Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Insulin Analogues on Insulin/IGF1 Hybrid Receptors: Increased Activation by Glargine but Not by Its Metabolites M1 and M2

Background In diabetic patients, the pharmacokinetics of injected human insulin does not permit optimal control of glycemia. Fast and slow acting insulin analogues have been developed, but they may have adverse properties, such as increased mitogenic or anti-apoptotic signaling. Insulin/IGF1 hybrid receptors (IR/IGF1R), present in most tissues, have been proposed to transmit biological effects close to those of IGF1R. However, the study of hybrid receptors is difficult because of the presence of IR and IGF1R homodimers. Our objective was to perform the first study on the pharmacological properties of the five marketed insulin analogues towards IR/IGF1R hybrids. Methodology To study the effect of insulin analogues on IR/IGF1R hybrids, we used our previously developed Bioluminescence Resonance Energy Transfer (BRET) assay that permits specific analysis of the pharmacological properties of hybrid receptors. Moreover, we have developed a new, highly sensitive BRET-based assay to monitor phophatidylinositol-3 phosphate (PIP3) production in living cells. Using this assay, we performed a detailed pharmacological analysis of PIP3 production induced by IGF1, insulin and insulin analogues in living breast cancer-derived MCF-7 and MDA-MB231 cells. Results Among the five insulin analogues tested, only glargine stimulated IR/IGF1R hybrids with an EC50 that was significantly lower than insulin and close to that of IGF1. Glargine more efficiently stimulated PIP3 production in MCF-7 cells but not in MDA-MB231 cells as compared to insulin. In contrast, glargine metabolites M1 and M2 showed lower potency for hybrid receptors stimulation, PIP3 production, Akt and Erk1/2 phosphorylation and DNA synthesis in MCF-7 cells, compared to insulin. Conclusion Glargine, possibly acting through IR/IGF1R hybrids, displays higher potency, whereas its metabolites M1 and M2 display lower potency than insulin for the stimulation of proliferative/anti-apoptotic pathways in MCF-7 cells.
Algorithm Research of Face Image Gender Classification Based on 2-D Gabor Wavelet Transform and SVM

Gender classification is one of the most challenging problems in the field of pattern recognition. The pixel-based gray image recognition method is quite sensitive to illumination variation and has high dimensions for computation. PCA-based image feature recognition algorithm can reduce the image dimension, but it is only on the basis of optimal entropy to choose face features which neglects the different gender information between the male and female. In order to overcome the disturbance of non-essential information such as illumination variations and facial expression changing, a new algorithm is proposed in this paper. That is, the 2-D Gabor transform is used for extracting the face features; a new method is put forwards to decrease dimensions of Gabor transform output for speeding up SVM training; finally gender recognition is accomplished with SVM classifier. Good performance of gender classification test is achieved on a relative large scale and low-resolution face database. KeywordsGender-Classification; 2-D Gabor-wavelet Transform; SVM ; PCA Analysis
Towards Semantic Event-Driven Systems

One of the critical success factors of event-driven systems is the capability of detecting complex events from simple and ordinary event notifications. Complex events which trigger or terminate actionable situations can be inferred from large event clouds or event streams based on their event instance sequence, their syntax and semantics. Using semantics of event algebra patterns defined on top of event instance sequences for event detection is one of the promising approaches for detection of complex events. The developments and successes in building standards and tools for semantic technologies such as declarative rules and ontologies are opening novel research and application areas in event processing. One of these promising application areas is semantic event processing. In this paper we contribute with a conceptual approach which supports the implementation of the vision of semantic event-driven systems; using Semantic Web technologies, benefiting from complex event processing, and ensuring quality through trust and reputation management. All of these novel technologies leads to more intelligent decision supporting systems.
Fryns syndrome phenotype caused by chromosome microdeletions at 15q26.2 and 8p23.1

Background: Fryns syndrome (FS) is the commonest autosomal recessive syndrome in which congenital diaphragmatic hernia (CDH) is a cardinal feature. It has been estimated that 10% of patients with CDH have FS. The autosomal recessive inheritance in FS contrasts with the sporadic inheritance for the majority of patients with CDH and renders the correct diagnosis critical for accurate genetic counselling. The cause of FS is unknown. Methods: We have used array comparative genomic hybridisation (array CGH) to screen patients who have CDH and additional phenotypic anomalies consistent with FS for cryptic chromosome aberrations. Results: We present three probands who were previously diagnosed with FS who had submicroscopic chromosome deletions detected by array CGH after normal karyotyping with G-banded chromosome analysis. Two female infants were found to have microdeletions involving chromosome band 15q26.2 and one male had a deletion of chromosome band 8p23.1. Conclusions: We conclude that phenotypes similar to FS can be caused by submicroscopic chromosome deletions and that high resolution karyotyping, including array CGH if possible, should be performed prior to the diagnosis of FS to provide an accurate recurrence risk in patients with CDH and physical anomalies consistent with FS.
A histogram-based detection of corrupted images from traffic surveillance cameras

Traffic surveillance has increasingly become an important research topic in Intelligent Transportation Systems (ITS). Recently, a number of traffic monitoring systems using Closed Circuit Televisions (CCTVs) have been implemented in many urban areas all over the world in order to monitor, record image sequences and report traffic information. Images are captured from cameras and are sent to other systems for processing or displaying at the traffic management center or websites. However, data transmission problems and/or camera malfuction can corrupt the quality of video images. In this paper, we propose a simple but efficient method to detect corrupted images received from traffic surveillance cameras using histogram analysis and neural network classifiers. The experimental results show that our approach can accurately differentiate between normal and corrupted images.
When Are New Firms More Innovative than Established Firms

We propose a model in which industry conditions influence the relative advantages of new and established firms as innovators. Using a unique data set of commercialization attempts of inventions simultaneously at risk of commercialization by both new and established firms, we find that industry characteristics including the number of firms in the industry, the size of the market, the availability of capital for new firms, and manufacturing intensity influence whether new or established firms are more innovative. Running headline: When are new firms more innovative?
Good intentions to read on mobiles are not good enough: reducing barriers to m-reading is crucial

Drawing on the analysis of 4330 surveys on mobile reading in seven developing countries, this study found that the discrepancy between intentions to read on mobiles and actual reading can be attributed to barriers such as problematic mobile connectivity and inadequate relevant reading material. The policy implication of this research finding is clear -- reducing barriers specific to mobile reading (m-reading) will improve programme effectiveness. This study applied the technological acceptance model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to the growing adoption of mobile reading in developing countries and partially verified key constructs of the theories. Findings suggest that the two models provide viable theoretical frameworks for future m-reading programs. This study is one of two companion papers to the 2014 UNESCO report on Reading in the Mobile Era. The other focuses on gender differences in m-reading.
Polygonal symmetry transform for detecting rectangular traffic signs

We present a new symmetry transform finding rectangles based on a gradient and corner information of images. We succeed the Fast Radial Symmetry Transform (FRST) finding radial objects with low cost and high accuracy. But FRST focused on using image gradient to find small circle shapes so that FRST can not detect larger polygonal shapes well. Our transform used image gradient and image corner to construct polygonal symmetry maps and extract regions of interest from images. We referred the set of polygon features as the Polygonal Symmetry Transform (PST). Then PST has large coverage with the advantages of FRST. To verify PST, we designed a polygon detectors which finds the road traffic sign such as radial and polygonal shapes. Our detector extracted regions of interest from real road images and calculated performances including detection rate for traffic signs and processing time per image in our experiments.
Machine Recognition of Human Face

Machine recognition of human face is an active and fast growing area of research due to a wide variety of commercial and law-enforcement applications including access control, security monitoring, and video surveillance. The major advantage of this biometric approach is its ability for authentic identification of a person’s identity. While the established and widely used authentication criteria such as passwords, PINs (Personal Identification Numbers) or magnetic cards suffer from the risk of getting stolen, copied, lost and are thus exposed to fraudulent use biometric approaches are expected to be immune from this drawback. Among the various biometric approaches, face recognition offers the additional advantage in that this approach allows a passive and non-intrusive identification in a user-friendly way without having to interrupt user activity. We provide, in this report, a brief overview of the techniques being explored for developing a face recognition system and indicate the present state-of-the-art of the field. * Email:anshul.nsit@yahoo.com
Mobile Application Quality and Platform Stickiness under Formal vs. Self-Control - Evidence from an Experimental Study

Although control modes have been extensively studied in IS research, minimal research attention has been directed towards understanding how different control mechanisms operate in software-based platforms. Drawing on self-determination theory and IS control literature, we conducted a laboratory experiment with 138 participants in which we examined how well third-party developers contribute to a mobile app development platform in terms of output quality and whether they are willing to stick with this platform under formal (i.e., output and process) and informal (i.e., self) control. We demonstrate that self-control has consistently stronger effects on application quality and platform stickiness than formal control modes. We also shed light on perceived autonomy as explanatory mechanism through which the control modes’ effects are mediated. Taken together, our study highlights the theoretically important finding that self-determination among third-party developers is a stronger driving force than typical hierarchical control mechanisms. Implications for research and practice are discussed.
Improved Multistage Learning for Multibody Motion Segmentation

We present an improved version of the MSL method of Sugaya and Kanatani for multibody motion segmentation. We replace their initial segmentation based on heuristic clustering by an analytical computation based on GPCA, fitting two 2-D affine spaces in 3-D by the Taubin method. This initial segmentation alone can segment most of the motions in natural scenes fairly correctly, and the result is successively optimized by the EM algorithm in 3-D, 5-D, and 7-D. Using simulated and real videos, we demonstrate that our method outperforms the previous MSL and other existing methods. We also illustrate its mechanism by our visualization technique.
An Automatic Image Registration Algorithm for Tracking Moving Objects in Low-Resolution Video

We propose an automatic image registration algorithm for tracking moving objects in low-resolution videos. The algorithm uses SIFT keypoints to identify matching stationary points in the input frame and the base frame. The best set of matching stationary points is used to create a spatial transform to register the points on the moving objects in the input frame to the base frame. We examined two probabilistic methods and one deterministic method of identifying the stationary points with two different fitness measures (Euclidean and Mahalanobis) and two spatial transforms (affine and projective). Our experiments on two low-resolution videos indicate that our algorithm performs better using the affine transform than the projective transform. However, the differences in average pixel error between the methods of determining stationary points and fitness measures are statistically insignificant. Therefore, which one to use depends on the execution speed and confidence interval required for the application.
Differential jumping: A novel mode for micro-robot navigation

Jumping is an effective navigation mode for micro-robots. Recent research has addressed significant challenges that exist in developing reliable, energy efficient, light weight micro actuation mechanisms, mostly focused on a single jump. However, there has been limited exploration of planning strategies to enable multi-hop navigation requiring turning and obstacle avoidance. This work introduces the concept of differential jump, generated by asymmetrical thrust from multiple thrusters, as a viable mode for navigation in micro-robots to enable turning during a jump. We present a simplified differential jump model, derive state propagation equations and adapt a sampling based motion planner to compute trajectories for such a system among obstacles in simulation. We further present an early prototype of a micro-robot (≈ 70mm cube) capable of navigation on a planar surface among obstacles using the differential jump mechanism presented.
Decision Support for Dynamic City Traffic Management using Vehicular Communication

In this paper, we present an integrated simulation approach featuring centralized and decentralized traffic management in urban areas. Our aim is to improve traffic flows by dynamic traffic management which is supported by vehicular communication interlinking centralized and decentralized decision making. We focus on traffic state estimation and the optimization of traffic lights as a central component to influence local traffic states, while individual traffic participants’ behavior is modeled by multiagent systems. Traffic participants achieve their individual goals by formation of groups and improving their knowledge about the road network by means of learning. Modeling of vehicular communication takes into account specific characteristics of urban areas, ensuring the realistic collection and dissemination of (de)centralized information. We provide a comprehensive microscopic traffic simulation framework featuring innovative functionality regarding dynamic traffic management, decentralized decision making as well as realistic communication modeling. To illustrate and validate our approach, we present a use case in a city scenario. Simulations are implemented based on the microscopic traffic simulator AIMSUN, which is significantly extended using the
Speaker diarization and linking of large corpora

Performing speaker diarization of a collection of recordings, where speakers are uniquely identified across the database, is a challenging task. In this context, inter-session variability compensation and reasonable computation times are essential to be addressed. In this paper we propose a two-stage system composed of speaker diarization and speaker linking modules that are able to perform data set wide speaker diarization and that handle both large volumes of data and inter-session variability compensation. The speaker linking system agglomeratively clusters speaker factor posterior distributions, obtained within the Joint Factor Analysis framework, that model the speaker clusters output by a standard speaker diarization system. Therefore, the technique inherently compensates the channel variability effects from recording to recording within the database. A threshold is used to obtain meaningful speaker clusters by cutting the dendrogram obtained by the agglomerative clustering. We show how the Hotteling t-square statistic is an interesting distance measure for this task and input data, obtaining the best results and stability. The system is evaluated using three subsets of the AMI corpus involving different speaker and channel variabilities. We use the within-recording and across-recording diarization error rates (DER), cluster purity and cluster coverage to measure the performance of the proposed system. Across-recording DER as low as within-recording DER are obtained for some system setups.
EBMT by Tree-Phrasing: a Pilot Study

We present a study we conducted to build a repository storing associations between simple dependency treelets in a source language and their corresponding phrases in a target language. To assess the impact of this resource in EBMT, we used the repository to compute coverage statistics on a test bitext and on a n-best list of translation candidates produced by a standard phrase-based decoder.
Auxiliary Multimodal LSTM for Audio-visual Speech Recognition and Lipreading

The Aduio-visual Speech Recognition (AVSR) which employs both the video and audio information to do Automatic Speech Recognition (ASR) is one of the application of multimodal leaning making ASR system more robust and accuracy. The traditional models usually treated AVSR as inference or projection but strict prior limits its ability. As the revival of deep learning, Deep Neural Networks (DNN) becomes an important toolkit in many traditional classification tasks including ASR, image classification, natural language processing. Some DNN models were used in AVSR like Multimodal Deep Autoencoders (MDAEs), Multimodal Deep Belief Network (MDBN) and Multimodal Deep Boltzmann Machine (MDBM) that actually work better than traditional methods. However, such DNN models have several shortcomings: (1) They don't balance the modal fusion and temporal fusion, or even haven't temporal fusion; (2)The architecture of these models isn't end-to-end, the training and testing getting cumbersome. We propose a DNN model, Auxiliary Multimodal LSTM (am-LSTM), to overcome such weakness. The am-LSTM could be trained and tested once, moreover easy to train and preventing overfitting automatically. The extensibility and flexibility are also take into consideration. The experiments show that am-LSTM is much better than traditional methods and other DNN models in three datasets.
The cycle of relapse and recovery of substance misusing offenders on a community based rehabilitation programme: The impact of childhoods, family, relationships, significant life events and psychological wellbeing :an interpretative phenomenological analysis and approach.

Background and aims There is a paucity of research into the relapse and recovery of Class A drug misusing offenders who are part of the Drug Interventions Programme (DIP). The key aims of the DIP, a UK Government criminal justice strategy, are to reduce Class A drug misuse and the [perceived] associated offending behaviour. This group have entrenched and long lasting addictions, with many ‘failed’ attempts at recovery. There is no published research about DIP clients, using a qualitative methodology, which explores childhoods, relationships, psychological health and significant life events and how these might impact on drug use, relapse and recovery from their perspective. The aim of the research was to ask community based DIP clients what they considered to be important factors in their relapse and recovery and to explore how they understand and make sense of these. Theories within the developmental psychology field, some of which have not been extensively applied within the addiction field, have helped to inform the research. Method and participants To address a gap in the field, an Interpretative Phenomenological Analysis (IPA) was adapted for use with a focus group design (four Focus Groups, total N= 10), to explore the value and merit of the research question. These findings helped to inform semi-structured interviews (N= 17) using IPA. Participants were adult men and women who were either current or past DIP clients recruited through the DIP teams in England. Findings The following themes were developed from the semi-structured interview study: 1) Experiences of abusive childhoods – links to emotions that were experienced as damaging/harmful and problem behaviours; 2) The divergent and damaged selves – links to substance misuse; and 3) Drug use to cope and survive dangerous events and trauma/responses. Recovery was about managing these in a healthier way. A fourth theme showed how participants’ experiences changed during transitions into and out of recovery, for example the presence or absence of supportive networks. Conclusion The research has implications for theory, policy, practice and future research. This includes the provision of trauma based therapy and supporting clients to manage their emotions as well as their wider mental health problems. The importance of an integrated framework of theories from a developmental attachment, trauma and family systems approach to inform training and practice are highlighted from the findings.
Lymphocyte Antigen 75 Polymorphisms Are Associated with Disease Susceptibility and Phenotype in Japanese Patients with Inflammatory Bowel Disease

Recent genome-wide association studies have rapidly improved our understanding of the molecular pathways leading to inflammatory bowel disease (IBD), which includes Crohn's disease (CD) and ulcerative colitis (UC). Although several reports have demonstrated that gene single nucleotide polymorphisms (SNPs) are associated with susceptibility to IBD, its precise genetic factors have not been fully clarified. Here, we performed an association analysis between lymphocyte antigen 75 (LY75) genetic variations and IBD susceptibility or phenotype. SNPs were genotyped in 51 CD patients, 94 UC patients, and 269 healthy controls of Japanese ethnicity. We detected a significant relationship with CD susceptibility for the rs16822581 LY75 SNP (P = 0.045). One haplotype (GT, P = 0.042) was also associated with CD susceptibility, while another carrying the opposite SNP (CA) was linked to an absence of surgical history for CD. Our findings confirm that LY75 is involved in CD susceptibility and may play a role in disease activity in the Japanese population.
PENGARUH KEPEMIMPINAN, PEMBAGIAN KERJA DAN KOMPENSASI TERHADAP KINERJA KARYAWAN PADA PT BANK DANAMON CABANG MANADO

Penelitian ini bertujuan untuk mengetahui pengaruh Kepemimpinan, Pembagian Kerja dan Kompensasi terhadap kinerja karyawan. Populasi penelitian adalah seluruh karyawan PT Bank Danamon Cabang Manado yang berjumlah 254 orang. Sampel yang digunakan adalah 72 responden yang ada. Dengan teknik yang digunakan yaitu Analisis Regresi Berganda dengan rumus adalah Y = b0 + b1X1 + b2X2 + b3X3 + e. Untuk menjawab penelitian ini apakah menunjukkan hasil bahwa: Kepemimpinan, Pembagian Kerja, dan Kompensasi secara bersama dan parsial berpengaruh positif dan signifikan terhadap kinerja karyawan. Pada PT Bank DanamonCabang Manado agar dapat meningkatkan kinerja karyawan agar pelayanan dapat menjadi efektif dan efisien. Karyawan, Kepemimpinan,pembagian kerja dan kompensasi, kinerja