Miracle-dz/UniFAR-FTUs huggingface dataset:
Proton nuclear magnetic resonance spectroscopy unambiguously identifies different neural cell types

Proton nuclear magnetic resonance (1H NMR) spectroscopy is a noninvasive technique that can provide information on a wide range of metabolites. Marked abnormalities of 1H NMR brain spectra have been reported in patients with neurological disorders, but their neurochemical implications may be difficult to appreciate because NMR data are obtained from heterogeneous tissue regions composed of several cell populations. The purpose of this study was to examine the 1H NMR profile of major neural cell types. This information may be helpful in understanding the metabolic abnormalities detected by 1H NMR spectroscopy. Extracts of cultured cerebellar granule neurons, cortical astrocytes, oligodendrocyte-type 2 astrocyte (O–2A) progenitor cells, oligodendrocytes, and meningeal cells were analyzed. The purity of the cultured cells was > 95% with all the cell lineages, except for neurons (approximately 90%). Although several constituents (creatine, choline- containing compounds, lactate, acetate, succinate, alanine, glutamate) were ubiquitously detectable with 1H NMR, each cell type had distinctive qualitative and/or quantitative features. Our most unexpected finding was a large amount of N-acetyl-aspartate (NAA) in O– 2A progenitors. This compound, consistently detected by 1H NMR in vivo, was previously thought to ne present only in neurons. The finding that meningeal cells have an alanine:creatine ratio three to four times higher than astrocytes, neurons, or oligodendrocytes is in agreement with observations that meningiomas express a higher alanine:creatine ratio than gliomas. The data suggest that each individual cell type has a characteristic metabolic pattern that can be discriminated by 1H NMR, even by looking at only a few metabolites (e.g., NAA, glycine, beta- hydroxybutyrate). (ABSTRACT TRUNCATED AT 250 WORDS)
Supporting Confidentiality in UML: A Profile for the Decentralized Label Model

We present a way of incorporating a decentralized label model into the UML by defining a profile which is the built-in extension mechanism of the UML. Our profile permits specifying the confidentiality of data in UML by offering annotations for classes, attributes, operations, values of objects, and parameters of operations. Furthermore, the profile supports the creation of Jif (Java information flow) code. The Jif compiler guarantees that the specified confidentiality constraints are not violated. Our approach is appealing in the sense that it offers the possibility to consider confidentiality in UML and that the obtained code is guaranteed to behave correctly.
Monetary disorder and financial regimes - The demand for money in Argentina, 1900-2006

Argentina is a unique experience of protracted economic instability and monetary disorder. In the framework of a long-term view, we investigate the demand for narrow money in Argentina from 1900 to 2006, shedding some light on the existence of money demand equilibria in extremely turbulent economies. The paper examines the effect of monetary regime changes by dealing with the presence of structural breaks in long-run equations. We estimate and test for regime changes through a sequential approach and we embed breaks in long-run models. A robust cointegration analysis can be hence performed in a single-equation framework. We find that estimated parameters are in sharp contrast with those reported in the literature for Argentina, but in line with those reported for industrialized countries, while significant structural breaks appear consistent with major policy shocks that took place in Argentina during the 20th century.
Symbolic Dynamic Programming for First-order POMDPs

Partially-observable Markov decision processes (POMDPs) provide a powerful model for sequential decision-making problems with partially-observed state and are known to have (approximately) optimal dynamic programming solutions. Much work in recent years has focused on improving the efficiency of these dynamic programming algorithms by exploiting symmetries and factored or relational representations. In this work, we show that it is also possible to exploit the full expressive power of first-order quantification to achieve state, action, and observation abstraction in a dynamic programming solution to relationally specified POMDPs. Among the advantages of this approach are the ability to maintain compact value function representations, abstract over the space of potentially optimal actions, and automatically derive compact conditional policy trees that minimally partition relational observation spaces according to distinctions that have an impact on policy values. This is the first lifted relational POMDP solution that can optimally accommodate actions with a potentially infinite relational space of observation outcomes.
Real-time Activity Recognition by Discerning Qualitative Relationships Between Randomly Chosen Visual Features

In this paper, we present a novel method to explore semantically meaningful visual information and identify the discriminative spatiotemporal relationships between them for real-time activity recognition. Our approach infers human activities using continuous egocentric (first-person-view) videos of object manipulations in an industrial setup. In order to achieve this goal, we propose a random forest that unifies randomization, discriminative relationships mining and a Markov temporal structure. Discriminative relationships mining helps us to model relations that distinguish different activities, while randomization allows us to handle the large feature space and prevents over-fitting. The Markov temporal structure provides temporally consistent decisions during testing. The proposed random forest uses a discriminative Markov decision tree, where every nonterminal node is a discriminative classifier and the Markov structure is applied at leaf nodes. The proposed approach outperforms the state-of-the-art methods on a new challenging video dataset of assembling a pump system.
Encrypted integer division

When processing signals in the encrypted domain, homomorphic encryption can be used to enable linear operations on encrypted data. Integer division of encrypted data however requires an additional protocol with the server and will be relatively expensive. We present new solutions for dividing encrypted data, having low computational complexity. Two protocols for computing exact division, and two for approximating the division result.
Knee Osteoarthritis Diagnosis Using Support Vector Machine and Probabilistic Neural Network

Support Vector Machines (SVM) and Probabilistic Neural Networks (PNN) that are within the scope of artificial intelligence applications has been widely accepted. Because of strong statistical foundations of these methods, they generate generalizable results. In this study we aimed to compare classification performance of SVM and PNN methods using real and empirical data sets. In the real data set, we investigated the success of diagnosis of some demographic characteristics and some gene polymorphisms on knee osteoarthritis. In addition, success of SVM and PNN methods were compared for classification of samples that have various data structure. We found that the classification performance of PNN method is better than SVM. The most important disadvantage of PNN method is that the analysis time increase with sample size and number of attributes. When the proportion of the observation number to attributes number is greater than 5, SVM and PNN can be used for classification.
Comparing the Biological Impact of Glatiramer Acetate with the Biological Impact of a Generic

For decades, policies regarding generic medicines have sought to provide patients with economical access to safe and effective drugs, while encouraging the development of new therapies. This balance is becoming more challenging for physicians and regulators as biologics and non-biological complex drugs (NBCDs) such as glatiramer acetate demonstrate remarkable efficacy, because generics for these medicines are more difficult to assess. We sought to develop computational methods that use transcriptional profiles to compare branded medicines to generics, robustly characterizing differences in biological impact. We combined multiple computational methods to determine whether differentially expressed genes result from random variation, or point to consistent differences in biological impact of the generic compared to the branded medicine. We applied these methods to analyze gene expression data from mouse splenocytes exposed to either branded glatiramer acetate or a generic. The computational methods identified extensive evidence that branded glatiramer acetate has a more consistent biological impact across batches than the generic, and has a distinct impact on regulatory T cells and myeloid lineage cells. In summary, we developed a computational pipeline that integrates multiple methods to compare two medicines in an innovative way. This pipeline, and the specific findings distinguishing branded glatiramer acetate from a generic, can help physicians and regulators take appropriate steps to ensure safety and efficacy.
Measuring Voter-Controlled Privacy

In voting, the notion of receipt-freeness has been proposed to express that a voter cannot gain any information to prove that she has voted in a certain way. Receipt-freeness aims to prevent vote buying, even when a voter chooses to renounce her privacy. In this paper, we distinguish various ways that a voter can communicate with the intruder to reduce her privacy and classify them according to their ability to reduce the privacy of a voter. We develop a formal framework combining knowledge reasoning and trace equivalences to formally model voting protocols and define vote privacy for the voters. Our framework is quantitative, in the sense that it defines a measure for the privacy of a voter. Therefore, the framework can precisely measure the level of privacy for a voter for each of the identified privacy classes. The quantification allows our framework to capture receipts that reduce, but not nullify, the privacy of the voter. This has not been identified and dealt with by other formal approaches.
Apoptosis regulatory protein-protein interaction demonstrates hierarchical scale-free fractal network

Dysregulation or inhibition of apoptosis favors cancer and many other diseases. Understanding of the network interaction of the genes involved in apoptotic pathway, therefore, is essential, to look for targets of therapeutic intervention. Here we used the network theory methods, using experimentally validated 25 apoptosis regulatory proteins and identified important genes for apoptosis regulation, which demonstrated a hierarchical scale-free fractal protein-protein interaction network. TP53, BRCA1, UBIQ and CASP3 were recognized as a four key regulators. BRCA1 and UBIQ were also individually found to control highly clustered modules and play an important role in the stability of the overall network. The connection among the BRCA1, UBIQ and TP53 proteins was found to be important for regulation, which controlled their own respective communities and the overall network topology. The feedback loop regulation motif was identified among NPM1, BRCA1 and TP53, and these crucial motif topologies were also reflected in high frequency. The propagation of the perturbed signal from hubs was found to be active upto some distance, after which propagation started decreasing and TP53 was the most efficient signal propagator. From the functional enrichment analysis, most of the apoptosis regulatory genes associated with cardiovascular diseases and highly expressed in brain tissues were identified. Apart from TP53, BRCA1 was observed to regulate apoptosis by influencing motif, propagation of signals and module regulation, reflecting their biological significance. In future, biochemical investigation of the observed hub-interacting partners could provide further understanding about their role in the pathophysiology of cancer.
Weak Markovian Bisimilarity: Abstracting from Prioritized/Weighted Internal Immediate Actions

Markovian process calculi constitute a useful framework for reasoning about the functional and performance aspects of concurrent systems. This is achieved by means of behavioral equivalences that take into account both the action names and their exponentially distributed durations. A notable extension to the expressiveness of Markovian process calculi derives from the adoption of GSPNlike immediate actions, i.e. actions with a zero duration and equipped with a priority level and a weight. Since internal immediate actions are unobservable both from the functional viewpoint and from the performance viewpoint, in this paper we tackle the problem of defining a weak variant of Markovian bisimilarity that abstracts from such actions. We show that the proposed equivalence is a congruence and admits a sound and complete axiomatization for the class of well-prioritized process terms.
Study on the application of Autism Screening Scale(CHAT-23)

【Objective】 To evaluate the validity and feasibility of CHAT-23 as a screening instrument in Mainland China. 【Methods】 To screen the 18~24 months old children for ASDs with CHAT-23 in Luwan District.To calculate the sensitivity,specificity and positive predictive value(PPV) of CHAT-23 parental questionnaire,autistic children were recruited as the patient group,and normal children from were recruited as the control group.Make some parents of the children fill CHAT-23 parental questionnaires again and calculate the retest reliability(kappa value). 【Results】 2 children were found to be a high risk for ASDs among the 484 children.The sensitivity and specificity of CHAT-23 parental questionnaires were 0.941 and 0.884. The PPV for CHAT-23 parental questionnaires plus section B interview was 50%. The kappa value of CHAT-23 parental questionnaire was 0.50. 【Conclusions】 CHAT-23 has good sensitivity and specificity.It's an applicable Autism Screening Scale for primary health care institutions,and suitable to promote in Mainland China.
Purification, characterization and crystallization of the human 80S ribosome

Ribosomes are key macromolecular protein synthesis machineries in the cell. Human ribosomes have so far not been studied to atomic resolution because of their particularly complex structure as compared with other eukaryotic or prokaryotic ribosomes, and they are difficult to prepare to high homogeneity, which is a key requisite for high-resolution structural work. We established a purification protocol for human 80S ribosomes isolated from HeLa cells that allows obtaining large quantities of homogenous samples as characterized by biophysical methods using analytical ultracentrifugation and multiangle laser light scattering. Samples prepared under different conditions were characterized by direct single particle imaging using cryo electron microscopy, which helped optimizing the preparation protocol. From a small data set, a 3D reconstruction at subnanometric resolution was obtained showing all prominent structural features of the human ribosome, and revealing a salt concentration dependence of the presence of the exit site tRNA, which we show is critical for obtaining crystals. With these well-characterized samples first human 80S ribosome crystals were obtained from several crystallization conditions in capillaries and sitting drops, which diffract to 26 Å resolution at cryo temperatures and for which the crystallographic parameters were determined, paving the way for future high-resolution work.
FIRE PROTECTED CARBON FIBRE REINFORCED PLASTICS FOR STRUCTURAL AIRCRAFT COMPONENTS

The presented study deals with the optimization of fire properties of CFRP fuselage materials in a "pool-fire" scenario (a kerosene fire underneath an aircraft on ground) with the aim to increase the time until the structure fails. For this purpose hybrid material systems are being developed where the flame retardant is directly incorporated into the material. In this way, weight can be saved and the peeling effect, which is common for finishes, would be prevented. Due to the very high heat flux during a kerosene fire, powdered additives and layers are introduced into the composite, which form a thermal barrier or a barrier for gases in case of fire and protect the underlying structure from the effects of the flames. Of these materials, the fire behaviour with regard to smoke density and smoke toxicity is determined. The results show a significant reduction of smoke density for most of the tested materials and comply with the limits of toxic gases for cabin material systems. For a realistic relevelling of the “pool-fire” scenario, a test facility with a propane torch was build, which creates a heat flux of 180kW/m². In this test, the specimens are exposed to flames for 4min and the temperature profile at their backside is recorded with a thermocouple. In order to investigate the mechanical damage of the materials trough the fire, additional specimen were exposed to flame for 15s and the remaining compressive strength was determined. It was found that in particular a titanium foil under the top layer can contribute significantly to improvements in the mechanical properties as well as to slow down the temperature rise on the back of the material.
On the performance of cooperative diversity protocols in practical wireless systems

The concepts of cooperative diversity promise to offer the benefits of spatial diversity gains to handheld wireless devices with single antennas. The information-theoretic bounds that have been established recently serve as basic guidelines; yet, the performance of such protocols should additionally be examined for more realistic assumptions. Towards this end, we study cooperative diversity protocols for systems employing limited modulation alphabets and realistic receiver structures regarding the knowledge of channel state information. Our findings imply that under these conditions full second order diversity can only be achieved by using adaptive versions of cooperative protocols. As with other diversity schemes (e.g. space time block codes), our results for uncoded transmission can easily be combined with FEC techniques to obtain excellent error rate performance.
A study on a QoS/QoE correlation model for QoE evaluation on IPTV service

We develop a QoS/QoE correlation model for IPTV QoE evaluation. With the growth of QoE interest, IPTV providers need a method to control QoE when QoE is not satisfied by subscribers. Network providers can evaluate subscriber's QoE using QoS parameters weight and some analyzed values which is from the proposed model, and control QoE as deduce QoS parameters(delay, loss, jitter etc) associated with QoE. Through our proposed QoS/QoE correlation model, network providers can predict subscribers' QoE value in offered network environment and, on the contrary, constitute network environment which can meet an optimum QoE.
Exploration of barriers and facilitators to publishing local public health findings: A mixed methods protocol

Background Worldwide, the US accounts for a large proportion of journals related to public health. Although the American Public Health Association (APHA) includes 54 affiliated regional and state associations, little is known about their capacity to support public health scholarship. The aim of this study is to assess barriers and facilitators to operation of state journals for the dissemination of local public health research and practices. Methods A mixed methods approach will be used to complete the 12-month study. Affiliate websites will be accessed through the APHA membership portal to evaluate organizational infrastructure and ascertain the presence/absence of a journal. The leader of each affiliate will be contacted via email containing a link to a 12-question on-line survey to collect his/her perceptions of scholarly journals and the publication of local health data. To determine barriers and facilitators to publication of local public health findings, 30-minute semi-structured telephone interviews will focus on the infrastructure of the association, perceptions of the leader about the journal (if in place), and its operation. Anticipated Results We anticipate that 54 affiliate websites will be reviewed to complete the extraction checklist, that 74% of affiliate leaders will respond to the survey, and that 11 semi-structured interviews will be conducted. A limited number of state/regional public health associations will operate journals and a small percentage of those without journals may express an interest in implementing them. Barriers to operation of journals may include lack of resources (i.e., personnel, funding), and low prioritization of publication of state and local public health findings. Facilitators may include strong affiliate-academic relationships, affiliate leadership with experience in publications, and affiliate relationships with state and local departments of health. Conclusions The research proposed in this protocol may stimulate other state public health associations and other academic public health programs to follow suit; it would not be the first time that an observational research study served as an intervention.
A case study of the Hirsch index for 26 non‐prominent physicists

The h index was introduced by Hirsch to quantify an individual's scientific research output. It has been widely used in different fields to show the relevance of the research work of prominent scientists. I have worked out 26 practical cases of physicists which are not so prominent. Therefore this case study should be more relevant to discuss various features of the Hirsch index which are interesting or disturbing or both for the more average situation. In particular, I investigate quantitatively some pitfalls in the evaluation and the influence of self‐citations.
Unpacking Engagement: Convergence and Divergence in Transportation and Identification

This review proposes that transportation and identification are distinct forms of engaging with narratives, that they are enhanced by different factors and that they have distinct roles in narrative persuasion. By describing and analyzing 56 studies that explore the antecedents and consequences of transportation and identification, the ways in which these two processes are similar and different are highlighted. Following the review, new directions for research in this area are explicated. Finally, implications for both theory and message design are explored.
GRIN: Utilizing the Empty Half of Full Bisection Networks

Various full bisection designs have been proposed for datacenter networks. They are provisioned for the worst case in which every server wishes to send flat out and there is no congestion anywhere in the network. However, these topologies are prone to considerable under-utilization in the average case encountered in practice. To utilize spare bandwidth we propose GRIN, a simple, cheap and easily deployable solution that simply wires up any free ports datacenter servers may have. GRIN allows each server to use up to a maximum amount of bandwidth dependent on its available network ports and the number of idle uplinks in the same rack. This design can be used to augment almost any existing datacenter network, with little initial effort and no additional maintenance costs.
What value high level concepts in vision to language problems

Much of the recent progress in Vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image features to text. We propose here a method of incorporating high-level concepts into the very successful CNN-RNN approach, and show that it achieves a significant improvement on the state-of-the-art performance in both image captioning and visual question answering. We also show that the same mechanism can be used to introduce external semantic information and that doing so further improves performance. In doing so we provide an analysis of the value of high level semantic information in V2L problems.