Miracle-dz/UniFAR-FTUs huggingface dataset:
PBX3 promotes migration and invasion of colorectal cancer cells via activation of MAPK/ERK signaling pathway.

AIM To investigate the role of pre-B-cell leukemia homeobox (PBX)3 in migration and invasion of colorectal cancer (CRC) cells. METHODS We detected PBX3 expression in five cell lines and surgical specimens from 111 patients with CRC using real-time reverse transcription-polymerase chain reaction. We forced expression of PBX3 in low metastatic HT-29 and SW480 cells and knocked down expression of PBX3 in highly metastatic LOVO and HCT-8 cells. Wound healing and Boyden chamber assays were used to detect cell migration and invasion after altered expression of PBX3. Western blot was performed to detect the change of signaling molecule ERK1/2 following PBX3 overexpression. RESULTS High level of PBX3 expression was correlated with the invasive potential of CRC cells, and significantly associated with lymph node invasion (P = 0.02), distant metastasis (P = 0.04), advanced TNM stage (P = 0.03) and poor overall survival of patients (P < 0.05). Ectopic expression of PBX3 in low metastatic cells was shown to promote migration and invasion, while inhibited PBX3 expression in highly metastatic cells suppressed migration and invasion. Furthermore, upregulation of phosphorylated extracellular signal-regulated kinase (ERK)1/2 was found to be one of the targeted molecules responsible for PBX3-induced CRC cell migration and invasion. CONCLUSION PBX3 induces invasion and metastasis of CRC cells partially through activation of the MAPK/ERK signaling pathway.
A natural human IgM that binds to gangliosides is therapeutic in murine models of amyotrophic lateral sclerosis

ABSTRACT Amyotrophic lateral sclerosis (ALS) is a devastating, fatal neurological disease that primarily affects spinal cord anterior horn cells and their axons for which there is no treatment. Here we report the use of a recombinant natural human IgM that binds to the surface of neurons and supports neurite extension, rHIgM12, as a therapeutic strategy in murine models of human ALS. A single 200 µg intraperitoneal dose of rHIgM12 increases survival in two independent genetic-based mutant SOD1 mouse strains (SOD1G86R and SOD1G93A) by 8 and 10 days, delays the onset of neurological deficits by 16 days, delays the onset of weight loss by 5 days, and preserves spinal cord axons and anterior horn neurons. Immuno-overlay of thin layer chromatography and surface plasmon resonance show that rHIgM12 binds with high affinity to the complex gangliosides GD1a and GT1b. Addition of rHIgM12 to neurons in culture increases α-tubulin tyrosination levels, suggesting an alteration of microtubule dynamics. We previously reported that a single peripheral dose of rHIgM12 preserved neurological function in a murine model of demyelination with axon loss. Because rHIgM12 improves three different models of neurological disease, we propose that the IgM might act late in the cascade of neuronal stress and/or death by a broad mechanism. Summary: A single peripheral dose of a recombinant natural human IgM increases lifespan and delays neurological deficits in mouse models of human ALS.
The Evolution of Fungal Metabolic Pathways

Fungi contain a remarkable range of metabolic pathways, sometimes encoded by gene clusters, enabling them to digest most organic matter and synthesize an array of potent small molecules. Although metabolism is fundamental to the fungal lifestyle, we still know little about how major evolutionary processes, such as gene duplication (GD) and horizontal gene transfer (HGT), have interacted with clustered and non-clustered fungal metabolic pathways to give rise to this metabolic versatility. We examined the synteny and evolutionary history of 247,202 fungal genes encoding enzymes that catalyze 875 distinct metabolic reactions from 130 pathways in 208 diverse genomes. We found that gene clustering varied greatly with respect to metabolic category and lineage; for example, clustered genes in Saccharomycotina yeasts were overrepresented in nucleotide metabolism, whereas clustered genes in Pezizomycotina were more common in lipid and amino acid metabolism. The effects of both GD and HGT were more pronounced in clustered genes than in their non-clustered counterparts and were differentially distributed across fungal lineages; specifically, GD, which was an order of magnitude more abundant than HGT, was most frequently observed in Agaricomycetes, whereas HGT was much more prevalent in Pezizomycotina. The effect of HGT in some Pezizomycotina was particularly strong; for example, we identified 111 HGT events associated with the 15 Aspergillus genomes, which sharply contrasts with the 60 HGT events detected for the 48 genomes from the entire Saccharomycotina subphylum. Finally, the impact of GD within a metabolic category was typically consistent across all fungal lineages, whereas the impact of HGT was variable. These results indicate that GD is the dominant process underlying fungal metabolic diversity, whereas HGT is episodic and acts in a category- or lineage-specific manner. Both processes have a greater impact on clustered genes, suggesting that metabolic gene clusters represent hotspots for the generation of fungal metabolic diversity.
VMTP: Versatile Message Transaction Protocol: Protocol specification

This memo specifies the Versatile Message Transaction Protocol (VMTP) [Version 0.7 of 19-Feb-88], a transport protocol specifically designed to support the transaction model of communication, as exemplified by remote procedure call (RPC). The full function of VMTP, including support for security, real-time, asynchronous message exchanges, streaming, multicast and idempotency, provides a rich selection to the VMTP user level. Subsettability allows the VMTP module for particular clients and servers to be specialized and simplified to the services actually required. Examples of such simple clients and servers include PROM network bootload programs, network boot servers, data sensors and simple controllers, to mention but a few examples. This RFC describes a protocol proposed as a standard for the Internet community.
MicroRNA Expression Signatures in Barrett's Esophagus and Esophageal Adenocarcinoma

Purpose: Esophageal adenocarcinoma is a highly aggressive malignancy that frequently develops from Barrett's esophagus, a premalignant pathologic change occurring in the lower end of the esophagus. Identifying Barrett's esophagus patients at high risk of malignant transformation is essential to the prevention of esophageal adenocarcinoma. Although microRNA (miRNA) expression signatures have been associated with the etiology and prognosis of several types of cancers, their roles in the development of esophageal adenocarcinoma have not been extensively evaluated. Experimental Design: In this study, we analyzed the expression patterns of 470 human miRNAs using Agilent miRNA microarray in 32 disease/normal-paired tissues from 16 patients diagnosed with Barrett's esophagus of either low- or high-grade dysplasia, or esophageal adenocarcinoma. Results: Using unsupervised hierarchical clustering and class comparison analyses, we found that miRNA expression profiles in tissues of Barrett's esophagus with high-grade dysplasia were significantly different from their corresponding normal tissues. Similar findings were observed for esophageal adenocarcinoma, but not for Barrett's esophagus with low-grade dysplasia. The expression patterns of selected miRNAs were further validated using quantitative reverse transcription real-time PCR in an independent set of 75 pairs of disease/normal tissues. Finally, we identified several miRNAs that were involved in the progressions from low grade-dysplasia Barrett's esophagus to esophageal adenocarcinoma. Conclusions: We showed that miRNAs were involved in the development and progression of esophageal adenocarcinoma. The identified significant miRNAs that may become potential targets for early detection, chemoprevention, and treatment of esophageal cancer. (Clin Cancer Res 2009;15(18):5744–52)
DeepFlow: Deep learning-based malware detection by mining Android application for abnormal usage of sensitive data

The open nature of Android allows application developers to take full advantage of the system. While the flexibility is brought to developers and users, it may raise significant issues related to malicious applications. Traditional malware detection approaches based on signatures or abnormal behaviors are invalid when dealing with novel malware. To solve the problem, machine learning algorithms are used to learn the distinctions between malware and benign apps automatically. Deep learning, as a new area of machine learning, is developing rapidly as its better characterization of samples. We thus propose DeepFlow, a novel deep learning-based approach for identifying malware directly from the data flows in the Android application. We test DeepFlow on thousands of benignware and malware. The results show that DeepFlow can achieve a high detection F1 score of 95.05%, outperforming traditional machine learning-based approaches, which reveals the advantage of deep learning technique in malware detection.
Ensembles of Neural Networks Based on the Alteration of Input Feature Values

An ensemble performs well when the component classifiers are diverse yet accurate, so that the failure of one is compensated for by others. A number of methods have been investigated for constructing ensemble in which some of them train classifiers with the generated patterns. This study investigates a new technique of training pattern generation. The method alters input feature values of some patterns using the values of other patterns to generate different patterns for different classifiers. The effectiveness of neural network ensemble based on the proposed technique was evaluated using a suite of 25 benchmark classification problems, and was found to achieve performance better than or competitive with related conventional methods. Experimental investigation of different input values alteration techniques finds that alteration with pattern values in the same class is better for generalization, although other alteration techniques may offer more diversity.
Concoqtion: Mixing Indexed Types and Hindley-Milner Type Inference

This paper addresses the question of how to extend OCaml’s Hindley-Milner type system with types indexed by logical propositions and proofs of the Coq theorem prover, thereby providing an expressive and extensible mechanism for ensuringfine-grained program invariants. We propose adopting the approached used by Shao et al. for certified binaries. This approach maintains a phase distinction between the computational and logical languages, thereby limiting effects and non-termination to the computational language, and maintaining the decidability of the type system. The extension subsumes language features such as impredicative first-class (higher-rank) polymorphism and type operators, that are notoriously difficult to integrate with the Hindley-Milner style of type inference that is used in OCaml. We make the observation that these features can be more easily integrated with type inference if the inference algorithm is free to adapt the order in which it solves typing constraints to each program. To this end we define a novel “order-free” type inference algorithm. The key enabling technology is a graph representation of constraints and a constraint solver that performs Hindley-Milner inference with just three graph rewrite rules.
Cooperative action of Sox9, Snail2 and PKA signaling in early neural crest development

In neural crest formation, transcription factors, such as group E Sox and Snail1/Snail2 (Slug) regulate subsequent epithelial-mesenchymal transition (EMT) and migration. In particular, Sox9 has a strong effect on neural crest formation, EMT and differentiation of crest-derived cartilages in the cranium. It remains unclear, however, how Sox9 functions in these events, and how Sox9 activity is regulated. In this study, our gain-of-function and loss-of-function experiments reveal that Sox9 is essential for BMP signal-mediated induction of Snail2 and subsequent EMT in avian neural crest. We also show that Snail2 activates the Snail2 promoter, although Snail family proteins have been known as a repressor. Consistently, Sox9 directly activates the Snail2 promoter in synergy with, and through a direct binding to, Snail2. Finally, functions of these transcription factors in neural crest cells are enhanced by PKA signaling.
Accurate, Low-Energy Trajectory Mapping for Mobile Devices

CTrack is an energy-efficient system for trajectory mapping using raw position tracks obtained largely from cellular base station fingerprints. Trajectory mapping, which involves taking a sequence of raw position samples and producing the most likely path followed by the user, is an important component in many location-based services including crowd-sourced traffic monitoring, navigation and routing, and personalized trip management. Using only cellular (GSM) fingerprints instead of power-hungry GPS and WiFi radios, the marginal energy consumed for trajectory mapping is zero. This approach is non-trivial because we need to process streams of highly inaccurate GSM localization samples (average error of over 175 meters) and produce an accurate trajectory. CTrack meets this challenge using a novel two-pass Hidden Markov Model that sequences cellular GSM fingerprints directly without converting them to geographic coordinates, and fuses data from low-energy sensors available on most commodity smart-phones, including accelerometers (to detect movement) and magnetic compasses (to detect turns). We have implemented CTrack on the Android platform, and evaluated it on 126 hours (1,074 miles) of real driving traces in an urban environment. We find that CTrack can retrieve over 75% of a user's drive accurately in the median. An important by-product of CTrack is that even devices with no GPS or WiFi (constituting a significant fraction of today's phones) can contribute and benefit from accurate position data.
Three-finger precision grasp on incomplete 3D point clouds

We present a novel method for three-finger precision grasp and its implementation in a complete grasping tool-chain. We start from binocular vision to recover the partial 3D structure of unknown objects. We then process the incomplete 3D point clouds searching for good triplets according to a function that accounts for both the feasibility and the stability of the solution. In particular, while stability is determined using the classical force-closure approach, feasibility is evaluated according to a new measure that includes information about the possible configuration shapes of the hand as well as the hand's inverse kinematics. We finally extensively assess the proposed method using the stereo vision and the kinematics of the iCub robot.
Chronic kidney disease as a risk factor for acute community-acquired infections in high-income countries: a systematic review

Objective A systematic review of the association of predialysis chronic kidney disease (CKD) with the incidence of acute, community-acquired infections. Design We searched the MEDLINE, EMBASE and Cochrane databases (inception to 16 January 2014) for studies analysing the association of predialysis kidney disease with the incidence of acute, community-acquired urinary tract infection (UTI), lower respiratory tract or central nervous system infections or sepsis. Studies were required to include at least 30 participants with and without kidney disease. Setting and participants Community-based populations of adults in high-income countries. Outcome measures Acute, community-acquired UTI, lower respiratory tract or central nervous system infections or sepsis. Results We identified 14 eligible studies. Estimates from two studies lacked 95% CIs and SEs. The remaining 12 studies yielded 17 independent effect estimates. Only three studies included infections managed in the community. Quality assessment revealed that probable misclassification of kidney disease status and poor adjustment for confounding were common. There was evidence from a few large high-quality studies of a graded association between predialysis CKD stage and hospitalisation for infection. One study found an interaction with age, with a declining effect of CKD on infection risk as age increased. There was evidence of between-studies heterogeneity (I2=96.5%, p<0.001) which persisted in subgroup analysis, and thus meta-analysis was not performed. Conclusions Predialysis kidney disease appears to be associated with increased risk of severe infection. Whether predialysis kidney disease increases the susceptibility to infections and whether age modifies this association remains unclear.
A Gaussian mixture model with Gaussian weight learning rate and foreground detection using neighbourhood correlation

Moving object detection is the first and foremost step in many computer vision applications such as automated visual surveillance, human-machine interface, tracking, traffic surveillance, etc. Background subtraction is widely used for classifying image pixels into either foreground or background in presence of stationary cameras. A Gaussian Mixture Model (GMM) model is one such popular method used for background subtraction due to a good compromise between robustness to various practical environments and real-time constraints. In this paper we assume background pixel follows Gaussian distribution spatially as well as temporally. The proposed research uses Gaussian weight learning rate over a neighbourhood to update the parameters of GMM. The background pixel can be dynamic especially in outdoor environment, so in this paper we have exploited neighborhood correlation of pixels in foreground detection. We compare our method with other state-of-the-art modeling techniques and report experimental results. The performance of the proposed algorithm is evaluated using both qualitative and quantitative measures. Quantitative accuracy measurement is obtained from PCC. Experimental results are demonstrated on publicly available videos sequences containing complex dynamic backgrounds. The proposed method is quiet effective enough to provide accurate silhouette of the moving object for real-time surveillance.
The Cosmological Constant Problem and Nonlocal Quantum Gravity

A nonlocal quantum gravity theory is presented which is finite and unitary to all orders of perturbation theory. Vertex form factors in Feynman diagrams involving gravitons suppress graviton and matter vacuum fluctuation loops by introducing a low‐energy gravitational scale, ΛGvac < 2.4 × 10−3 eV. Gravitons coupled to non‐vacuum matter loops and matter tree graphs are controlled by a vertex form factor with the energy scale, ΛGM < 1 – 10 TeV. A satellite Eotvos experiment is proposed to test a violation of the equivalence principle for coupling of gravitons to pure vacuum energy compared to matter.
Antigens of Aeromonas salmonicida subsp. salmonicida specifically induced in vivo in Oncorhynchus mykiss

Furunculosis is a major fish disease, associated with high mortality rates, and its effect is particularly significant in farmed salmonids (Hiney & Olivier 1999). Efficacious vaccines, generally injected alongside an oil adjuvant, are available. However, certain side effects have been reported (Midtlyng, Reitan & Speilberg 1996; Anderson et al. 1997) and protection can lapse in stressed fish and in non‐salmonid species (Gudmundsdottir & Bjornsdottir 2007).
Casting a web of trust over Wikipedia: an interaction-based approach

We report in this short paper results on inferring a signed network (a "web of trust") from user interactions. On the Wikipedia network of contributors, from a collection of articles in the politics domain and their revision history, we investigate mechanisms by which relationships between contributors - in the form of signed directed links - can be inferred from their interactions. Our preliminary study provides valuable insight into principles underlying a signed network of Wikipedia contributors that is captured by social interaction. We look into whether this network (called hereafter WikiSigned) represents indeed a plausible configuration of link signs. We assess connections to social theories such as structural balance and status, which have already been considered in online communities. We also evaluate on this network the accuracy of a learned predictor for edge signs. Equipped with learning techniques that have been applied before on three explicit signed networks, we obtain good accuracy over the WikiSigned edges. Moreover, by cross training-testing we obtain strong evidence that our network does reveal an implicit signed configuration and that it has similar characteristics to the explicit ones, even though it is inferred from interactions. We also report on an application of the resulting signed network that impacts Wikipedia readers, namely the classification of Wikipedia articles by importance and quality.
ENERGY BALANCE CASCADED MULTILEVEL INVERTER FOR PHOTOVOLTAIC APPLICATION

Energy balance cascaded multilevel inverter for photovoltaic application. It is used for less no of power switches, losses, installation area, voltage stress and converter cost. It is also used for solar energy that is connected to the micro grid system. It reduces the transformerless operation and improves the power quality. Ability to operate in both symmetrical and asymmetrical mode is analyzed. The results are done with cascaded H bridge (CHB) and flying capacitor (FC) multilevel inverters. The simulation is done with Matlab Simulink.
Development of virtual exhibition display system based on mixed reality technology

Virtual Exhibition based on Mixed Reality Technology may greatly enhance the interaction between product and visitor. In this paper, The Virtual Hall roaming and product displaying system is designed, built from the aspects of the design ideas, technical solution, interface, running processes, and hardware and software systems. The method of basic structure of Mixed Reality Technology and its application in the virtual display was proposed。
J un 2 01 5 From S-confinement to 3 D Chiral Theories : Dressing the Monopoles

Monopole operators play a central role in 3 dimensional supersymmetric dualities: a careful understanding of their spectrum is necessary to match chiral operators on either sides of a conjectured duality. In Chern-Simons theories (k 6= 0), monopole operators acquire an electric charge, thus they need to be “dressed” by chiral matter superfields to be made gauge-invariant. Here we present strong evidence that “dressed” monopoles appear in SU(N) chiral theories even for k = 0 because of mixed CS terms generated along certain Coulomb branch directions. Our analysis is based on the dimensional reduction of 4-dimensional dualities which, for the simplest s-confining case, allows us to easily identify the spectrum of the electric chiral operators.
In-network data processing architecture for energy efficient wireless sensor networks

In traditional WSNs, the sensor nodes are used only for capturing data, which is analyzed in a centralized base station. This requires a continuous communication but wastes the energy of sensor nodes and results in a reduction of the overall network lifetime. In this paper, we proposed an in-network data processing architecture to improve the energy efficiency and the scalability and the accuracy of sensor networks. We also adapted a data mining algorithm to process data for matching a specific event. The simulations show a reduction in energy consumption and an improvement in data accuracy.