Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Cerebral Autoregulation in Pregnancy and the Risk of Preeclampsia

Preeclampsia may affect severely the cerebral circulation leading to impairment of cerebral autoregulation, edema, and ischemia. It is not known whether impaired autoregulation occurs before the clinical onset of preeclampsia, and whether this can predict the occurrence of preeclampsia. Seventy-two women at 25 to 28 weeks of gestation were studied. Control values were derived from 26 nonpregnant women. Dynamic properties of cerebral autoregulation (DCA) were measured in the middle and posterior cerebral artery using transcranial Doppler and transfer function analysis (phase and gain) of respiratory-induced 0.1 Hz hemodynamic oscillations. Uterine artery ultrasound was performed to search for a notch sign as an early marker of general endothelial dysfunction. All women were followed up until 6 weeks after delivery for the occurrence of preeclampsia. The autoregulation parameter gain did not differ between pregnant and nonpregnant women. Phase was slightly but significantly higher in pregnant women, indicating better DCA. Women with a notch sign did not show altered DCA. A history of preeclampsia during a previous pregnancy was associated with lower phase in middle cerebral artery and posterior cerebral artery (P<0.05 each). During follow-up, 9 women developed preeclampsia. None of the DCA parameters were associated with the occurrence of preeclampsia. In conclusion, DCA is well preserved during late midterm pregnancy, even in women with disturbed uterine blood flow. Yet, pregnant women with preeclampsia in a previous pregnancy seem to have poorer DCA. Although limited in statistical power, this study does not support DCA as a strong early risk marker of preeclampsia.
Smart Cities and the Future Internet : Innovation ecosystems of embedded spatial intelligence

Intelligent or smart cities rely on collaboration networks among human communities, innovation ecosystems, and digital infrastructure, applications and e-services, which enable the urban challenges of competitiveness, sustainability and inclusion to be addressed more efficiently. This urban paradigm is characterized by continuous transformation, fuelled by evolutions in innovation ecosystems, broadband networks and web technologies. This paper explores changes which are currently being introduced by future Internet research and outlines how cities and regions are affected by emerging Internet technologies, smart environments, and the resulting changes to innovation ecosystems. The paper is based on a corpus of foresight exercises, roadmaps to future Internet technologies and FIRE experimental facilities, OECD and governmental reports on the future of the Internet economy and surveys on hype cycles for smart city technologies. The first section is a short introduction to intelligent city concepts and looks at the turn towards future Internet technologies. Sections two and three are about the drivers of embedded spatial intelligence of cities guided by the rise of sensor networks and solutions embedded into the physical space of cities. The next four sections discuss the technological context of the future Internet and the expected impact of the Internet-of-Things, sensors, tags and RFID, semantic web, and cloud computing on smart cities. The final section is concerned with the new innovation ecosystems emerging from embedded spatial intelligence where citizen empowerment meets with smart environments.
Pseudomonas aeruginosa Utilizes the Type III Secreted Toxin ExoS to Avoid Acidified Compartments within Epithelial Cells

Invasive Pseudomonas aeruginosa (PA) can enter epithelial cells wherein they mediate formation of plasma membrane bleb-niches for intracellular compartmentalization. This phenotype, and capacity for intracellular replication, requires the ADP-ribosyltransferase (ADPr) activity of ExoS, a PA type III secretion system (T3SS) effector protein. Thus, PA T3SS mutants lack these capacities and instead traffic to perinuclear vacuoles. Here, we tested the hypothesis that the T3SS, via the ADPr activity of ExoS, allows PA to evade acidic vacuoles that otherwise suppress its intracellular viability. The acidification state of bacteria-occupied vacuoles within infected corneal epithelial cells was studied using LysoTracker to visualize acidic, lysosomal vacuoles. Steady state analysis showed that within cells wild-type PAO1 localized to both membrane bleb-niches and vacuoles, while both exsA (transcriptional activator) and popB (effector translocation) T3SS mutants were only found in vacuoles. The acidification state of occupied vacuoles suggested a relationship with ExoS expression, i.e. vacuoles occupied by the exsA mutant (unable to express ExoS) were more often acidified than either popB mutant or wild-type PAO1 occupied vacuoles (p < 0.001). An exoS-gfp reporter construct pJNE05 confirmed that high exoS transcriptional output coincided with low occupation of acidified vacuoles, and vice versa, for both popB mutants and wild-type bacteria. Complementation of a triple effector null mutant of PAO1 with exoS (pUCPexoS) reduced the number of acidified bacteria-occupied vacuoles per cell; pUCPexoSE381D which lacks ADPr activity did not. The H+-ATPase inhibitor bafilomycin rescued intracellular replication to wild-type levels for exsA mutants, showing its viability is suppressed by vacuolar acidification. Taken together, the data show that the mechanism by which ExoS ADPr activity allows intracellular replication by PA involves suppression of vacuolar acidification. They also show that variability in ExoS expression by wild-type PA inside cells can differentially influence the fate of individual intracellular bacteria, even within the same cell.
Accelerating One-Pass Clustering by Cluster Selection Racing

This paper introduces a racing mechanism in the cluster selection process for one-pass clustering algorithms. We focus on cases where data are not numerical vectors and where it is not necessarily possible to compute a mean for each cluster. In this case, the distance of each point to existing clusters can be computed exhaustively with a quadratic complexity which is not tractable in most of nowadays use cases. In this paper we first introduce a stochastic approach for estimating the distance of each new data point to existing clusters based on Hoeffding and Bernstein bounds, that reduces the number of computations by simultaneously selecting the quantity of data to be sampled and by eliminating the non-competitive clusters. Second, this paper shows that it is possible to improve the efficiency of our approach by reducing the theoretical values of the Hoeffding and Bernstein bounds. Our algorithms, tested on real data sets, provide significant acceleration of the one-pass clustering algorithms, while making less error (or any depending on parameters) than one-pass clustering algorithm with fixed number of comparisons with each cluster.
Phone Set Generation Based on Acoustic and Contextual Analysis for Multilingual Speech Recognition

This study presents a novel approach to generating phone units generation for the recognition of multilingual speech. Acoustic and contextual analysis is performed to characterize multilingual phonetic units for phone set generation. A confusion matrix combining acoustic and contextual similarities between every two phonetic units is constructed for phonetic unit clustering. Acoustic likelihood and hyperspace analog to language (HAL) model are adopted for acoustic similarity and contextual similarity estimation of phone models, respectively. Experiments show that the generated phone set provides a compact and robust set that considers acoustic and contextual information for multilingual speech recognition.
Supply Restoration in Power Distribution Systems - A Benchmark for Planning under Uncertainty

This paper proposes the problem of supply restoration in faulty power distribution systems as a benchmark for planning under uncertainty. This benchmark, which is derived from a significant real-world case, is both simple to understand and easily scalable. The goal is to reconfigure the distribution network to resupply a maximum of consumers affected by the faults. Due to sensor and actuator uncertainty, the location of the faulty areas and the current network configuration are only partially observable. This makes the problem very challenging.
Statistical selection of relevant subspace projections for outlier ranking

Outlier mining is an important data analysis task to distinguish exceptional outliers from regular objects. For outlier mining in the full data space, there are well established methods which are successful in measuring the degree of deviation for outlier ranking. However, in recent applications traditional outlier mining approaches miss outliers as they are hidden in subspace projections. Especially, outlier ranking approaches measuring deviation on all available attributes miss outliers deviating from their local neighborhood only in subsets of the attributes. In this work, we propose a novel outlier ranking based on the objects deviation in a statistically selected set of relevant subspace projections. This ensures to find objects deviating in multiple relevant subspaces, while it excludes irrelevant projections showing no clear contrast between outliers and the residual objects. Thus, we tackle the general challenges of detecting outliers hidden in subspaces of the data. We provide a selection of subspaces with high contrast and propose a novel ranking based on an adaptive degree of deviation in arbitrary subspaces. In thorough experiments on real and synthetic data we show that our approach outperforms competing outlier ranking approaches by detecting outliers in arbitrary subspace projections.
Gene Alterations of Ovarian Cancer Cells Expressing Estrogen Receptors by Estrogen and Bisphenol A Using Microarray Analysis

Since endocrine disrupting chemicals (EDCs) may interfere with the endocrine system(s) of our body and have an estrogenicity, we evaluated the effect(s) of bisphenol A (BPA) on the transcriptional levels of altered genes in estrogen receptor (ER)-positive BG-1 ovarian cancer cells by microarray and real-time polymerase-chain reaction. In this study, treatment with 17β-estradiol (E2) or BPA increased mRNA levels of E2-responsive genes related to apoptosis, cancer and cell cycle, signal transduction and nucleic acid binding etc. In parallel with their microarray data, the mRNA levels of some altered genes including RAB31_MEMBER RAS ONCOGENE FAMILY (U59877), CYCLIN D1 (X59798), CYCLIN-DEPENDENT KINASE 4 (U37022), IGF-BINDING PROTEIN 4 (U20982), and ANTI-MULLERIAN HORMONE (NM_000479) were significantly induced by E2 or BPA in this cell model. These results indicate that BPA in parallel with E2 induced the transcriptional levels of E2-responsive genes in an estrogen receptor (ER)-positive BG-1 cells. In conclusion, these microarray and real-time polymerase-chain reaction results indicate that BPA, a potential weak estrogen, may have estrogenic effect by regulating E2-responsive genes in ER-positive BG-1 cells and BG-1 cells would be the best in vitro model to detect these estrogenic EDCs.
Nonlinear Speed Regulation with Electric Power Generation for Spark-Ignition Engine

A speed regulation scheme is investigated for spark-ignition (SI) engine with alternator. In the case that the magnetic torque variation with the irregular operations of the electric loads is treated as the measurable disturbance, a state feedback controller with the magnetic torque feedforward is designed to regulate the engine speed. The approximate algorithm is also discussed for the implementation in practice. Finally, to demonstrate the validity of the proposed approaches, the algorithm is examined on the 3GR-FSE engine test bench. The experiment results show that the speed servo performance is improved.
Robust Extraction of Named Entity Including Unfamiliar Word

This paper proposes a novel method to extract named entities including unfamiliar words which do not occur or occur few times in a training corpus using a large unannotated corpus. The proposed method consists of two steps. The first step is to assign the most similar and familiar word to each unfamiliar word based on their context vectors calculated from a large unannotated corpus. After that, traditional machine learning approaches are employed as the second step. The experiments of extracting Japanese named entities from IREX corpus and NHK corpus show the effectiveness of the proposed method.
Dual comparison one cycle control for single phase AC to DC converters

Classical control and one cycle control of current are popular methods used to generate PWM pulses in active rectifiers for ac-dc power conversion. One cycle control has lower control complexity and can be implemented using simple analog circuits and digital circuits, when compared with the classical approach. However, it also suffers from distortion in input current when the converter is lightly loaded and steady state dc offset in input current under all load conditions. In this paper a new dual comparison one cycle control is proposed which overcomes the above limitations. The proposed control strategy makes use of two comparators which compares sensed input current and inverted sensed input current with a saw-tooth carrier to generate gating signals for switching devices. Detailed simulation studies are carried to validate the viability of the proposed control.
Regular Articles: Some General Guidelines for Choosing Missing Data Handling Methods in Educational Research

The effect of a number of factors, such as the choice of analytical method, the handling method for missing data, sample size, and proportion of missing data, were examined to evaluate the effect of missing data treatment on accuracy of estimation. A methodological approach involving simulated data was adopted. One outcome of the statistical analyses undertaken in this study is the formulation of easy-to-implement guidelines for educational researchers that allows one to choose one of the following factors when all others are given: sample size, proportion of missing data in the sample, method of analysis, and missing data handling method.
A Novel Error Detection due to Joint CRC Aided Denoise-and-Forward Network Coding for Two-Way Relay Channels

In wireless two-way (TW) relay channels, denoise-and-forward (DNF) network coding (NC) is a promising technique to achieve spectral efficiency. However, unsuccessful detection at relay severely deteriorates the diversity gain, as well as end-to-end pairwise error probability (PEP). To handle this issue, a novel joint cyclic redundancy code (CRC) check method (JCRC) is proposed in this paper by exploiting the property of two NC combined CRC codewords. Firstly, the detection probability bounds of the proposed method are derived to prove its efficiency in evaluating the reliability of NC signals. On the basis of that, three JCRC aided TW DNF NC schemes are proposed, and the corresponding PEP performances are also derived. Numerical results reveal that JCRC aided TW DNF NC has similar PEP comparing with the separate CRC one, while the complexity is reduced to half. Besides, it demonstrates that the proposed schemes outperform the conventional one with log-likelihood ratio threshold.
Dynamic load balancing for distributed search

This paper examines how computation can be mapped across the nodes of a distributed search system to effectively utilize available resources. We specifically address computationally intensive search of complex data, such as content-based retrieval of digital images or sounds, where sophisticated algorithms must be evaluated on the objects of interest. Since these problems require significant computation, we distribute the search over a collection of compute nodes, such as active storage devices, intermediate processors and host computers. A key challenge with mapping the desired computation to the available resources is that the most efficient distribution depends on several factors: relative power and number of compute nodes; network bandwidth between the compute nodes; the cost of evaluating query predicates; and the selectivity of the given query. This wide range of variables renders manual partitioning of the computation infeasible, particularly since some of the parameters (e.g., available network bandwidth) can change during the course of a search. This paper proposes several techniques for dynamic partitioning of computation, and demonstrates that they can significantly improve efficiency for distributed search applications.
DNA-SCARS: distinct nuclear structures that sustain damage-induced senescence growth arrest and inflammatory cytokine secretion

DNA damage can induce a tumor suppressive response termed cellular senescence. Damaged senescent cells permanently arrest growth, secrete inflammatory cytokines and other proteins and harbor persistent nuclear foci that contain DNA damage response (DDR) proteins. To understand how persistent damage foci differ from transient foci that mark repairable DNA lesions, we identify sequential events that differentiate transient foci from persistent foci, which we term ‘DNA segments with chromatin alterations reinforcing senescence’ (DNA-SCARS). Unlike transient foci, DNA-SCARS associate with PML nuclear bodies, lack the DNA repair proteins RPA and RAD51, lack single-stranded DNA and DNA synthesis and accumulate activated forms of the DDR mediators CHK2 and p53. DNA-SCARS form independently of p53, pRB and several other checkpoint and repair proteins but require p53 and pRb to trigger the senescence growth arrest. Importantly, depletion of the DNA-SCARS-stabilizing component histone H2AX did not deplete 53BP1 from DNA-SCARS but diminished the presence of MDC1 and activated CHK2. Furthermore, depletion of H2AX reduced both the p53-dependent senescence growth arrest and p53-independent cytokine secretion. DNA-SCARS were also observed following severe damage to multiple human cell types and mouse tissues, suggesting that they can be used in combination with other markers to identify senescent cells. Thus, DNA-SCARS are dynamically formed distinct structures that functionally regulate multiple aspects of the senescent phenotype.
Allosteric Modulation of G Protein Coupled Receptors by Cytoplasmic, Transmembrane and Extracellular Ligands

G protein coupled receptors (GPCRs) bind diverse classes of ligands, and depending on the receptor, these may bind in their transmembrane or the extracellular domains, demonstrating the principal ability of GPCRs to bind ligand in either domains. Most recently, it was also observed that small molecule ligands can bind in the cytoplasmic domain, and modulate binding and response to extracellular or transmembrane ligands. Thus, all three domains in GPCRs are potential sites for allosteric ligands, and whether a ligand is allosteric or orthosteric depends on the receptor. Here, we will review the evidence supporting the presence of putative binding pockets in all three domains of GPCRs and discuss possible pathways of communication between these pockets.
The architecture of the DIVA processing-in-memory chip

The DIVA (Data IntensiVe Architecture) system incorporates a collection of Processing-In-Memory (PIM) chips as smart-memory co-processors to a conventional microprocessor. We have recently fabricated prototype DIVA PIMs. These chips represent the first smart-memory devices designed to support virtual addressing and capable of executing multiple threads of control. In this paper, we describe the prototype PIM architecture. We emphasize three unique features of DIVA PIMs, namely, the memory interface to the host processor, the 256-bit wide datapaths for exploiting on-chip bandwidth, and the address translation unit. We present detailed simulation results on eight benchmark applications. When just a single PIM chip is used, we achieve an average speedup of 3.3X over host-only execution, due to lower memory stall times and increased fine-grain parallelism. These 1-PIM results suggest that a PIM-based architecture with many such chips yields significantly higher performance than a multiprocessor of a similar scale and at a much reduced hardware cost.
Regulatory mode shift of Tbc1d1 is required for acquisition of insulin-responsive GLUT4-trafficking activity

Tbc1d1 is involved in AICAR-dependent GLUT4 liberation. Tbc1d1 acquires temporal insulin responsiveness with AICAR pretreatment. This shift in regulatory mode requires Ser- 237 phosphorylation and the PTB1 domain. PTB1 mutants exhibit no shift in regulatory mode and thus no insulin responsiveness.
A Splice Mutation and mRNA Decay of EXT2 Provoke Hereditary Multiple Exostoses

Background Hereditary multiple exostoses (HME) is an autosomal dominant disease. The classical paradigm of mutation screening seeks to relate alterations in the exostosin glycosyltransferase genes, EXT1 and EXT2, which are responsible for over 70% of HME cases. However, the pathological significance of the majority of these mutations is often unclear. Methods In a Chinese family with HME, EXT1 and EXT2 genes were screened by direct sequencing. The consequence of a detected mutant was predicted by in silico analysis and confirmed by mRNA analysis. The EXT1 and EXT2 mRNA and protein levels and the HS patterns in the HME patients were compared with those in healthy controls. Results A heterozygous transition (c.743+1G>A) in the EXT2 gene, which co-segregated with the HME phenotype in this family, was identified. The G residue at position +1 in intron 4 of EXT2 was predicted to be a 5′ donor splice site. The mRNA analysis revealed an alternative transcript with a cryptic splice site 5 bp downstream of the wild-type site, which harbored a premature stop codon. However, the predicted truncated protein was not detected by western blot analysis. Decay of the mutant mRNA was shown by clone sequencing and quantification analysis. The corresponding downregulation of the EXT2 mRNA will contribute to the abnormal EXT1/EXT2 ratio and HS pattern that were detected in the patients with HME. Conclusion The heterozygous mutation c.743+1G>A in the EXT2 gene causes HME as a result of abnormal splicing, mRNA decay, and the resulting haploinsufficiency of EXT2.
Consumption, Investment and International Linkages

This paper seeks to explain the strong contemporaneous relationship between Australian and foreign output growth. It does so by adopting a more disaggregated approach than previous work, focussing in particular on consumption and investment. The theoretical frameworks of the permanent income hypothesis for consumption and the cash flow version of the neo-classical model of investment are used to identify potential foreign linkages. Some evidence of a foreign linkage through consumption is established. Little evidence is found of foreign influences on domestic investment, although an indirect channel operating through business confidence is identified. The paper also provides evidence of a decline in liquidity constraints since financial deregulation, and confirms previous evidence of the importance of cash flow in determining investment.