Miracle-dz/UniFAR-FTUs huggingface dataset:
m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks

As the most prevalent mammalian mRNA epigenetic modification, N6-methyladenosine (m6A) has been shown to possess important post-transcriptional regulatory functions. However, the regulatory mechanisms and functional circuits of m6A are still largely elusive. To help unveil the regulatory circuitry mediated by mRNA m6A methylation, we develop here m6A-Driver, an algorithm for predicting m6A-driven genes and associated networks, whose functional interactions are likely to be actively modulated by m6A methylation under a specific condition. Specifically, m6A-Driver integrates the PPI network and the predicted differential m6A methylation sites from methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data using a Random Walk with Restart (RWR) algorithm and then builds a consensus m6A-driven network of m6A-driven genes. To evaluate the performance, we applied m6A-Driver to build the context-specific m6A-driven networks for 4 known m6A (de)methylases, i.e., FTO, METTL3, METTL14 and WTAP. Our results suggest that m6A-Driver can robustly and efficiently identify m6A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m6A methylated genes. Pathway analysis of the constructed context-specific m6A-driven gene networks further revealed the regulatory circuitry underlying the dynamic interplays between the methyltransferases and demethylase at the epitranscriptomic layer of gene regulation.
Psychiatric and physical comorbidities and pain in patients with multiple sclerosis

Background It has been observed that patients with multiple sclerosis (MS), who have psychiatric and physical comorbidities such as depression and COPD, have an increased risk of experiencing more pain. In this study, we have distinguished between pain intensity and pain affect, as the latter, particularly, requires treatment. Furthermore, while pain and comorbidities have been assessed using questionnaires, this is possibly a less reliable method for those who are cognitively vulnerable. Objective The aim of this study was to determine whether psychiatric and physical comorbidities can predict pain intensity and pain affect in MS patients, susceptible to cognitive impairment. Methods Ninety-four patients with MS and 80 control participants participated in this cross-sectional study. Besides depression and anxiety, 47 additional comorbidities were extracted from patients’ medical records. Depression and anxiety were assessed using the Beck Depression Inventory and the Symptom Check List-90. Pain was assessed using the Number of Words Chosen Affective, Coloured Analog Scale, and the Faces Pain Scale. Cognitive functions, for example, memory and executive functions, were assessed using several neuropsychological tests. Results The main findings indicate that psychiatric comorbidities (depression and anxiety) predict both pain intensity and pain affect and that total physical comorbidity predicts only pain affect in MS patients, susceptible to cognitive impairment. Conclusion Both psychiatric and physical comorbidities predict pain affect. All three clinical outcomes enhance MS patients’ suffering.
Person re-identification by using a method combining DPM and SVM

Person re-identification refers to pedestrian matching under the surveillance of non-overlapping multi-camera, which is usually used in criminal investigation work and image retrieval. Many metric learning methods have achieved good results, but a single global metric learning is difficult to fit discriminant features. Aiming at the person re-identification under single shot, this paper proposes a new method on the basis of metric methods and classifiers, cutting pedestrians into parts and performing local metric learning on each subset of component, meanwhile, carrying out global metric learning combined with SVM classifier in the entire training set. The experimental results show that the proposed method improves the performance of global metric learning method.
Efficient SpiNNaker simulation of a heteroassociative memory using the Neural Engineering Framework

The biological brain is a highly plastic system within which the efficacy and structure of synaptic connections are constantly changing in response to internal and external stimuli. While numerous models of this plastic behavior exist at various levels of abstraction, how these mechanisms allow the brain to learn meaningful values is unclear. The Neural Engineering Framework (NEF) is a hypothesis about how large-scale neural systems represent values using populations of spiking neurons, and transform them using functions implemented by the synaptic weights between populations. By exploiting the fact that these connection weight matrices are factorable, we have recently shown that static NEF models can be simulated very efficiently using the SpiNNaker neuromorphic architecture. In this paper, we demonstrate how this approach can be extended to efficiently support both supervised and unsupervised learning rules designed to operate on these factored matrices. We then present a heteroassociative memory architecture built using these learning rules and prove that it is capable of learning a human-scale semantic network. Finally we demonstrate a 100 000 neuron version of this architecture running on the SpiNNaker simulator with a speed-up exceeding 150x when compared to the Nengo reference simulator.
The inhibitory input to mouse cerebellar Purkinje cells is reciprocally modulated by Bergmann glial P2Y1 and AMPA receptor signaling

Synaptic transmission has been shown to be modulated by glial functions, but the modes of specific glial action may vary in different neural circuits. We have tested the hypothesis, if Bergmann GLIA (BG) are involved in shaping neuronal communication in the mouse cerebellar cortex, using acutely isolated cerebellar slices of wild‐type (WT) and of glia‐specific receptor knockout mice. Activation of P2Y1 receptors by ADP (100 µM) or glutamatergic receptors by AMPA (0.3 µM) resulted in a robust, reversible and repeatable rise of evoked inhibitory input in Purkinje cells by 80% and 150%, respectively. The ADP‐induced response was suppressed by prior application of AMPA, and the AMPA‐induced response was suppressed by prior application of ADP. Genetic deletion or pharmacological blockade of either receptor restored the response to the other receptor agonist. Both ADP and AMPA responses were sensitive to Rose Bengal, which blocks vesicular glutamate uptake, and to the NMDA receptor antagonist D‐AP5. Our results provide strong evidence that activation of both ADP and AMPA receptors, located on BGs, results in the release of glutamate, which in turn activates inhibitory interneurons via NMDA‐type glutamate receptors. This infers that BG cells, by means of metabotropic signaling via their AMPA and P2Y1 receptors, which mutually suppress each other, would interdependently contribute to the fine‐tuning of Purkinje cell activity in the cerebellar cortex. GLIA 2016. GLIA 2016;64:1265–1280
Anti‐human neutrophil antigen‐1a, ‐1b, and ‐2 antibodies in neonates and children with immune neutropenias analyzed by extracted granulocyte antigen immunofluorescence assay

Anti‐human neutrophil antigen (HNA) antibodies have been implicated in the development of neonatal alloimmune neutropenia (NAN) and autoimmune neutropenia (AIN). There are many conventional assay methods that detect anti‐HNA antibodies. However, a method to measure multiple samples and detect several anti‐HNA antibodies simultaneously is needed.
Reconfigurable, Wideband, Low-Profile, Circularly Polarized Antenna and Array Enabled by an Artificial Magnetic Conductor Ground

A reconfigurable, wideband, and low-profile circular polarization (CP) antenna is presented. Its wideband CP reconfigurability is realized by incorporating RF switches into a cross-bowtie radiator. A compact, wide bandwidth, and polarization-independent artificial magnetic conductor ground plane is developed to minimize the overall profile of the antenna while maintaining its wide bandwidth. The simplicity of this single-element design facilitates the realization of a reconfigurable, wide bandwidth CP array that achieves higher directivity without changing its overall profile. Prototypes of the single element and of a <inline-formula> <tex-math notation="LaTeX">$1 \times 4$ </tex-math></inline-formula> array of these elements were fabricated and tested. The measured results for both prototypes are in good agreement with their simulated values, validating their design principles. They are low profile with a height <inline-formula> <tex-math notation="LaTeX">$\sim 0.05\lambda _{\mathrm {\mathbf {0}}}$ </tex-math></inline-formula>. The array exhibits a wide fractional operational bandwidth: 1.65 GHz (21.7%), and a high realized gain: 13 dBic. Since they would enhance their channel capacity and avoid polarization mismatch issues, these reconfigurable CP antenna systems are very suitable for modern wireless systems.
Forces on Halves of a Uniformly Magnetized Sphere

Show that the force between hemispheres in the left figure is the same as that between one such hemisphere and either a perfectly conducting plate, or a plate of infinite magnetic permeability. This problem was inspired by e-discussions with J. Castro Paredes. The force between two rectangular-prism permanent magnets has been deduced in [1]. For cylindrical magnets, see [2, 3], and for spheres, see [4].
Identification of Ras suppressor-1 (RSU-1) as a potential breast cancer metastasis biomarker using a three-dimensional in vitro approach

Breast cancer (BC) is the most common malignant disease in women, with most patients dying from metastasis to distant organs, making discovery of novel metastasis biomarkers and therapeutic targets imperative. Extracellular matrix (ECM)-related adhesion proteins as well as tumor matrix stiffness are important determinants for metastasis. As traditional two-dimensional culture does not take into account ECM stiffness, we employed 3-dimensional collagen I gels of increasing concentration and stiffness to embed BC cells of different invasiveness (MCF-7, MDA-MB-231 and MDA-MB-231-LM2) or tumor spheroids. We tested the expression of cell-ECM adhesion proteins and found that Ras Suppressor-1 (RSU-1) is significantly upregulated in increased stiffness conditions. Interestingly, RSU-1 siRNA-mediated silencing inhibited Urokinase Plasminogen Activator, and metalloproteinase-13, whereas tumor spheroids formed from RSU-1-depleted cells lost their invasive capacity in all cell lines and stiffness conditions. Kaplan-Meier survival plot analysis corroborated our findings showing that high RSU-1 expression is associated with poor prognosis for distant metastasis-free and remission-free survival in BC patients. Taken together, our results indicate the important role of RSU-1 in BC metastasis and set the foundations for its validation as potential BC metastasis marker.
The case for a multi-methodological, cross-disciplinary approach to the analysis of ICT investment and projects in the developing world

This article makes the case for greater collaboration and communication between scholars and practitioners of information communications technology (ICT) for development projects and for the use of multi-methodological, cross-disciplinary approaches to understand what will make for more successful endeavors in this important arena, the necessity of which is reflected in the widespread use of public-private partnerships. To this end, we utilize both macro- and micro-level analyses to examine ICT investment in the developing world. The first research question—can ICT investment be shown to contribute to economic growth? —is addressed through econometric analysis. Though our model finds evidence to the affirmative, macro-level models often do not account for how ICT funds are employed, differentiate growth effects, or discern the characteristics of specific initiatives that make them more or less likely to succeed. This is where the case-study level of analysis becomes essential. This approach is used to address our second research question: whether successful strategies found among case studies can better inform policy prescription. To this end, we present a case study of a project based in rural Vietnam, followed by a summary of findings that unifies the lessons learned. In this way we intend to shed light on policy prescription, improve the outcomes of ICT initiatives, and present a first step in the mixed-methodological, cross-disciplinary direction. © 2008 Wiley Periodicals, Inc.
Prior Thermal Injury Accelerates Endotoxin-Induced Inflammatory Cytokine Production and Intestinal Nuclear Factor-&kgr;B Activation in Mice

The objective of this study was to increase the understanding of the “second-hit” response in thermal injury. The authors hypothesized that prior thermal injury increases the endotoxin-induced inflammatory response of intestinal mucosa. Mice underwent sham or 25% TBSA scald injury. Seven days after injury, mice were injected with lipopolysaccharide. Blood, jejunum, and colon specimens were obtained at intervals. Serum, jejunal, and colon inflammatory cytokine levels were measured by enzyme-linked immunosorbent assay. Jejunal and colon nuclear factor (NF)-&kgr;B activation was measured by electrophoretic mobility shift assay. After remote thermal injury, lipopolysaccharide exposure led to an acute increase in serum interleukin (IL)-6, IL-10, and chemokine keratinocyte-derived chemokine (KC) levels. This correlated with lipopolysaccharide-induced increased IL-6 in colon and chemokine KC in the jejunum and colon in burned mice when compared with sham-injured mice. Lipopolysaccharide-induced NF-&kgr;B activation occurred more rapidly in jejunum and colon from burned mice compared with sham-injured mice. Prior thermal injury accelerates lipopolysaccharide-induced inflammatory cytokine production systemically in jejunum and colon. The “second hit” of lipopolysaccharide led to earlier intestinal NF-&kgr;B activation in burned mice compared with sham-injured mice. These results indicate that there is a heightened inflammatory response by jejunum and colon in response to a “second hit” of lipopolysaccharide after burn injury.
Optimal Task Allocation in Near-Far Computing Enhanced C-RAN for Wireless Big Data Processing

With the increasing popularity of user equipments, the corresponding UE generated big data (UGBD) is also growing substantially, which makes both UEs and current network structures struggle to process those data and applications. This article proposes a near-far computing enhanced C-RAN (NFC-RAN) architecture that can better process big data and its corresponding applications. NFC-RAN is composed of near edge computing (NEC) and far edge computing (FEC) units. NEC is located in the remote radio head,, which can quickly respond to delay-sensitive tasks from the UEs, while FEC sits next to a baseband unit pool, which can do other computation-intensive tasks. Task allocation between NEC and FEC is introduced in this article. Also, WiFi indoor positioning is illustrated as a case study of the proposed architecture. Moreover, simulation and experiment results are provided to show the effectiveness of the proposed task allocation and architecture.
Centrality-based bipartite local community detection algorithm

As a kind of typical complex network, bipartite network has already received many specialized research. Local community detection is very useful for bipartite network, but it has not yet been systematically researched. To guarantee equivalent partitioning (obtaining coincident community structure starting from arbitrary node) for two category nodes in bipartite network, a centrality-based bipartite local community detection (CBLCD) algorithm is proposed inspired by water surface undulation. The algorithm first gets local centralized subgraphs/subnetworks according to resource allocation index R defined in this paper, and then expands and combines these subnetworks, until the ultimate local communities are detected. The experimental results on some typical network datasets show that the algorithm achieved both good accuracy and stability.
Circularly Polarized Horns Based on Standard Horns and a Metasurface Polarizer

A method for designing circularly polarized (CP) horn antenna is proposed, which is based on a standard pyramidal horn with a linear-to-circular polarization transformer. A compact transparent metasurface transformer is first designed to perform linear-to-circular polarization transformation, and then this is inserted in a linearly polarized standard pyramidal horn to create a CP horn. To validate the CP horn design, both simulations and experiments were conducted and good agreement was obtained. The CP performance achieved a 3 dB axial-ratio bandwidth of 7.4%, a maximum gain reduction of 1.1 dB without altering the horn's original size for a light increase in weight and at a small cost increase.
Finite element-based uncertainty analysis of deteriorated structures by perturbation techniques

The paper is focused on the uncertainty analysis of reinforced concrete beams in aggressive environments. In particular, the case of reinforcement corrosion due to chlorides is considered. The probabilistic investigation is performed by means of the perturbation method. The time of corrosion initiation and the failure load are expressed as Taylor series expansions in terms of the input random variables. The material resistances of concrete and reinforcement steel, the concrete cover, the diffusion properties of the concrete and the environmental conditions are modeled by random variables. The Taylor series expansion of the structural response is derived from the non-linear FE model, which is used to simulate the behavior of corroded structures.
Control of Femtosecond Laser Ablation of Thin Films from a Dielectric Surface by Nonlinear Interaction with the Substrate

Laser ablation is potentially important for nanofabrication but can suffer from poor reproducibility, as it is highly sensitive to even small fluctuations in the laser energy. The authors exploit nonlinear effects in the propagation of high-intensity light through transparent media to control ablation of ultrathin (8 nm) polymer films, achieving subwavelength resolution and a tolerance to energy fluctuations that allows high reproducibility. They also show conversely how thin-film laser ablation can be used to profile laser beams undergoing self-focusing and filamentation.
Face recognition based on convolution siamese networks

In this paper, we use convolutional siamese networks for face recognition. Face recognition is one of the non-contact biometrics and widely applied in many circumstances. As the advent of artificial intelligence era, the demands on accuracy and robustness get greater and greater at present. Due to its low accuracy and restrictions in many conditions, it's hard for traditional methods to satisfy the demands. In this paper we propose a new network with convolutional siamese architecture for face recognition. Like some common face recognition systems, we use face detection to get the position of the face in an image. The deep learning methods are used to exploit the feature of the face. We finish the comparison of the detected face and the faces in the database. The experiments demonstrate a very good performance compared to other methods.
Leverage and the Maturity Structure of Debt in Emerging Markets

The aim of this paper is to analyse for a multi-country large emerging market sample the choice between debt and equity simultaneously with the decision between short- and long-term debts. In order to investigate the joint decision among leverage and maturity, we examine an unique sample of 986 firms and 13,490 firm-year observations from Latin America and 686 firms and 7919 firm-year observations from Eastern Europe for the period 1990-2003. We employ dynamic panel data analysis using Generalized Method of moments. The empirical results support three main findings. First, the cross-effects between leverage and maturity behave exactly the opposite between Latin America and Eastern Europe sub-samples. Capital structure and debt maturity are policy complements in Latin America and substitutes in Eastern Europe. Second, there is a significant dynamic effects component in the determination of leverage and maturity. Finally, adjustment to the target maturity is by no means costless and instantaneous with firm facing moderate adjustment costs.
Evidence Base Update for Psychosocial Treatments for Children and Adolescents Exposed to Traumatic Events

Child and adolescent trauma exposure is prevalent, with trauma exposure-related symptoms, including posttraumatic stress, depressive, and anxiety symptoms often causing substantial impairment. This article updates the evidence base on psychosocial treatments for child and adolescent trauma exposure completed for this journal by Silverman et al. (2008). For this review, we focus on 37 studies conducted during the seven years since the last review. Treatments are grouped by overall treatment family (e.g., cognitive behavioral therapy), treatment modality (e.g., individual vs. group), and treatment participants (e.g., child only vs. child and parent). All studies were evaluated for methodological rigor according to Journal of Clinical Child & Adolescent Psychology evidence-based treatment evaluation criteria (Southam-Gerow & Prinstein, 2014), with cumulative designations for level of support for each treatment family. Individual CBT with parent involvement, individual CBT, and group CBT were deemed well-established; group CBT with parent involvement and eye movement desensitization and reprocessing (EMDR) were deemed probably efficacious; individual integrated therapy for complex trauma and group mind–body skills were deemed possibly efficacious; individual client-centered play therapy, individual mind–body skills, and individual psychoanalysis were deemed experimental; and group creative expressive + CBT was deemed questionable efficacy. Advances in the evidence base, with comparisons to the state of the science at the time of the Silverman et al. (2008) review, are discussed. Finally, we present dissemination and implementation challenges and areas for future research.
Constrained Private Mechanisms for Count Data

Concern about how to aggregate sensitive user data without compromising individual privacy is a major barrier to greater availability of data. Differential privacy has emerged as an accepted model to release sensitive information while giving a statistical guarantee for privacy. Many different algorithms are possible to address different target functions. We focus on the core problem of count queries, and seek to design mechanisms to release data associated with a group of n individuals. Prior work has focused on designing mechanisms by raw optimization of a loss function, without regard to the consequences on the results. This can leads to mechanisms with undesirable properties, such as never reporting some outputs (gaps), and overreporting others (spikes). We tame these pathological behaviors by introducing a set of desirable properties that mechanisms can obey. Any combination of these can be satisfied by solving a linear program (LP) which minimizes a cost function, with constraints enforcing the properties. We focus on a particular cost function, and provide explicit constructions that are optimal for certain combinations of properties, and show a closed form for their cost. In the end, there are only a handful of distinct optimal mechanisms to choose between: one is the well-known (truncated) geometric mechanism; the second a novel mechanism that we introduce here, and the remainder are found as the solution to particular LPs. These all avoid the bad behaviors we identify. We demonstrate in a set of experiments on real and synthetic data which is preferable in practice, for different combinations of data distributions, constraints, and privacy parameters.