluate novel stents and catheter materials using 3D optical microscopy and Raman spectroscopy. Two metallic specimens, pure nickel and cupronickel (80% Cu-20% Ni), along with two polymers, silicone and polyurethane, were chosen as examples to ratify the system. Each set of specimens was assigned to the LBR using either tap water or an NB (Nutrient broth based on peptone from animal foods and beef extract mainly)—cultured solution with E-coli formed over 48–72 h. The specimens were then analyzed using Raman Spectroscopy. 3D optical microscopy was employed to corroborate the Raman Spectroscopy results for only the metallic specimens since the inherent roughness of the polymer specimens made such measurements difficult. The findings suggest that the closed loop-type LBR together with Raman spectroscopy analysis is a useful method for evaluating biomaterials as a potential urinary system.
Max-Matching Context Kernel Design for SIFT Feature

In object matching and recognition it is useful to represent an image with the sets of local features such as SIFT etc. However this representation poses a challenge to the popular SVM machine learning method, since it needs ordered and fixlength data. To solve this problem, we focus in this paper on a Max-matching context kernel, which computes Max-matching points based on the angle between two points of sets and sums its context energy to Max-matching point energy. We prove Maxmatching context kernel yields a Mercer kernel. We demonstrate our algorithm on Caltech 101's object recognition, which shows that the proposed method is accurate and significantly more efficient than current other approaches. Keywords-kernel,SIFT,SVM, Object recognition.
Influence of Residual Stress on the Thermal Expansion Behavior of Electroformed Nickel Layers

Nickel layers with tensile or pressure residual stress were prepared by electroforming technique from two kinds of electrolyte. Subsequent heat treatment was adopted to get the stress released. The coefficients of thermal expansion (CTEs) were measured with a thermal dilatometer and the relationship between residual stress and the measured CTEs was revealed both from experimental results and theoretical analysis.
Collaborative Performance: Addressing the ROI of Collaboration

Collaboration is gaining attention as a key driver of overall business performance, innovation capabilities and productivity. However, there is a discrepancy between the perceived importance of collaboration and the extent to which companies approach collaboration in a structured manner. Few companies methodically evaluate how well they perform in the area of collaboration, and few have implemented management and leadership principles to systematically improve collaborative performance. This article explains how businesses can benefit from systematic, structured investment in tools and methods supporting collaboration, and concludes with a few governing principles and a list of specific action points for businesses that are interested in improving their collaborative performance and obtaining a higher Return on Investment (ROI) on their collaboration initiatives.
Dimension Extractors

A dimension extractor is an algorithm designed to increase the effective dimension – i.e., the computational information density – of an infinite sequence. A constructive dimension extractor is exhibited by showing that every sequence of positive constructive dimension is Turing equivalent to a sequence of constructive strong dimension arbitrarily close to 1. Similar results are shown for computable dimension and truth-table equivalence, and for pispace dimension and pispace Turing equivalence, where pispace represents Lutz’s hierarchy of super-polynomial space bounds. Thus, with respect to constructive, computable, and pispace information density, any sequence in which almost every prefix has information density bounded away from zero can be used to compute a sequence in which infinitely many prefixes have information density that is nearly maximal. In the constructive dimension case, the reduction is uniform with respect to the input sequence: a single oracle Turing machine, taking as input a rational upper bound on the dimension of the input sequence, works for every input sequence of positive constructive dimension. As an application, the resource-bounded extractors are used to characterize the computable dimension of individual sequences in terms of compression via truthtable reductions and to characterize the pispace dimension of individual sequences in terms of compression via pispace-bounded Turing reductions, in analogy to previous known results connecting effective dimensions to compression with effective reductions. ∗This research was funded in part by grant number 9972653 from the National Science Foundation as part of their Integrative Graduate Education and Research Traineeship (IGERT) program.
Balance the Trade-Off between the Accessibility and Performance of Distributed Routing Schemes in Multi-Hop Wireless Networks

In this paper, a general framework, called probability-based solution (PBS), is proposed to balance the trade-off between the accessibility (in terms of the success probability to find a route reaching the destination) and the performance (in terms of spectral efficiency, outage probability or energy consumption) of distributed routing schemes in multi-hop wireless networks. In the PBS, the candidate nodes (receivers) in each hop are first separated into two groups based on a direction either diverging from, or converging to, the direct line between the source and destination. Then, one of the best nodes from the two groups is selected according to a carefully-defined probability by considering the uncertainty in the subsequent hops. The simulation results demonstrate that integrating the proposed PBS with conventional distributed routing schemes can significantly improve the accessibility and effectively guarantee other system performance. Moreover, the PBS can also be combined with facing routing to solve the connectivity hole problem.
The free-form light stage

The Free-form Light Stage captures the reflectance field of an object using a freely movable, hand-held light source. By photographing the object under different illumination conditions, we are able to render the object under any lighting condition, using a linear combination of basis images. Our technique builds on recent techniques, such as the Light Stage [Debevec et al. 2000], where light sources are placed at fixed and known positions (e.g. using a gantry). We remove this limitation, and put no restrictions on light source placement.
Default probability calculation model based on credit spread

The main contribution of the paper is it puts forward a model to calculate the default probability of corporation according to the credit spread of corporate bond. The term structure premium of T−1 years is measured by the difference between yields to maturity of T years and 1 year on the yield curve of corporate bonds with the same credit rating. The credit spread is measured by the yield to maturity of corporate bond of T years minus the term structure premium of T−1 years and the risk free interest rate. The default probability of corporation is calculated according to the credit spread and the loss given default of corporate bond. The empirical study shows that the default probabilities calculated with the model are consistent with the credit rating orders by the Moody's company, which verifies the rationality of the model.
Simultaneous Visualization of Peroxisomes and Cytoskeletal Elements Reveals Actin and Not Microtubule-Based Peroxisome Motility in Plants1,212

Peroxisomes were visualized in living plant cells using a yellow fluorescent protein tagged with a peroxisomal targeting signal consisting of the SKL motif. Simultaneous visualization of peroxisomes and microfilaments/microtubules was accomplished in onion (Allium cepa) epidermal cells transiently expressing the yellow fluorescent protein-peroxi construct, a green fluorescent protein-mTalin construct that labels filamentous-actin filaments, and a green fluorescent protein-microtubule-binding domain construct that labels microtubules. The covisualization of peroxisomes and cytoskeletal elements revealed that, contrary to the reports from animal cells, peroxisomes in plants appear to associate with actin filaments and not microtubules. That peroxisome movement is actin based was shown by pharmacological studies. For this analysis we used onion epidermal cells and various cell types of Arabidopsis including trichomes, root hairs, and root cortex cells exhibiting different modes of growth. In transient onion epidermis assay and in transgenic Arabidopsis plants, an interference with the actin cytoskeleton resulted in progressive loss of saltatory movement followed by the aggregation and a complete cessation of peroxisome motility within 30 min of drug application. Microtubule depolymerization or stabilization had no effect.
TripSense: A Trust-Based Vehicular Platoon Crowdsensing Scheme with Privacy Preservation in VANETs

In this paper, we propose a trust-based vehicular platoon crowdsensing scheme, named TripSense, in VANET. The proposed TripSense scheme introduces a trust-based system to evaluate vehicles’ sensing abilities and then selects the more capable vehicles in order to improve sensing results accuracy. In addition, the sensing tasks are accomplished by platoon member vehicles and preprocessed by platoon head vehicles before the data are uploaded to server. Hence, it is less time-consuming and more efficient compared with the way where the data are submitted by individual platoon member vehicles. Hence it is more suitable in ephemeral networks like VANET. Moreover, our proposed TripSense scheme integrates unlinkable pseudo-ID techniques to achieve PM vehicle identity privacy, and employs a privacy-preserving sensing vehicle selection scheme without involving the PM vehicle’s trust score to keep its location privacy. Detailed security analysis shows that our proposed TripSense scheme not only achieves desirable privacy requirements but also resists against attacks launched by adversaries. In addition, extensive simulations are conducted to show the correctness and effectiveness of our proposed scheme.