Miracle-dz/UniFAR-FTUs huggingface dataset:
Liraglutide prevents fast weight gain and β-cell dysfunction in male catch-up growth rats

We reported recently that after a nutritional growth retardation, rats showed significant weight gain, central fat accumulation, dyslipidemia, and β-cell dysfunction during a catch-up growth (CUG) phase. Here, we investigated whether glucagon-like peptide-1 (GLP-1) ameliorated the rapid weight gain, central fat deposition, and β-cell dysfunction during the CUG in rats. Sixty-four male Sprague Dawley rats were stratified into four groups including normal control group, CUG group, catch-up growth with liraglutide treatment group, and catch-up growth with liraglutide and exendin 9–39 treatment group. Energy intake, body weight, and body length were monitored. Fat mass percentage was analyzed by dual energy X-ray absorptiometry scan. Plasma triglyceride and non-esterified fatty acid were measured. The β-cell mass was analyzed by morphometric analysis and signaling molecules were examined by Western blot and real-time PCR. Insulin secretion capability was evaluated by hyperglycemic clamp test. Liraglutide prevented weight gain and improved lipid and glucose metabolism in rats under CUG conditions, which were associated with reduced fasting insulin levels and improved glucose-stimulated insulin secretion. Improved β-cell function is found to be associated with increased β-cell replication as determined by β-cell density and insulin-Ki67 dual staining. Furthermore, liraglutide increased islet pancreatic duodenal homeobox-1 (Pdx-1) and B-cell lymphoma-2 transcript and protein expression, and reduced Procaspase-3 transcript and Caspase-3 p11 subunit protein expression, suggesting that expression of Pdx-1 and reduction of apoptosis may be the mechanisms involved. The therapeutic effects were attenuated in rats co-administered with exendin 9–39, suggesting a GLP-1 receptor-dependent mechanism. These studies revealed that incretin therapy effectively prevented fast weight gain and β-cell dysfunction in rats under conditions of nutrition restriction followed by nutrition excess, which is in part due to enhanced functional β-cell mass and insulin secretory capacity.
HDAC10 promotes lung cancer proliferation via AKT phosphorylation

Histone deacetylase 10 (HDAC10) is a member of the class II HDACs, and its role in cancer is emerging. In this study, we found that HDAC10 is highly expressed in lung cancer tissues. It resides mainly in the cytoplasm of lung cancer cells but resides in the nucleus of adjacent normal cells. Further examinations revealed that HDAC10 resides in the cytoplasm in multiple lung cancer cell lines, including the A549, H358 and H460 cell lines, but mainly resides in the nucleus of normal lung epithelial 16HBE cells. A leucine-rich motif, R505L506L507C508V509A510L511, was identified as its nuclear localization signal (NLS), and a mutant (Mut-505-511) featuring mutations to A at each of its original R and L positions was found to be nuclear-localization defective. Functional analysis revealed that HDAC10 promoted lung cancer cell growth and that its knockdown induced cell cycle arrest and apoptosis. Mechanistic studies showed that HDAC10 knockdown significantly decreased the phosphorylation of AKT at Ser473 and that AKT expression significantly rescued the cell cycle arrest and apoptosis elicited by HDAC10 knockdown. A co-immunoprecipitation assay suggested that HDAC10 interacts with AKT and that inhibition of HDAC10 activity decreases its interaction with and phosphorylation of AKT. Finally, we confirmed that HDAC10 promoted lung cancer proliferation in a mouse model. Our study demonstrated that HDAC10 localizes and functions in the cytoplasm of lung cancer cells, thereby underscoring its potential role in the diagnosis and treatment of lung cancer.
Do Vendors’ Pricing Decisions Fully Reflect Information in Online Reviews?

By using online retail data collected from Amazon, Barnes & Nobel, and Pricegrabber, this paper investigates whether online vendors’’ pricing decisions fully reflect the information contained in various components of customers’ online reviews. The findings suggest that there is inefficiency in vendors’ pricing decisions. Specifically, vendors do not appear to fully understand the incremental predictive power of online reviews in forecasting future sales when they adjust their prices. However, they do understand demand persistence. Interestingly, vendors reduce price if the actual demand is higher than the expected demand (positive demand shock). This phenomenon is attributed to the advertising effect suggested in previous literature and the intense competitiveness of e-Commerce. Finally, we document that vendors do not change their prices directly in response to online reviews; their response to online reviews is through forecasting consumer’s future demand.
Modelling TFE renal cell carcinoma in mice reveals a critical role of WNT signaling

TFE-fusion renal cell carcinomas (TFE-fusion RCCs) are caused by chromosomal translocations that lead to overexpression of the TFEB and TFE3 genes (Kauffman et al., 2014). The mechanisms leading to kidney tumor development remain uncharacterized and effective therapies are yet to be identified. Hence, the need to model these diseases in an experimental animal system (Kauffman et al., 2014). Here, we show that kidney-specific TFEB overexpression in transgenic mice, resulted in renal clear cells, multi-layered basement membranes, severe cystic pathology, and ultimately papillary carcinomas with hepatic metastases. These features closely recapitulate those observed in both TFEB- and TFE3-mediated human kidney tumors. Analysis of kidney samples revealed transcriptional induction and enhanced signaling of the WNT β-catenin pathway. WNT signaling inhibitors normalized the proliferation rate of primary kidney cells and significantly rescued the disease phenotype in vivo. These data shed new light on the mechanisms underlying TFE-fusion RCCs and suggest a possible therapeutic strategy based on the inhibition of the WNT pathway. DOI: http://dx.doi. org/10.7554/eLife.17047. 001
X3DOM: a DOM-based HTML5/X3D integration model

We present a model that allows to directly integrate X3D nodes into HTML5 DOM content. This model tries to fulfill the promise of the HTML5 specification, which references X3D for declarative 3D scenes but does not define a specific integration mode. The goal of this model is to ease the integration of X3D in modern web applications by directly mapping and synchronizing live DOM elements to a X3D scene model. This is a very similar approach to the current SVG integration model for 2D graphics. Furthermore, we propose a framework that includes a new X3D Profile for the DOM integration. This profile should make implementation simple, but in addition we show that the current X3D runtime model still scales well. A detailed discussion includes DOM integration issues like events, namespaces and scripting. We finally propose an implementation framework that should work with multiple browser frontends (e.g. Firefox and WebKit) and different X3D runtime backends. We hope to connect the technologies and the X3D/ W3C communities with this proposal and outline a model, how an integration without plugins could work. Moreover, we hope to inspire further work, which could lead to a native X3D implementation in browsers similar to the SVG implementations today.
Meeting profiles: size, duration, and location

Managers spend a majority of their days in face-to-face meetings so it is not surprising that meeting support is becoming an important focus of information technology. But meetings vary widely in size, duration, and location. Some are brief deskside meetings between two or three people, others are lengthy conference room meetings with a dozen or more participants. Thus, support needs may also vary by size, duration, and location. Surprisingly, past use-of-time studies have told us little about how meetings vary along these dimensions. We review the history of the early studies and present a new diary study to look at these dimensions and their relationships. <<ETX>>
Adaptive Packet Context-Constrained KL-Divergence Model for Intrusion Detection

Recently, intrusion detection research has make some important achievements. A variety of classical machine learning and entropy analyze algorithms has been applied to intrusion detection with varying levels of success. In this paper, we analyze the traffic connection statistics information, and give the definition the In/Out flows normal and abnormal statue. To calculate two classes probability KL-divergence, we proposed a novel DDos detection mechanism, which can robustly detect the DDos attack and abnormal network status through combine the related entropy of packet-context with KL-divergence. Experiment results indicate that the proposed approach can significantly reduce the percentage of false positives. The adaptive model based on related entropy of packet-context has a significant advantage in detection speed and performance.
Direct Flux Vector Control of Axial Flux IPM Motors for in-wheel traction solutions

The paper describes a Direct-Flux Vector Control (DFVC) scheme suitable for synchronous Permanent Magnet (PM) drives employed in applications that require motor flux-weakening operation, as happens for traction drives. The proposed controller operates in stator flux reference frame and can be easily adapted for Surface-mounted PM (SPM) and Interior PM (IPM) motors. The stator flux is directly controlled by the direct component of the voltage vector, while the torque is controlled by regulating the quadrature current component. The DFVC scheme has been used for an Axial Flux IPM (AFIPM) designed as a wheel motor for light traction application. Experimental results are provided for an 1.2 kW AFIPM prototype, demonstrating good steady-state and transient performance, as well as maximum torque production under current and voltage limitations.
Optimal Design of Piezoelectric Cantilevered Actuators With Guaranteed Performances by Using Interval Techniques

Piezoelectric materials are well recognized for the development of systems and actuators working at the micro/nano-scale such as microsystems. This recognition is thanks to the high resolution, high bandwidth, and high force density that they can offer. However, piezoelectric actuators are typified by low range of displacement relative to other actuators like magnetic or thermal actuators. To obtain the sufficient range of displacement with a piezoelectric actuator, either we use high input voltages or we redesign the actuator to have larger dimensions. The former solution may lead to the destruction of the actuators and the latter is not congruent with the objectives of microsystems where the dimensions should be miniaturized. Furthermore, increasing the dimensions of the actuators reduces their rapidity and bandwidth. This paper proposes an approach based on interval analysis to design piezoelectric actuators with cantilever structures. The aim consists in reducing their dimensions while still satisfying some specified performances in term of output range and in term of resonant frequency (and thus bandwidth). The problem of the design is formulated as a set-inversion problem which can be solved using interval techniques. The obtained results, validated with prototype fabrication and experimental characterization, demonstrate the efficiency and the interests of the proposed method for designing systems and actuators working at the micro/nano-scale in general.
Universal response curve for nanowire superconducting single-photon detectors

Using detector tomography, we investigate the detection mechanism in NbN-based superconducting single photon detectors (SSPDs). We demonstrate that the detection probability uniquely depends on a particular linear combination of bias current and energy, for a large variation of bias currents, input energies and detection probabilities, producing a universal detection curve. We obtain this result by studying multiphoton excitations in a nanodetector with a sparsity-based tomographic method that allows factoring out of the optical absorption. We discuss the implication of our model system for the understanding of meander-type SSPDs.
Embodied virtual coaches as an example of captology

Virtual coaches are an example of captology; computer systems that are used as persuasive technology. In this paper existing applications of embodied virtual coaches are discussed and compared to each other. Virtual coaches are related to existing classifications of persuasive technologies. An attempt is made to subclassify virtual coaches based on several dimensions. Based on what was seen in current research, we look at the expected future developments for virtual coaches.
On the Growth Rate of the Input-Output Weight Distribution of Convolutional Encoders

In this paper, exact formul\ae of the input-output weight distribution function and its exponential growth rate are derived for truncated convolutional encoders. In particular, these weight distribution functions are expressed in terms of generating functions of error events associated with a minimal realization of the encoder. Although explicit analytic expressions can be computed for relatively small truncation lengths, the explicit expressions become prohibitively complex to compute as the truncation lengths and the weights increase. Fortunately, a very accurate asymptotic expansion can be derived using the multidimensional saddle-point method (MSP method). This approximation is substantially easier to evaluate and is used to obtain an expression of the asymptotic spectral function, and to prove continuity and concavity in its domain (convex and closed). Finally, this approach is able to guarantee that the sequence of exponential growth rates converges uniformly to the asymptotic limit, and to estimate.
What Makes Different People's Representations Alike: Neural Similarity Space Solves the Problem of Across-subject fMRI Decoding

A central goal in neuroscience is to interpret neural activation and, moreover, to do so in a way that captures universal principles by generalizing across individuals. Recent research in multivoxel pattern-based fMRI analysis has led to considerable success at decoding within individual subjects. However, the goal of being able to decode across subjects is still challenging: It has remained unclear what population-level regularities of neural representation there might be. Here, we present a novel and highly accurate solution to this problem, which decodes across subjects between eight different stimulus conditions. The key to finding this solution was questioning the seemingly obvious idea that neural decoding should work directly on neural activation patterns. On the contrary, to decode across subjects, it is beneficial to abstract away from subject-specific patterns of neural activity and, instead, to operate on the similarity relations between those patterns: Our new approach performs decoding purely within similarity space. These results demonstrate a hitherto unknown population-level regularity in neural representation and also reveal a striking convergence between our empirical findings in fMRI and discussions in the philosophy of mind addressing the problem of conceptual similarity across neural diversity.
Balanced feature selection method for Internet traffic classification

In Internet traffic classification, the class imbalance problem is mainly addressed by adjusting the class distribution. In the meanwhile, feature selection is also a key factor evoking this problem. Therefore a new filter feature selection method called balanced feature selection (BFS) is proposed. Every feature is measured both locally and globally and then an optimal feature subset is selected by our search model. A certainty coefficient is presented to measure the correlation between a feature and a certain class locally. The symmetric uncertainty is utilised to measure a feature and all classes globally. Through experiments on two real traffic traces using three classification algorithms, BFS is compared with five existing feature selection methods. Results show that it outperforms others by more than 15.29% g-mean improvement. Classification results are averaged over all datasets and classifiers here, 59.54% g-mean, 86.35% Mauc and 91.42% overall accuracy are achieved, respectively, when it is used.
DEEP-CARVING: Discovering visual attributes by carving deep neural nets

Most of the approaches for discovering visual attributes in images demand significant supervision, which is cumbersome to obtain. In this paper, we aim to discover visual attributes in a weakly supervised setting that is commonly encountered with contemporary image search engines. For instance, given a noun (say forest) and its associated attributes (say dense, sunlit, autumn), search engines can now generate many valid images for any attribute-noun pair (dense forests, autumn forests, etc). However, images for an attributenoun pair do not contain any information about other attributes (like which forests in the autumn are dense too). Thus, a weakly supervised scenario occurs: each of the M attributes corresponds to a class such that a training image in class m ∈ {1, . , M} contains a single label that indicates the presence of the mth attribute only. The task is to discover all the attributes present in a test image. Deep Convolutional Neural Networks (CNNs) [20] have enjoyed remarkable success in vision applications recently. However, in a weakly supervised scenario, widely used CNN training procedures do not learn a robust modelfor predicting multiple attribute labels simultaneously. The primary reason is that the attributes highly co-occur within the training data, and unlike objects, do not generally exist as well-defined spatial boundaries within the image. To ameliorate this limitation, we propose Deep-Carving, a novel training procedure with CNNs, that helps the net efficiently carve itselffor the task of multiple attribute prediction. During training, the responses of the feature maps are exploited in an ingenious way to provide the net with multiple pseudo-labels (for training images) for subsequent iterations. The process is repeated periodically after a fixed number of iterations, and enables the net carve itself iteratively for efficiently disentangling features. Additionally, we contribute a noun-adjective pairing inspired Natural Scenes Attributes Dataset to the research community, CAMITNSAD, containing a number of co-occurring attributes within a noun category. We describe, in detail, salient aspects of this dataset. Our experiments on CAMITNSAD and the SUN Attributes Dataset [29], with weak supervision, clearly demonstrate that the Deep-Carved CNNs consistently achieve considerable improvement in the precision of attribute prediction over popular baseline methods.
Bias and Its Implications for Evidence-Based Clinical Decision Making

In this review, we define and discuss several aspects of publication bias: why it occurs; its importance to dental practitioners, dental educators, and dental students; its potential to affect treatment decisions; and how it can be detected. In addition, we briefly discuss attempts to reduce publication bias. Ideally, clinical decision making should be based on the totality of evidence and not on a sample biased by the selective publication of studies that show significant results. Dentistry increasingly depends on evidence-based decision making for treatment planning and therapy. As a result we, as a profession, need to fully appreciate the potential for publication bias to hinder advancements in oral health care by decreasing the availability of scientific evidence and threatening the validity of evidence-based practice. Dr. Crawford is Professor of Clinical Periodontics, Department of Periodontics; Dr. Briggs is Director of the Office of Dental Education; and Dr. Engeland is Assistant Professor, Department of Periodontics—all at the College of Dentistry, University of Illinois at Chicago. Direct correspondence and requests for reprints to Dr. John M. Crawford, Department of Periodontics, College of Dentistry, University of Illinois at Chicago, 801 South Paulina Street, Chicago, IL 60612; 312-996-1266 phone; 312-996-0943 fax; jimmy@uic.edu.
Start-Up Incentives: Entrepreneurship Policy or Active Labour Market Programme?

As several European governments develop new start-up programmes during recessions, the appropriateness of these policies recently has become a hot policy issue. This study suggests that the contribution of these incentives is dubious, if aimed to combat economic and jobs crises as part of the entrepreneurship policy, and can be shaped by various country-specific factors, such as the economic situation and the stringency of labour laws. To provide support for these claims, this paper investigates the underlying determinants of an individual's decision to switch from unemployment to self-employment in Europe and stresses the need to devote special attention to the role of three essential dimensions: (i) the existing heterogeneity within self-employment, by considering self-employed individuals who hire employees (employers) and self-employed individuals without personnel (own-account workers) as separated groups; (ii) the effects of different measures of social capital and network contacts, as forms of micro level institutional factors; and (iii) the explanatory power of cross-country differences in the state of the economy, expenditure on start-up incentives, and the stringency of labour laws, as forms of macro level institutional factors. This study has useful theory and policy implications for entrepreneurship development.
Involvement of the 5' proximal coding sequences of hepatitis C virus with internal initiation of viral translation.

The 5' nontranslated region (NTR) of hepatitis C virus (HCV) consists of 341 nucleotides (nt). This region comprises the majority of the internal ribosome entry site (IRES) which controls the efficiency of viral translation. Previous studies of the 3' boundary of the HCV IRES yielded conflicting data regarding the involvement of viral coding sequences in IRES activity. We therefore studied the functional significance of the 5' proximal coding sequences of the HCV core gene on IRES activity. We constructed monocistronic and bicistronic DNAs that contained either a chloramphenicol acetyl transferase (CAT) gene or a luciferase (Luc) gene as the reporter. Results from both in vitro and in vivo experiments indicated that the optimal IRES ranged within nt 1-371. Further mutational analyses of sequences surrounding the initiation codon revealed that primary sequences downstream of the AUG initiator rather than the secondary structure are important in regulating optimal IRES function. We are also able to demonstrate that a non-AUG codon could be used to initiate the synthesis of a reporter protein, albeit with lower efficiency. These findings bear important implications for the HCV IRES secondary structures.
Critical Success Factors of Knowledge Management, Innovation and Organisational Performance: An Empirical Study of the Iraqi Mobile Telecommunication Sector

There is lack of evidence from previous studies that examined the relationships among critical success factors of knowledge management, innovation, and organisational performance, particularly in the Iraqi mobile telecommunication sector. Using quantitative survey research involving 220 mid-level managers, present study empirically tested a proposed theoretical framework that examines the above relationships based on structural equation model. The results show that critical success factors of knowledge management had a statistically significant and direct positive effect on innovation and OP. Most importantly, the findings indicate that critical success factors of knowledge management had a positive and statistically significant effect on organisational performance through the partial mediation effect of innovation. The present study shows the significance of the critical success factors of knowledge management in relation to enhanced innovation and improved organisational performance.