Miracle-dz/UniFAR-FTUs huggingface dataset:
The Current Knowledge of the Role of PPAR in Hepatic Ischemia-Reperfusion Injury

Strategies to improve the viability of steatotic livers could reduce the risk of dysfunction after surgery and increase the number of organs suitable for transplantation. Peroxisome proliferator-activated receptors (PPARs) are major regulators of lipid metabolism and inflammation. In this paper, we review the PPAR signaling pathways and present some of their lesser-known functions in liver regeneration. Potential therapies based on PPAR regulation will be discussed. The data suggest that further investigations are required to elucidate whether PPAR could be a potential therapeutic target in liver surgery and to determine the most effective therapies that selectively regulate PPAR with minor side effects.
Biochemical characterization and crystal structure of a GH10 xylanase from termite gut bacteria reveal a novel structural feature and significance of its bacterial Ig‐like domain

Bacterial Ig‐like (Big) domains are commonly distributed in glycoside hydrolases (GH), but their structure and function remains undefined. Xylanase is a GH, and catalyzes the hydrolysis of the internal β‐xylosidic linkages of xylan. In this study, we report the molecular cloning, biochemical and biophysical characterization, and crystal structure of a termite gut bacterial xylanase, Xyl‐ORF19, which was derived from gut bacteria of a wood‐feeding termite (Globitermes brachycerastes). The protein architecture of Xyl‐ORF19 reveals that it has two domains, a C‐terminal GH10 catalytic domain and an N‐terminal Big_2 non‐catalytic domain. The catalytic domain folds in an (α/β)8 barrel as most GH10 xylanases do, but it has two extra β‐strands. The non‐catalytic domain is structurally similar to an immunoglobulin‐like domain of intimins. The recombinant enzyme without the non‐catalytic domain has fairly low catalytic activity, and is different from the full‐length enzyme in kinetic parameters, pH and temperature profiles, which suggests the non‐catalytic domain could affect the enzyme biochemical and biophysical properties as well as the role for enzyme localization. This study provides a molecular basis for future efforts in xylanase bioengineering. Biotechnol. Bioeng. 2013;110: 3093–3103. © 2013 Wiley Periodicals, Inc.
Fabrication of carbon nanofibrous microelectrode array (CNF-MEA) using nanofiber immersion photolithography

Microelectrode arrays (MEAs) are widely used for stimulating and receiving electrical signals between human and machines and for in vitro neural study. This work demonstrates the fabrication process of nanofibrous 3D microelectrodes using immersion lithography. Oil immersion negates the diffraction effects intrinsic in the photopatterning of electrospun nanofibers to give increased aspect ratio microarchitectures. Nanofiber electrode resistivity is characterized and its performance compared to that of carbon thin film. In vitro testing of electrodes are performed using E18 cortical neurons and analyzed for cell density and cell viability.
Nuclear graphite : structural characterisation and effects of irradiation

This thesis investigates graphites used in nuclear materials engineering applications – so called nuclear graphites. As well as outlining a microstructural model for nuclear graphite, it aims to develop a new understanding for nuclear graphite degradation under neutron irradiation and at high temperatures by the use of different characterization techniques and different irradiating species. Part of the thesis aims to develop a new methodology for understanding nuclear graphite and its damage processes via the correlative use of different characterisation methods and by novel data processing methods applied to characterisation data. This includes use of electron energy loss spectroscopy (EELS) acquired under damage free conditions and analysed using an improved automated fitting method in order to extract information on carbon bonding within the samples. Some of this methodology was developed using in situ electron irradiation of nuclear graphite. A range of different virgin (unirradiated) nuclear graphite grades were initially investigated and these were compared in terms of the crystallite coherence lengths obtained from X-ray diffraction (XRD) and by the relative proportions of non-graphitic carbon derived from Raman spectra. From both these results and from scanning and transmission electron microscopy (SEM and TEM) an initial model was developed for micro- and nano-structure of the complex composite graphitic material. Neutron irradiated nuclear graphites were then sourced and analysed using XRD, Raman and TEM/EELS and the effect of degradation was studied as variation of irradiation dose and temperature in terms of the change in crystallite size and carbon bonding. These results indicated a model involving the fragmentation of graphite crystallites and an increase in porosity during irradiation with the presence of some effects due to annealing of defects at higher temperatures. This is similar to a previous model developed for electron irradiation at doses lower than 1 dpa and temperatures lower than 370 °C.
Will they buy?

The proliferation of inexpensive video recording hardware and enormous storage capacity has enabled the collection of retail customer behavior at an unprecedented scale. The vast majority of this data is used for theft prevention and never used to better understand the customer. In what ways can this huge corpus be leveraged to improve the experience of customer and the performance of the store? This thesis presents MIMIC, a system that processes video captured in a retail store into predictions about customer proclivity to purchase. MIMIC relies on the observation that aggregate patterns of all of a store's patrons—the gestalt—captures behavior indicative of an imminent transaction. Video is distilled into a homogenous feature vector that captures the activity distribution by first tracking the locations of customers, then discretizing their movements into a feature vector using a collection of functional locations—areas of the store relevant to the tasks of patrons and employees. A time series of these feature vectors can then be classified as predictive-of-transaction using a Hidden Markov Model. MIMIC is evaluated on a small operational retail store located in the Mall of America near Minneapolis, Minnesota. Its performance is characterized across a wide cross-section of the model's parameters. Through manipulation of the training data supplied to MIMIC, the behavior of customers in the store can be examined at fine levels of detail without foregoing the potential afforded by big data. MIMIC enables a suite of valuable tools. For ethnographic researchers, it offers a technique for identifying key moments in hundreds or thousands of hours of raw video. Retail managers gain a fine-grained metric to evaluate the performance of their stores, and interior designers acquire a critical component in a store layout optimization framework.
Blind Deconvolution of Medical Ultrasound Images: A Parametric Inverse Filtering Approach

The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, this problem is addressed via proposing a blind deconvolution method which is innovative in several ways. In particular, the method is based on parametric inverse filtering, whose parameters are optimized using two-stage processing. At the first stage, some partial information on the point spread function is recovered. Subsequently, this information is used to explicitly constrain the spectral shape of the inverse filter. From this perspective, the proposed methodology can be viewed as a ldquohybridizationrdquo of two standard strategies in blind deconvolution, which are based on either concurrent or successive estimation of the point spread function and the image of interest. Moreover, evidence is provided that the ldquohybridrdquo approach can outperform the standard ones in a number of important practical cases. Additionally, the present study introduces a different approach to parameterizing the inverse filter. Specifically, we propose to model the inverse transfer function as a member of a principal shift-invariant subspace. It is shown that such a parameterization results in considerably more stable reconstructions as compared to standard parameterization methods. Finally, it is shown how the inverse filters designed in this way can be used to deconvolve the images in a nonblind manner so as to further improve their quality. The usefulness and practicability of all the introduced innovations are proven in a series of both in silico and in vivo experiments. Finally, it is shown that the proposed deconvolution algorithms are capable of improving the resolution of ultrasound images by factors of 2.24 or 6.52 (as judged by the autocorrelation criterion) depending on the type of regularization method used.
Non-parametric Filtering for Geometric Detail Extraction and Material Representation

Geometric detail is a universal phenomenon in real world objects. It is an important component in object modeling, but not accounted for in current intrinsic image works. In this work, we explore using a non-parametric method to separate geometric detail from intrinsic image components. We further decompose an image as albedo * (coarse-scale shading + shading detail). Our decomposition offers quantitative improvement in albedo recovery and material classification. Our method also enables interesting image editing activities, including bump removal, geometric detail smoothing/enhancement and material transfer.
An Evaluation Tool for Machine Translation: Fast Evaluation for MT Research

In this paper we present a tool for the evaluation of translation quality. First, the typical requirements of such a tool in the framework of machine translation (MT) research are discussed. We define evaluation criteria which are more adequate than pure edit distance and we describe how the measurement along these quality criteria is performed semi-automatically in a fast, convenient and above all consistent way using our tool and the corresponding graphical user interface.
Logic Design for Access Control , Security , Trust , and Assurance

Designs created by hardware and software engineers are often part of larger systems where confidentiality, integrity, and availability of information and other resources is a primary concern. Whether the system is a military one where assurance of mission-critical capabilities is paramount, or the system delivers financial services where assurances of integrity of financial data and transactions are crucial, the concerns are still the same. What are the access control and security policies and mechanisms that permit or deny the use of a resource or capability? Trust—who or what is believed and under what circumstances? Assurance—how do we know that what is proposed or implemented makes sense and is justifiable? This paper describes a logic and tools for access control, security, and trust. Over two hundred students from over 40 US universities have learned and applied this logic to reason about access control, security, and trust. As credible assurance requires certification and verification by independent evaluators, the logic and its applications are implemented as conservative extensions of the Higher-Order Logic (HOL) theorem prover. The HOL implementation is a means for rigorous and reusable descriptions and verifications of the access-control logic and its application.
Bmc Medical Informatics and Decision Making Substantial Reduction of Inappropriate Tablet Splitting with Computerised Decision Support: a Prospective Intervention Study Assessing Potential Benefit and Harm

Background: Currently ambulatory patients break one in four tablets before ingestion. Roughly 10% of them are not suitable for splitting because they lack score lines or because enteric or modified release coating is destroyed impairing safety and effectiveness of the medication. We assessed impact and safety of computerised decision support on the inappropriate prescription of split tablets.
Abnormal Levels of Gadd45alpha in Developing Neocortex Impair Neurite Outgrowth

To better understand the short and long-term effects of stress on the developing cerebral cortex, it is necessary to understand how early stress response genes protect or permanently alter cells. One family of highly conserved, stress response genes is the growth arrest and DNA damage-45 (Gadd45) genes. The expression of these genes is induced by a host of genotoxic, drug, and environmental stressors. Here we examined the impact of altering the expression of Gadd45alpha (Gadd45a), a member of the Gadd45 protein family that is expressed throughout the developing cortices of mice and humans. To manipulate levels of Gadd45a protein in developing mouse cortex, we electroporated cDNA plasmids encoding either Gadd45a or Gadd45a shRNA to either overexpress or knockdown Gadd45a levels in the developing cortices of mice, respectively. The effects of these manipulations were assessed by examining the fates and morphologies of the labeled neurons. Gadd45a overexpression both in vitro and in vivo significantly impaired the morphology of neurons, decreasing neurite complexity, inducing soma hypertrophy and increasing cell death. Knockdown of Gadd45a partially inhibited neuronal migration and reduced neurite complexity, an effect that was reversed in the presence of an shRNA-resistant Gadd45a. Finally, we found that shRNA against MEKK4, a direct target of Gadd45a, also stunted neurite outgrowth. Our findings suggest that the expression of Gadd45a in normal, developing brain is tightly regulated and that treatments or environmental stimuli that alter its expression could produce significant changes in neuronal circuitry development.
Understanding learning at a glance: an overview of learning dashboard studies

Research on learning dashboards aims to identify what data is meaningful to different stakeholders in education, and how data can be presented to support sense-making processes. This paper summarizes the main outcomes of a systematic literature review on learning dashboards, in the fields of Learning Analytics and Educational Data Mining. The query was run in five main academic databases and enriched with papers coming from GScholar, resulting in 346 papers out of which 55 were included in the final analysis. Our review distinguishes different kinds of research studies as well as different aspects of learning dashboards and their maturity in terms of evaluation. As the research field is still relatively young, many of the studies are exploratory and proof-of-concept. Among the main open issues and future lines of work in the area of learning dashboards, we identify the need for longitudinal research in authentic settings, as well as studies that systematically compare different dashboard design options.
Dirichlet-Based Histogram Feature Transform for Image Classification

Histogram-based features have significantly contributed to recent development of image classifications, such as by SIFT local descriptors. In this paper, we propose a method to efficiently transform those histogram features for improving the classification performance. The (L1-normalized) histogram feature is regarded as a probability mass function, which is modeled by Dirichlet distribution. Based on the probabilistic modeling, we induce the Dirichlet Fisher kernel for transforming the histogram feature vector. The method works on the individual histogram feature to enhance the discriminative power at a low computational cost. On the other hand, in the bag-of-feature (BoF) frame- work, the Dirichlet mixture model can be extended to Gaussian mixture by transforming histogram-based local descriptors, e.g., SIFT, and thereby we propose the method of Dirichlet-derived GMM Fisher kernel. In the experiments on diverse image classification tasks including recognition of subordinate objects and material textures, the pro- posed methods improve the performance of the histogram- based features and BoF-based Fisher kernel, being favor- ably competitive with the state-of-the-arts.
Use of machine learning in big data analytics for insider threat detection

In current enterprise environments, information is becoming more readily accessible across a wide range of interconnected systems. However, trustworthiness of documents and actors is not explicitly measured, leaving actors unaware of how latest security events may have impacted the trustworthiness of the information being used and the actors involved. This leads to situations where information producers give documents to consumers they should not trust and consumers use information from non-reputable documents or producers. The concepts and technologies developed as part of the Behavior-Based Access Control (BBAC) effort strive to overcome these limitations by means of performing accurate calculations of trustworthiness of actors, e.g., behavior and usage patterns, as well as documents, e.g., provenance and workflow data dependencies. BBAC analyses a wide range of observables for mal-behavior, including network connections, HTTP requests, English text exchanges through emails or chat messages, and edit sequences to documents. The current prototype service strategically combines big data batch processing to train classifiers and real-time stream processing to classifier observed behaviors at multiple layers. To scale up to enterprise regimes, BBAC combines clustering analysis with statistical classification in a way that maintains an adjustable number of classifiers.
Comparative Study of Conversion of Coral with Ammonium Dihydrogen Phosphate and Orthophosphoric Acid to Produce Calcium Phosphates

Biogenic materials like corals, which are readily available, could be used to produce bioceramic materials and address significant advantages due to their unique structures and chemical compositions that contain Mg and Sr. Many conversion processes has been in the past proposed. In this work, a comparison study between the conversion of coral with orthophosphoric acid and ammonium dihydrogen phosphate was conducted. The resultant structures and compositions were studied using XRD, ICP-MS, SEM and FTIR. The results show that with phosphoric acid the coral was converted into mainly monetite (92%). The ammonium dihydrogen phosphate converted approximately 76% of the coral to hydroxyapatite through solid state reactions. The two routes proved to be effective in producing bioceramic materials from corals under moderate conditions of temperature with a basic condition favouring the yield of hydroxyapatite.
Niche specialization of reef-building corals in the mesophotic zone: metabolic trade-offs between divergent Symbiodinium types

The photobiology of two reef corals and the distribution of associated symbiont types were investigated over a depth gradient of 0–60 m at Scott Reef, Western Australia. Pachyseris speciosa hosted mainly the same Symbiodinium C type similar to C3 irrespective of sampling depth. By contrast, Seriatopora hystrix hosted predominantly Symbiodinium type D1a or D1a-like at shallow depths while those in deeper water were dominated by a Symbiodinium C type closely related to C1. The photosynthesis/respiration (P/R) ratio increased consistently with depth at the two sampling times (November 2008 and April 2009) for P. speciosa and in November 2008 only for S. hystrix, suggesting a reduction in metabolic energy expended for every unit of energy obtained from photosynthesis. However, in April 2009, shallow colonies of S. hystrix exhibited decreased P/R ratios down to depths of approximately 23 m, below which the ratio increased towards the maximum depth sampled. This pattern was mirrored by changes in tissue biomass determined as total protein content. The depth of change in the direction of the P/R ratio correlated with a shift from Symbiodinium D to C-dominated colonies. We conclude that while photobiological flexibility is vital for persistence in contrasting light regimes, a shift in Symbiodinium type may also confer a functional advantage albeit at a metabolic cost with increased depth.
On efficient extraction of partially specified test sets for synchronous sequential circuits

Testing systems-on-a-chip (SOC) involves applying huge amounts of test data, which is stored in the tester memory and then transferred to the circuit under test (CUT) during test application. Therefore, practical techniques, such as test compression and compaction, are required to reduce the amount of test data in order to reduce both the total testing time and the memory requirements for the tester. Relaxing test sequences, i.e. extracting partially specified test sequences, can improve the efficiency of both test compression and test compaction. In this paper, we propose an efficient test relaxation technique for synchronous sequential circuits that maximizes the number of unspecified bits while maintaining the same fault coverage as the original test set.
Caveolin modulates integrin function and mechanical activation in the cardiomyocyte

β1 integrins (β1) transduce mechanical signals in many cells, including cardiac myocytes (CM). Given their close localization, as well as their role in mechanotransduction and signaling, we hypothesized that caveolin (Cav) proteins might regulate integrins in the CM. β1 localization, complex formation, activation state, and signaling were analyzed using wild‐type, Cav3 knockout, and Cav3 CM‐specific transgenic heart and myocyte samples. Studies were performed under basal and mechanically loaded conditions. We found that: 1) β1 and Cav3 colocalize in CM and coimmunoprecipitate from CM protein lysates; 2) β1 is detected in a subset of caveolae; 3) loss of Cav3 caused reduction of β1D integrin isoform and active β1 integrin from the buoyant domains in the heart; 4) increased expression of myocyte Cav3 correlates with increased active β1 integrin in adult CM; 5) in vivo pressure overload of the wild‐type heart results in increased activated integrin in buoyant membrane domains along with increased association between active integrin and Cav3; and 6) Cav3‐deficient myocytes have perturbed basal and stretch mediated signaling responses. Thus, Cav3 protein can modify integrin function and mechanotransduction in the CM and intact heart. —Israeli‐Rosenberg, S. , Chen, C. , Li, R. , Deussen, D. N. , Niesman, I. R. , Okada, H. , Patel, H. H. , Roth, D. M. , Ross, R. S. Caveolin modulates integrin function and mechanical activation in the cardiomyocyte. FASEB J. 29, 374‐384 (2015). www.fasebj. org
Improving Security in Multi-Agent Systems by Using a Novel Reputation Model

Multi-agent systems like Peer-to-Peer networks are widely used for content sharing and searching. However, privacy and security are the concerns with such networks as they are vulnerable to attacks due to open, dynamic and anonymous nature. To protect communications over such networks many models came into existence that are based on the reputation or trust of the peers that interact with each other. However, they can not cope with the unpredictable behavior of malicious peers. To overcome this problem and improve QoS (Quality of Service) Das and Islam presented a dynamic trust computation model. In this paper we implement that trust model and build a prototype of multi-agent system to show the proof of concept. The experimental results revealed that the application facilitates dynamic trust computation and achieves secure communication and quality of service by balancing load among the agents.