Slavic languages are Type 3 languages: replies

Peer Reviewe

Wie viel Syntax braucht die Semantik, und wie viel Semantik enthält die Syntax?

The nature of the syntax-semantics interface is a controversial issue in grammar theory. Current theorizing in Generative Grammar endorses a 'tight fit' conception. Abstract syntax is to provide distinct structural configurations for distinct semantic entities in terms of semantically typed functional heads that project functional phrases. Positive and convincing evidence for the tight fit approach is wanting. The opposite viewpoint, once called the autonomy viewpoint in Generative Grammar, takes syntax to be a module of grammar that is not necessarily tailored to the needs of semantics. It is an algorithm that maps strings on structures and vice versa. The semantic construction algorithm maps syntactic structure on adequate semantic domains. In this contribution, evidence from three areas syntax of adverbials, syntax of negation, alleged semantic correlates of argument structure are reviewed in order to clarify which of the two viewpoints is more likely to be adequate. It is argued that the autonomy approach is superior

Current Source Density Estimation Enhances the Performance of Motor-Imagery Related Brain-Computer Interface

The objective is to evaluate the impact of EEG referencing schemes and spherical surface Laplacian (SSL) methods on the classification performance of motor-imagery (MI)-related brain-computer interface systems. Two EEG referencing schemes: common referencing and common average referencing and three surface Laplacian methods: current source density (CSD), finite difference method, and SSL using realistic head model were implemented separately for pre-processing of the EEG signals recorded at the scalp. A combination of filter bank common spatial filter for features extraction and support vector machine for classification was used for both pairwise binary classifications and four-class classification of MI tasks. The study provides three major outcomes: 1) the CSD method performs better than CR, providing a significant improvement of 3.02% and 5.59% across six binary classification tasks and four-class classification task, respectively; 2) the combination of a greater number of channels at the pre-processing stage as compared with the feature extraction stage yields better classification accuracies for all the Laplacian methods; and 3) the efficiency of all the surface Laplacian methods reduced significantly in the case of a fewer number of channels considered during the pre-processing

Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface

The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to non-stationarity based covariate shifts, the input data distributions of EEG-based BCI systems change during inter- and intra-session transitions, which poses great difficulty for developments of online adaptive data-driven systems. Ensemble learning approaches have been used previously to tackle this challenge. However, passive scheme based implementation leads to poor efficiency while increasing high computational cost. This paper presents a novel integration of covariate shift estimation and unsupervised adaptive ensemble learning (CSE-UAEL) to tackle non-stationarity in motor-imagery (MI) related EEG classification. The proposed method first employs an exponentially weighted moving average model to detect the covariate shifts in the common spatial pattern features extracted from MI related brain responses. Then, a classifier ensemble was created and updated over time to account for changes in streaming input data distribution wherein new classifiers are added to the ensemble in accordance with estimated shifts. Furthermore, using two publicly available BCI-related EEG datasets, the proposed method was extensively compared with the state-of-the-art single-classifier based passive scheme, single-classifier based active scheme and ensemble based passive schemes. The experimental results show that the proposed active scheme based ensemble learning algorithm significantly enhances the BCI performance in MI classifications

Transcriptome analysis of human cancer reveals a functional role of Heme Oxygenase-1 in tumor cell adhesion

<p>Abstract</p> <p>Background</p> <p>Heme Oxygenase-1 (HO-1) is expressed in many cancers and promotes growth and survival of neoplastic cells. Recently, HO-1 has been implicated in tumor cell invasion and metastasis. However, the molecular mechanisms underlying these biologic effects of HO-1 remain largely unknown. To identify a common mechanism of action of HO-1 in cancer, we determined the global effect of HO-1 on the transcriptome of multiple tumor entities and identified a universal HO-1-associated gene expression signature.</p> <p>Results</p> <p>Genome-wide expression profiling of Heme Oxygenase-1 expressing versus HO-1 silenced BeWo choriocarcinoma cells as well as a comparative meta-profiling of the preexisting expression database of 190 human tumors of 14 independent cancer types led to the identification of 14 genes, the expression of which correlated strongly and universally with that of HO-1 (P = 0.00002). These genes included regulators of cell plasticity and extracellular matrix (ECM) remodeling (MMP2, ADAM8, TGFB1, BGN, COL21A1, PXDN), signaling (CRIP2, MICB), amino acid transport and glycosylation (SLC7A1 and ST3GAL2), estrogen and phospholipid biosynthesis (AGPAT2 and HSD17B1), protein stabilization (IFI30), and phosphorylation (ALPPL2). We selected PXDN, an adhesion molecule involved in ECM formation, for further analysis and functional characterization. Immunofluorescence and Western blotting confirmed the positive correlation of expression of PXDN and HO-1 in BeWo cancer cells as well as co-localization of these two proteins in invasive extravillous trophoblast cells. Modulation of HO-1 expression in both loss-of and gain-of function cell models (BeWo and 607B melanoma cells, respectively) demonstrated a direct relationship of HO-1 expression with cell adhesion to Fibronectin and Laminin coated wells. The adhesion-promoting effects of HO-1 were dependent on PXDN expression, as loss of PXDN in HO-1 expressing BeWo and 607B cells led to reduced cell attachment to Laminin and Fibronectin coated wells.</p> <p>Conclusions</p> <p>Collectively, our results show that HO-1 expression determines a distinct 'molecular signature' in cancer cells, which is enriched in genes associated with tumorigenesis. The protein network downstream of HO-1 modulates adhesion, signaling, transport, and other critical cellular functions of neoplastic cells and thus promotes tumor cell growth and dissemination.</p