Miracle-dz/UniFAR-FTUs huggingface dataset:
Intracranial Error Detection via Deep Learning

Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalography (EEG). However, these methods were so far only rarely evaluated for use in intracranial EEG. We employed convolutional neural networks (CNNs) to classify and characterize the error-related brain response as measured in 24 intracranial EEG recordings. Decoding accuracies of CNNs were significantly higher than those of a regularized linear discriminant analysis. Using time-resolved deep decoding, it was possible to classify errors in various regions in the human brain, and further to decode errors over 200 ms before the actual erroneous button press, e.g., in the precentral gyrus. Moreover, deeper networks performed better than shallower networks in distinguishing correct from error trials in all-channel decoding. In single recordings, up to 100 % decoding accuracy was achieved. Visualization of the networks' learned features indicated that multivariate decoding on an ensemble of channels yields related, albeit non-redundant information compared to single-channel decoding. In summary, here we show the usefulness of deep learning for both intracranial error decoding and mapping of the spatio-temporal structure of the human error processing network.
The diffusion of exporting in Brazilian industrial clusters

The present research aimed at understanding the process by which firms in a cluster start to export based on systemic interactions, and the process of diffusion of exporting as a business strategy within the cluster. Diffusion was defined, following Rogers’ (1995: 5) seminal work, as ‘the process by which an innovation is communicated through certain channels over time among the members of a social system’. The research method used was industry case studies and the unit of analysis selected was the cluster. Two manufacturing industries were chosen to be investigated, and within each geographic area clusters were identified as the origin of dynamic export growth in the industry. Players in each industrial cluster, as well as other significant actors, were interviewed. Extensive secondary data research was done to study clusters’ historical development. Detailed analysis and a comparison of the experiences permitted the extraction of some general conclusions concerning the similarities and differences between the clusters in terms of the adoption and diffusion of exporting. Results showed that the diffusion of exporting in an industrial cluster is quite similar to the dissemination of technical innovation. Social ties were important to facilitate the diffusion of exporting in one of the clusters studied. Also, the role of domestically-owned flagship firms in leading the internationalization process proved to be important in only one of the clusters, while the role of external actors was fully supported in the two industries studied. Finally, a number of support institutions, private and public, interfered in different stages of the internationalization process. In both industries, the federal government had only a late and limited impact on export initiation and development.
Embedding of Hierarchically Typed Knowledge Bases

Embedding has emerged as an important approach to prediction, inference, data mining and information retrieval based on knowledge bases and various embedding models have been presented. Most of these models are "typeless," namely, treating a knowledge base solely as a collection of instances without considering the types of the entities therein. In this paper, we investigate the use of entity type information for knowledge base embedding. We present a framework that augments a generic "typeless" embedding model to a typed one. The framework interprets an entity type as a constraint on the set of all entities and let these type constraints induce isomorphically a set of subsets in the embedding space. Additional cost functions are then introduced to model the fitness between these constraints and the embedding of entities and relations. A concrete example scheme of the framework is proposed. We demonstrate experimentally that this framework offers improved embedding performance over the typeless models and other typed models.
A New Method for Predicting Protein Functions From Dynamic Weighted Interactome Networks

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of proteins can only be annotated computationally. Under new conditions or stimuli, not only the number and location of proteins would be changed, but also their interactions. This dynamic feature of protein interactions, however, was not considered in the existing function prediction algorithms. Taking the dynamic nature of protein interactions into consideration, we construct a dynamic weighted interactome network (DWIN) by integrating protein-protein interaction (PPI) network and time course gene expression data, as well as proteins' domain information and protein complex information. Then, we propose a new prediction approach that predicts protein functions from the constructed dynamic weighted interactome network. For an unknown protein, the proposed method visits dynamic networks at different time points and scores functions derived from all neighbors. Finally, the method selects top N functions from these ranked candidate functions to annotate the testing protein. Experiments on PPI datasets were conducted to evaluate the effectiveness of the proposed approach in predicting unknown protein functions. The evaluation results demonstrated that the proposed method outperforms other competing methods.
Increased expression of EIF5A2, via hypoxia or gene amplification, contributes to metastasis and angiogenesis of esophageal squamous cell carcinoma.

BACKGROUND & AIMS Solid tumors often become hypoxic, leading to activation of hypoxia-response genes. We investigated the effects of overexpression of the hypoxia response genes eIF5A2 in esophageal squamous cell carcinoma (ESCC). METHODS We used quantitative real-time polymerase chain reaction and immunohistochemistry analyses to compare expression of eIF5A2 between paired ESCC samples and nontumor esophageal tissues, and fluorescence in situ hybridization to detect gene copy-number alterations. Luciferase reporter and chromatin immunoprecipitation assays were used to study interactions between eIF5A2 and hypoxia-inducible factor-1α (HIF1α). We determined the effects of eIF5A2 overexpression and knockdown in ESCC cell lines and growth of ESCC xenograft tumors in nude mice. RESULTS Levels of eIF5A2 messenger RNA and protein were increased in >40% of ESCC samples compared with matched nontumor tissues, along with levels of HIF1α and vascular endothelial growth factor. Increased levels of EIF5A2 were significantly associated with ESCC metastasis to lymph nodes (P < .001) and tissue invasion (P = .037), and shorter survival times of patients (P < .001). Amplification of eIF5A2 was detected in 35.14% of ESCC samples that overexpressed eIF5A2. Hypoxia increased expression of eIF5A2 4- to 8-fold in ESCC cell lines; we observed bidirectional regulation between eIF5A2 and HIF1α. Transient transfection of ESCC cell lines with eIF5A2 increased their migratory and invasive abilities and markers of the epithelial to mesenchymal transition, and eIF5A2 knockdown or HIFα inhibition reduced these. In mice, xenograft tumors grown from ESCC cells that expressed eIF5A2 formed tumors more rapidly than cells that expressed only vector (controls); they also expressed higher levels of HIF1α and vascular endothelial growth factor, and formed more microvessels than controls. Knockdown of eIF5A2 in ESCC cells with interfering RNAs reduced their growth as xenograft tumors in mice, particularly when mice were given docetaxel or cisplatin. CONCLUSIONS eIF5A2 is overexpressed by gene amplification or hypoxia in ESCCs, and associated with up-regulation of HIF1α, metastasis, and shorter survival times of patients. Increased expression of eIF5A2 increases metastasis and angiogenesis in ESCC via the HIF1α-mediated signaling pathway.
Supplementation of procyanidins B2 attenuates photooxidation-induced apoptosis in ARPE-19 cells

Abstract During the aging process, dimers of dietary vitamin A accumulated in retinal pigment epithelium (RPE) cells. Vitamin A dimer-mediated photooxidation resulted in RPE apoptosis, which is associated with age-related degenerative disease of retina, leading to blindness. It has been reported that proanthocyanidin-rich grape seed extract reduces oxidative stress in the eye. In this study, we investigated the underlying mechanism of photooxidation-induced apoptosis inhibition by procyanidins B2 (PB2), one of the main components of grape seed proanthocyanidin. To mimic vitamin A dimer-mediated photooxidation, ARPE-19 cells that accumulated vitamin A dimer, A2E, were used as a model system. Exposure of A2E loaded ARPE-19 cells to blue light induced ER stress and resulted in significant apoptosis. Pretreatment of blue light-exposed A2E containing ARPE-19 cells with PB2 inhibited apoptosis, increased the ratio of Bcl-2/Bax in the mitochondria, attenuated ROS and cytochrome c release, and decreased caspase cleavage. Additionally, PB2 inhibited the phosphorylation of ER stress markers elF2α and IRE1α and reduced CHOP expression. Moreover, PB2 inhibition of apoptosis is dependent on the UPR chaperone GRP78, indicating PB2 inhibits vitamin A dimer-mediated apoptosis in RPE cells by activating the UPR.
Canada–US productivity gap: The role of competition intensity differential

The relative lack of competitive pressure in product markets and lower investment in both fundamental and applied innovation are among the potential factors that have been put forward to explain Canada’s weak productivity performance with respect to the US. Since competition is generally seen as the single leading catalyst for fundamental and applied innovation, this paper analyzes the role of product market competition in the Canada–US productivity level gap. We develop an empirical framework in which competition exerts both direct and indirect effects on productivity, with the indirect impact coming through fundamental and applied innovation. We find statistically significant evidence that the competition intensity differential (between Canada and the US) has contributed to the Canada–US productivity level gap directly, as well as indirectly through lower investment in both R&D activities and M&E (including ICT) investment. We also find statistically significant evidence that Canada’s relatively poor performance in both productivity and M&E (including ICT) investment have acted as a self-reinforcing mechanism, which further causes detriment to the country’s productivity.
Class III Homeodomain-Leucine Zipper Gene Family Members Have Overlapping, Antagonistic, and Distinct Roles in Arabidopsis Developmentw⃞

The Arabidopsis thaliana genome contains five class III homeodomain-leucine zipper genes. We have isolated loss-of-function alleles for each family member for use in genetic analysis. This gene family regulates apical embryo patterning, embryonic shoot meristem formation, organ polarity, vascular development, and meristem function. Genetic analyses revealed a complex pattern of overlapping functions, some of which are not readily inferred by phylogenetic relationships or by gene expression patterns. The PHABULOSA and PHAVOLUTA genes perform overlapping functions with REVOLUTA, whereas the PHABULOSA, PHAVOLUTA, and CORONA/ATHB15 genes perform overlapping functions distinct from REVOLUTA. Furthermore, ATHB8 and CORONA encode functions that are both antagonistic to those of REVOLUTA within certain tissues and overlapping with REVOLUTA in other tissues. Differences in expression patterns explain some of these genetic interactions, whereas other interactions are likely attributable to differences in protein function as indicated by cross-complementation studies.
Advantages of virtual reality in the rehabilitation of balance and gait

Background Virtual reality (VR) has emerged as a therapeutic tool facilitating motor learning for balance and gait rehabilitation. The evidence, however, has not yet resulted in standardized guidelines. The aim of this study was to systematically review the application of VR-based rehabilitation of balance and gait in 6 neurologic cohorts, describing methodologic quality, intervention programs, and reported efficacy. Methods This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. VR-based treatments of Parkinson disease, multiple sclerosis, acute and chronic poststroke, traumatic brain injury, and cerebral palsy were researched in PubMed and Scopus, including earliest available records. Therapeutic validity (CONTENT scale) and risk of bias in randomized controlled trials (RCT) (Cochrane Collaboration tool) and non-RCT (Newcastle-Ottawa scale) were assessed. Results Ninety-seven articles were included, 68 published in 2013 or later. VR improved balance and gait in all cohorts, especially when combined with conventional rehabilitation. Most studies presented poor methodologic quality, lacked a clear rationale for intervention programs, and did not utilize motor learning principles meticulously. RCTs with more robust methodologic designs were widely recommended. Conclusion Our results suggest that VR-based rehabilitation is developing rapidly, has the potential to improve balance and gait in neurologic patients, and brings additional benefits when combined with conventional rehabilitation. This systematic review provides detailed information for developing theory-driven protocols that may assist overcoming the observed lack of argued choices for intervention programs and motor learning implementation and serves as a reference for the design and planning of personalized VR-based treatments. Registration PROSPERO CRD42016042051.
A resource-advantage perspective on pricing: shifting the focus from ends to means-end in pricing research?

This paper contributes to a long-lasting debate between practitioners who argue that academia is unable to understand what pricing is all about and academics who criticize practitioner pricing approaches for lacking rigor or rationality. The paper conceptualizes a resource-advantage (R-A) perspective on pricing by drawing on the R-A theory of competition. After a review of R-A theory, the paper integrates the price discretion concept and pricing as a spanning competence by introducing a separation between resources that create and resources that extract value, thereby expanding R-A theory to pricing. The perspective aims to shed light on how the process of competition helps organizations to learn/benefit from pricing capabilities. The research shifts the focus of pricing research from an equilibrium-based static view to a dynamic, disequilibrium-provoking pricing competence. In this way, it draws attention to what is perhaps most relevant to pricing in practice: the actual means necessary to determine price.
Political Influence of Firms in the Tradables and Non-Tradables Sectors: A Cross-Country Analysis

Recent theoretical studies have shown that firms lobby government agencies to influence the structure of trade policies. This article empirically examines whether firms classified as either exporting or import‐competing (i.e. firms in the tradables sector) have differential levels of political influence relative to domestic firms that only produce non‐traded goods (i.e. firms in the non‐tradables sector). We use a rich firm‐level, cross‐sectional dataset from the World Business Environment Survey to achieve this objective. Results from the analysis reveal that exporting or import‐competing firms do have more political influence relative to domestic firms that neither export nor produce import‐competing goods. Market structure, firm age, firm size, government ownership, and dependence on public infrastructure also affect the extent of political influence that firms have.
Capnocytophaga canimorsus Prosthetic Aortitis in an HIV-Positive Woman

ABSTRACT A 49-year-old woman with a 15-year history of HIV-hepatitis C virus coinfection had aortic valve and ascending aorta replacement in 2007. She presented with abdominal pain, episodic diarrhea, and profuse sweating in 2010. Thoracoabdominal positron emission tomography-computed tomography finally suggested infectious aortitis, a diagnosis confirmed by a blood culture positive for Capnocytophaga canimorsus.
An Automatic Cardiac Arrhythmia Classification System With Wearable Electrocardiogram

This paper presents an automatic wearable electrocardiogram (ECG) classification and monitoring system with stacked denoising autoencoder (SDAE). We use a wearable device with wireless sensors to obtain the ECG data, and send these ECG data to a computer with Bluetooth 4.2. Then, these ECG data are classified by the automatic cardiac arrhythmia classification system. First, the ECG feature representation is learned by the SDAE with sparsity constraint. Then, the softmax regression is used to classify the ECG beats. In the fine-tuning phase, an active learning is added to improve the performance. In the active learning phase, we use the method that relies on the deep neural networks posterior probabilities to associate confidence measures to select the most informative samples. Breaking-ties and modified breaking-ties methods are used to select the most informative samples. We validate the proposed method on the well-known MIT-BIH arrhythmia database and ECG data obtained from the wearable device. We follow the recommendations of the Association for the Advancement of Medical Instrumentation for class labeling and results presentation. The results show that the classification performance of our proposed approach outperforms the most of the state-of-the-art methods.
Walking Down Wall Street with a Tablet: A Survey of Stock Market Predictions Using the Web

‘A blindfolded chimpanzee throwing darts at The Wall Street Journal could select a portfolio that would do as well as the (stock market) experts’ [Malkiel (2003) The efficient market hypothesis and its critics. Journal of Economic Perspectives 17(1): 59–82)]. However, what if this chimpanzee could browse the Internet before throwing any darts? In this paper, we ask whether online news has any influence on the financial market, and we also investigate how much influence it has. We explore the burgeoning literature on the predictability of financial movements using online information and report its mixed findings. In addition, we collate the efforts of various disciplines, including economics, text mining, sentiment analysis and machine learning, and we offer suggestions for future research.
Subdividing the commons: the politics of property rights transformation in Kenya's Maasailand

"This paper discusses the internal processes and decisions that characterized the transition from collectively held group ranches to individualized property systems among the Maasai pastoralists of Kajiado district in Kenya. It addresses the question of why group ranch members would demand individualized property systems, but then turn against the outcome. In addressing this puzzle the paper discusses the process of land allocation and distribution during group ranch subdivision. It examines who the main actors were during subdivision, their degree of latitude in crafting and changing rules, and the interactions between Maasai and state institutions. Findings suggest that, because the process by which property rights change is so intertwined with politics, we may need to move beyond economic models of relative price changes and state enforcement in order to better understand such transitions. Models that accommodate competition by actors and the possibility that state actors may not provide the arbitration or enforcement that is often taken for granted are more useful for analyzing the complexities of shifting property rights. When the possibility for conflict and competition is factored into the property rights equation, the relative gains from privatizing/individualizing may not be as large or as obvious as anticipated. " Author's Abstract
Joint Learning of Evolving Links for Dynamic Network Embedding

This paper studies the problem of learning node embeddings (a.k.a. distributed representations) for dynamic networks. The embedding methods allocate each node in network with a d-dimensions vector, which can generalize across various tasks, such as item recommendation, node labeling, and link prediction. In practice, many real-world networks are evolving with nodes/links added or deleted. However, most of the proposed methods are focusing on static networks. Although some previous researches have shown some promising results to handle the dynamic scenario, they just considered the added links and ignored the deleted ones. In this work, we designed a joint learning of added and deleted links model, named RDEM, for dynamic network embedding.
Antecedents of paperless income tax filing by young professionals in India: an exploratory study

Purpose – The aim of this study is to investigate the likely antecedents of young Indian professionals' behavioral intention (BI) to use the income tax e‐filing service.Design/methodology/approach – Prior to data collection, for which a self‐administered survey was conducted, lecture‐demonstrations were arranged to familiarize the potential respondents with the income tax e‐filing service. The psychometric data were analyzed for reliability and construct validity of the measures, and thereafter a regression analysis was carried out.Findings – Regression results showed that antecedents of young Indian professionals' BI to use the income tax e‐filing service are: perceived ease‐of‐use, personal innovativeness in information technology, relative advantage (RA), performance of e‐filing service, and compatibility (COMP). Originality/value – Past studies on income tax e‐filing are beset by problems of adopter bias, and failure to treat trust as a multi‐dimensional. In addition, these studies are skewed towards t.
Serum‐free transient protein production system based on adenoviral vector and PER.C6 technology: High yield and preserved bioactivity†

Stable E1 transformed cells, like PER.C6, are able to grow at scale and to high cell densities. E1‐deleted adenoviruses replicate to high titer in PER.C6 cells whereas subsequent deletion of E2A from the vector results in absence of replication in PER.C6 cells and drastically lowers the expression of adenovirus proteins in such cells. We therefore considered the use of an ΔE1/ΔE2 type 5 vector (Ad5) to deliver genes to PER.C6 cells growing in suspension with the aim to achieve high protein yield. To evaluate the utility of this system we constructed ΔE1/ΔE2 vector carrying different classes of protein, that is, the gene coding for spike protein derived from the Coronavirus causing severe acute respiratory syndrome (SARS‐CoV), a gene coding for the SARS‐CoV receptor or the genes coding for an antibody shown to bind and neutralize SARS‐CoV (SARS‐AB). The ΔE1/ΔE2A‐vector backbones were rescued on a PER.C6 cell line engineered to constitutively over express the Ad5 E2A protein. Exposure of PER.C6 cells to low amounts (30 vp/cell) of ΔE1/ΔE2 vectors resulted in highly efficient (>80%) transduction of PER.C6 cells growing in suspension. The efficient cell transduction resulted in high protein yield (up to 60 picogram/cell/day) in a 4 day batch production protocol. FACS and ELISA assays demonstrated the biological activity of the transiently produced proteins. We therefore conclude that ΔE1/ΔE2 vectors in combination with the PER.C6 technology may provide a viable answer to the increasing demand for high quality, high yield recombinant protein. Biotechnol. Bioeng. 2008;100: 273–283. © 2007 Wiley Periodicals, Inc.