Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of ginsenoside Rb1 on oxidative stress injury in rat spinal cords by regulating the eNOS/Nrf2/HO-1 signaling pathway

The present study aimed to investigate whether ginsenoside Rb1 (G-Rb1) attenuates spinal cord injury-associated oxidative stress in rats by regulating the endothelial nitric oxide synthase eNOS/nuclear factor erythroid 2-related factor 2 (Nrf2)/heme oxygenase (HO)-1 signaling pathway. Sprague Dawley rats were randomly divided into the sham operation group (S group), spinal cord injury group (SCI group), G-Rb1 treatment group (G-Rb1 group) and SCI+G-Rb1+Inhibitor L-name group (L-name group). The posterior limb function was evaluated via the Basso, Beattie and Bresnahan scoring method. The levels of superoxide dismutase (SOD), malondialdehyde (MDA), catalase (CAT) and glutathione (GSH) in serum were measured by ELISA. The pathological changes in the spinal cord were observed by H&E staining. Reverse transcription-quantitative polymerase chain reaction and western blot analyses were used to detect eNOS, phosphorylated (p)-eNOS, heat shock protein (HSP)90, Nrf2 and NAD(P)H quinone dehydrogenase 1 (Nqo1) at the mRNA and protein level. Immunohistochemistry was used to detect the expression of Nrf2 and p-eNOS. Compared with the S group, the scores of spinal cord function in the SCI group were significantly lower, and the levels of MDA were significantly increased, while the levels of SOD, CAT and GSH protein in spinal cord were significantly decreased (P<0.05). The spinal cord tissue exhibited hemorrhage, neuronal degeneration/necrosis, as well as mononuclear cell and lymphocyte infiltration. The eNOS, HSP90, Nrf2, Nqo1 and HO-1 mRNA levels were decreased (P<0.05). Compared with those in the SCI group, the spinal cord function score in the G-Rb1 group were significantly higher and the serum MDA content was significantly decreased, while the activity of SOD, CAT and GSH was significantly increased (P<0.05). The degeneration/necrosis of spinal cord neurons was attenuated, inflammatory cell infiltration was significantly reduced and the levels of eNOS, HSP90, Nrf2, Nqo1 and HO-1 were significantly upregulated (P<0.05). In the group that was administered the eNOS inhibitor L-name, the levels of eNOS, HSP90, Nrf2, Nqo1 and HO-1 were significantly decreased. In conclusion, G-Rb1 attenuates oxidative stress in injured spinal cords. The mechanism may at least in part involve the eNOS/Nrf2/HO-1 pathway.
The NIRS Brain AnalyzIR Toolbox

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low-levels of light (650–900 nm) to measure changes in cerebral blood volume and oxygenation. Over the last several decades, this technique has been utilized in a growing number of functional and resting-state brain studies. The lower operation cost, portability, and versatility of this method make it an alternative to methods such as functional magnetic resonance imaging for studies in pediatric and special populations and for studies without the confining limitations of a supine and motionless acquisition setup. However, the analysis of fNIRS data poses several challenges stemming from the unique physics of the technique, the unique statistical properties of data, and the growing diversity of non-traditional experimental designs being utilized in studies due to the flexibility of this technology. For these reasons, specific analysis methods for this technology must be developed. In this paper, we introduce the NIRS Brain AnalyzIR toolbox as an open-source Matlab-based analysis package for fNIRS data management, pre-processing, and first- and second-level (i.e., single subject and group-level) statistical analysis. Here, we describe the basic architectural format of this toolbox, which is based on the object-oriented programming paradigm. We also detail the algorithms for several of the major components of the toolbox including statistical analysis, probe registration, image reconstruction, and region-of-interest based statistics.
Parameter Tuning and Optimal Design of Decentralized Structured Controllers for Power Oscillation Damping in Electrical Networks

Power system stabilizers are controllers which damp power oscillations in electrical networks. They typically reside in the automation system of the power plant. Their design and structure are typically fixed in the design of the power plant. Optimal design and tuning of these decentralized controllers such that power oscillations are avoided is a challenging task. In the first part of the paper, we outline this problem and transform it into a so called structured controller synthesis problem where the control structure is fixed and optimal controller parameters need to be found. Based on this formulation, which preserves the real controller parameters, we propose a coordinate descent method to solve the controller design and tuning problem. To this end, we consider additional steady-state constraints in the system. We show the effectiveness of the proposed approach by detailed simulations of an established power system benchmark.
Clinical predictors of involuntary detention among voluntary inpatients in St Patrick’s University Hospital (SPUH)

Background Few studies have described clinical characteristics of patients subject to an involuntary detention in an Irish context. The Irish Mental Health Act 2001 makes provision under Section 23(1), whereby a person who has voluntary admission status can be detained. Aims This study aimed to describe all involuntary admissions to St Patrick’s University Hospital (SPUH) (2011–2013) and to evaluate clinical characteristics of voluntary patients who underwent Mental Health Act assessment during 2011 to determine differences in those who had involuntary admission orders completed and those who did not. Methods All uses of Mental Health Act 2001 within SPUH 2011–2013 were identified. All uses of Section 23(1) during 2011 were reviewed and relevant documents/case-notes examined using a pro forma covering clinical data, factors recognized to influence involuntary admissions and validated scales were used to determine diagnoses, insight, suicide and violence risk. Results Over 2011–2013, 2.5–3.8% of all admissions were involuntary with more detained after use of Section 23(1) than Section 14(2). The majority of initiations of Section 23(1) did not result in an involuntary admission (72%), occurred out of hours (52%) and many occurred early after admission (<1 week, 43%). Initiation of Section 23(1) by a consultant psychiatrist (p=0.001), suicide risk (p=0.03) and lack of patient insight into treatment (p=0.007) predicted conversion to involuntary admission. Conclusion This study predicts a role for patient insight, suicide risk and consultant psychiatrist decision making in the initiation of Mental Health Act assessment of voluntary patients. Further data describing the involuntary admissions process in an Irish setting are needed.
MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis

Recently, there is an increasing tendency to embed functionalities for recognizing emotions from user-generated media content in automated systems such as call-centre operations, recommendations, and assistive technologies, providing richer and more informative user and content profiles. However, to date, adding these functionalities was a tedious, costly, and time-consuming effort, requiring identification and integration of diverse tools with diverse interfaces as required by the use case at hand. The MixedEmotions Toolbox leverages the need for such functionalities by providing tools for text, audio, video, and linked data processing within an easily integrable plug-and-play platform. These functionalities include: 1) for text processing: emotion and sentiment recognition; 2) for audio processing: emotion, age, and gender recognition; 3) for video processing: face detection and tracking, emotion recognition, facial landmark localization, head pose estimation, face alignment, and body pose estimation; and 4) for linked data: knowledge graph integration. Moreover, the MixedEmotions Toolbox is open-source and free. In this paper, we present this toolbox in the context of the existing landscape, and provide a range of detailed benchmarks on standard test-beds showing its state-of-the-art performance. Furthermore, three real-world use cases show its effectiveness, namely, emotion-driven smart TV, call center monitoring, and brand reputation analysis.
LP relaxations and Fuglede's conjecture

Consider a unitary (up to scaling) submatrix of the Fourier matrix with rows indexed by <tex>$\mathcal{I}$</tex> and columns indexed by <tex>$\mathcal{J}$</tex>. From the column index set <tex>$\mathcal{J}$</tex> we construct a graph <tex>$\mathcal{G}$</tex> so that the row index set <tex>$\mathcal{I}$</tex> determines a max-clique. Interpreting <tex>$\mathcal{G}$</tex> as coming from an association scheme gives certain bounds on the clique number, which has possible applications to Fuglede's conjecture on spectral and tiling sets.
Interaction of Haloarchaeal Gas Vesicle Proteins Determined by Split-GFP

Several extremely halophilic archaea produce proteinaceous gas vesicles consisting of a gas-permeable protein wall constituted mainly by the gas vesicle proteins GvpA and GvpC. Eight additional accessory Gvp are involved in gas vesicle formation and might assist the assembly of this structure. Investigating interactions of halophilic proteins in vivo requires a method functioning at 2.5–5 M salt, and the split-GFP method was tested for this application. The two fragments NGFP and CGFP do not assemble a fluorescent GFP protein when produced in trans, but they assemble a fluorescent GFP when fused to interacting proteins. To adapt the method to high salt, we used the genes encoding two fragments of the salt-stable mGFP2 to construct four vector plasmids that allow an N- or C-terminal fusion to the two proteins of interest. To avoid a hindrance in the assembly of mGFP2, the fusion included a linker of 15 or 19 amino acids. The small gas vesicle accessory protein GvpM and its interaction partners GvpH, GvpJ, and GvpL were investigated by split-GFP. Eight different combinations were studied in each case, and fluorescent transformants indicative of an interaction were observed. We also determined that GvpF interacts with GvpM and uncovered the location of the interaction site of each of these proteins in GvpM. GvpL mainly interacted with the N-terminal 25-amino acid fragment of GvpM, whereas the other three proteins bound predominately to the C-terminal portion. Overall, the split-GFP method is suitable to investigate the interaction of two proteins in haloarchaeal cells. In future experiments, we will study the interactions of the remaining Gvps and determine whether some or all of these accessory Gvp proteins form (a) protein complex(es) during early stages of the assembly of the gas vesicle wall.
Simultaneous Parameters Identification and State Estimation based on Unknown Input Observer for a class of LPV Systems

A novel unknown input observer structure for parameters and state estimation is proposed to enhance the performance of the estimator. In this paper, we suggest how a failed matching condition in a nonlinear unknown input observer can be recovered by using time delayed measurement to solve the inversing problem. Based on delayed outputs, an augmented system is constructed from which the parameters of the model and the system states can be simultaneously estimated. The augmented nonlinear model is transformed into a Takagi Sugeno (TS) form. Sufficient conditions for the existence of the estimator are given in terms of linear matrix inequalities (LMIs). Using the obtained information on the unknown input observer, unknown parameters are identified. Finally, the feasibility and the effectiveness of the suggested approach is demonstrated on examples.
Approximate Fully Connected Neural Network Generation

Approximate computing is exploited in implementation of fully connected networks for classification problems. A multiplier structure whose area is scalable over accuracy through approximate computing is proposed. In order to employ the multipliers in a network, an area reduction algorithm is formed. It can adjust the approximation level of multipliers while still maintaining the target classification performance, without prior information on the value of network weights. Implementing on a Spartan6 FPGA, up to 79% area saving is recorded for various performance targets.
A Linear- and Linear-Matrix-Inequality-Constrained Extended Kalman Filter

This study proposes a method for state estimation of nonlinear systems that incorporates linear- and linear-matrix-inequalities as constraints on the state estimate. Rewriting the standard maximum likelihood objective function used to derive the Kalman filter allows the Kalman gain to be found by solving a constrained optimization problem with a linear objective function subject to a linear-matrix-inequality constraint. In this formulation, additional state-estimate constraints are incorporated into the optimization problem as linear- and linear-matrix-inequality constraints. This methodology is applied in the multiplicative extended Kalman filter (MEKF) framework for three-dimensional position and attitude estimation, and is validated on a simulation of a mobile robot translating and rotating in a constrained domain space. Results are presented and compared to those for the unconstrained MEKF.
Question Generation With Doubly Adversarial Nets

We study the problem of question generation on a specific domain, where there are no labeled data. To address this problem, we propose a novel neural question generation approach called DoubAN, or doubly adversarial nets, which fully utilizes labeled data from other domains (source domains) and unlabeled data from the target domain. Learning a DoubAN involves two adversarial procedures between a question generator and two adversaries. One adversary is a domain-classification discriminator (DC-Dis), which is designed to help the generator learn domain-general representations of the input text. The other is a question-answering discriminator (QA-Dis), which provides more training data with estimated reward scores for generated text-question pairs. We conduct experiments on the SQuAD dataset as target-domain unlabeled data and the NewsQA dataset as source-domain labeled data. Experiment results show that our DoubAN achieves better results than baselines. Compared to model variants, which adopt only DC-Dis or QA-Dis, we find that the DC-Dis and QA-Dis indirectly interact with each other and jointly improve the quality of generated questions on the target domain. Moreover, extensive analysis and discussion prove the reasonableness and effectiveness of our proposed approach.
Efficient Approximation for Restricted Biclique Cover Problems

Covering the edges of a bipartite graph by a minimum set of bipartite complete graphs (bicliques) is a basic graph theoretic problem, with numerous applications. In particular, it is used to characterize parsimonious models of a set of observations (each biclique corresponds to a factor or feature that relates the observations in the two sets of nodes connected by the biclique). The decision version of the minimum biclique cover problem is NP-Complete, and unless P=NP, the cover size cannot be approximated in general within less than a sub-linear factor of the number of nodes (or edges) in the graph. In this work, we consider two natural restrictions to the problem, motivated by practical applications. In the first case, we restrict the number of bicliques a node can belong to. We show that when this number is at least 5, the problem is still NP-hard. In contrast, we show that when nodes belong to no more than two bicliques, the problem has efficient approximations. The second model we consider corresponds to observing a set of independent samples from an unknown model, governed by a possibly large number of factors. The model is defined by a bipartite graph G=(L,R,E), where each node in L is assigned to an arbitrary subset of up to a constant f factors, while the nodes in R (the independent observations) are assigned to random subsets of the set of k factors where k can grow with size of the graph. We show that this practical version of the biclique cover problem is amenable to efficient approximations.
A Comet Assay for DNA Damage and Repair After Exposure to Carbon-Ion Beams or X-rays in Saccharomyces Cerevisiae

Ionizing radiation (IR) can result in serious genomic instability and genotoxicity by causing DNA damage. Carbon ion (CI) beams and X-rays are typical IRs and possess high-linear energy transfer (LET) and low-LET, respectively. In this article, a comet assay that was optimized by decreasing the electrophoresis time (8 minutes) and voltage (0.5 V/cm) was performed to elucidate and quantify the DNA damage induced by CI or X-rays radiation. Two quantitative methods for the comet assay, namely, comet score and olive tail moment, were compared, and the appropriate means and parameter values were selected for the present assay. The dose–effect relationship for CI or X-rays radiation and the DNA repair process were studied in yeast cells. These results showed that the quadratic function fitted the dose–effect relationship after CI or X-rays exposure, and the trend for the models fitted the dose–effect curves for various repair times was precisely described by the cubic function. A kinetics model was also creatively used to describe the process of DNA repair, and equations were calculated within repairable ranges that could be used to roughly evaluate the process and time necessary for DNA repair.
Partially-Robust Communications over a Noisy Channel

To study the fundamental limits on the joint transmission of data of different levels of sensitivity, we establish the deterministic-code capacity region of a network with one transmitter and two receivers: an “ordinary receiver” and a “robust receiver. ” The channel to the ordinary receiver is a given (known) discrete memoryless channel, whereas the channel to the robust receiver is an arbitrarily varying channel. Both receivers are required to decode the “common message” (the more sensitive data), whereas only the ordinary receiver is required to decode the “private message” (the less sensitive data).
How do libraries use social networking sites to interact with users

Social networking sites (SNS) are helpful for stirring up interactions among users. The number of libraries which adopt SNSs is increasing. However, user engagement is low on many libraries' SNSs. Existing research mainly focuses on the ways SNSs used in libraries and the librarians or users' attitudes towards libraries using SNSs. Little research has been done on how to use SNSs to interact with library users effectively. This study focuses on the interactions between libraries and users on libraries' Facebook, Twitter and Weibo. Four types of interactions are examined, including knowledge sharing, information dissemination, communication and knowledge gathering. A mixed method is applied in this study: quantitative results, generated from the analysis on around 1700 posts sampled from 40 libraries' SNSs, are incorporated with qualitative results concluded from the interviews with 10 librarians. The study finds that among the four types of interactions, knowledge sharing attracts the largest volume of user responses on libraries' SNSs. The study's investigation on the differences of Facebook-like and Twitter-like SNSs and those between academic and public libraries on using SNSs suggests that in order to improve the efficiency of interacting with users on SNSs, there are necessities for libraries to coordinate different types of SNSs and take the properties of their communities under consideration.
Tunnel-Based EAP Effective Security Attacks WPA2 Enterprise Evaluation and Proposed Amendments

Tunnel-Based Extensible Authentication Protocol has become fundamental for wireless Network access Control. It provides authentication, privacy and authorization for enterprise network access protected by WPA/WPA2 security framework. WPA2 is considered the latest and most secure standard for wireless communication especially for Wi-Fi networks. However, WPA/WPA2-PSK have been lately threatened by advanced versions of wireless attacks. In this paper, we study WPA/WPA2-Enterprise authentication with Tunnel-Based EAP common methods focusing on their strength and weak points and the impact of recent WPA/WPA2 attacks. We also propose protection techniques to mitigate discovered security flaws.
Preclinical Efficacy of the Novel Monocarboxylate Transporter 1 Inhibitor BAY-8002 and Associated Markers of Resistance

The lactate transporter SLC16A1/monocarboxylate transporter 1 (MCT1) plays a central role in tumor cell energy homeostasis. In a cell-based screen, we identified a novel class of MCT1 inhibitors, including BAY-8002, which potently suppress bidirectional lactate transport. We investigated the antiproliferative activity of BAY-8002 in a panel of 246 cancer cell lines and show that hematopoietic tumor cells, in particular diffuse large B-cell lymphoma cell lines, and subsets of solid tumor models are particularly sensitive to MCT1 inhibition. Associated markers of sensitivity were, among others, lack of MCT4 expression, low pleckstrin homology like domain family A member 2, and high pellino E3 ubiquitin protein ligase 1 expression. The antitumor effect of MCT1 inhibition was less pronounced on tumor xenografts, with tumor stasis being the maximal response. BAY-8002 significantly increased intratumor lactate levels and transiently modulated pyruvate levels. In order to address potential acquired resistance mechanisms to MCT1 inhibition, we generated MCT1 inhibitor–resistant cell lines and show that resistance can occur by upregulation of MCT4 even in the presence of sufficient oxygen, as well as by shifting energy generation toward oxidative phosphorylation. These findings provide insight into novel aspects of tumor response to MCT1 modulation and offer further rationale for patient selection in the clinical development of MCT1 inhibitors. Mol Cancer Ther; 17(11); 2285–96. ©2018 AACR.
Generalized Optimal Storage Scaling via Network Coding

It is critical to support efficient scaling in distributed storage systems so as to meet increasing storage demands with new storage nodes. However, the scaling process incurs substantial scaling bandwidth due to reorganizing currently stored data to new storage nodes. Recent work has applied network coding to minimize scaling bandwidth for a special case where <tex>$(n,\ k)$</tex> MDS codes are scaled to <tex>$(n^{\prime}, k^{\prime})$</tex> MDS codes for <tex>$n^{\prime}-k^{\prime}=n-k$</tex>. In this paper, we extend the results and prove the minimum scaling bandwidth for a more general setting where <tex>$(n,\ k)$</tex> MDS codes are scaled to <tex>$(n^{\prime}, k^{\prime})$</tex> MDS codes for <tex>$n^{\prime} > n$</tex> and <tex>$k^{\prime}\geq k$</tex>. Furthermore, we present a family of MDS code construction that achieves optimal scaling from <tex>$(n,\ k)$</tex> to <tex>$(n^{\prime}, k^{\prime})$</tex> where <tex>$k=k^{\prime}$</tex>.
Optimization of the $\mathcal{H}_{\infty}$-norm of Dynamic Flow Networks

In this paper, we study the $\mathcal{H}_{\infty}$-norm of linear systems over graphs, which is used to model distribution networks. In particular, we aim to minimize the $\mathcal{H}_{\infty}$-norm subject to allocation of the weights on the edges. The optimization problem is formulated with LMI (Linear-Matrix-Inequality) constraints. For distribution networks with one port, i.e., SISO systems, we show that the $\mathcal{H}_{\infty}$-norm coincides with the effective resistance between the nodes in the port. Moreover, we derive an upper bound of the $\mathcal{H}_{\infty}$-norm, which is in terms of the algebraic connectivity of the graph on which the distribution network is defined.