Miracle-dz/UniFAR-FTUs huggingface dataset:
The fine structure of the reconstructed neural retina of chick embryos.

The fine structure of reconstructed neural retina formed from dissociated neural retinal cells of 6½-day-old chick embryos on the chorio-allantoic membrane of chick embryos was examined with the electron microscope. Three nuclear layers (ganglion cell layer, inner and outer nuclear layers) and two fibrous layers (inner and outer plexiform layers) are found within the reconstructed retina. Both the outer and the inner limiting membranes of the reconstructed structure are constituted from the processes of differentiated Muller cells. The ganglion cell layer consists of two types of cell, though a typical ganglion cell with axonal process is not observed. Optic nerve fibres are not formed. Amacrine cells are recognized within the inner nuclear layer. Differentiation of the inner segment of the photoreceptor cell occurs, but not of the outer segment. Synaptic structures are recognized in the inner plexiform layer, but not in the outer plexiform layer.
A simple and quick method for rendering underwater optical effect

Underwater optical effect simulation is an important part of simulating natural scene. However, computing these effects is difficult and time-consuming, since light refracts when passing through waves. To address the problem, this paper proposes a simple and quick method for rendering underwater scenes, including the shafts of light refracted by the dynamic water surface, caustics caused by the effects of light and shadows beneath the creatures in the water. The method simplifies the Iwasaki's method to save the computation-time by an eye-irrelative method calculating the intensities of the illumination volumes with parts of intersection points of Iwasaki's. As the experiment results show that our method got a high frame rate with an acceptable quality for real-time applications.
Mitochondria Isolated from Rat Brown Adipose Tissue and Liver

Mitochondrial fractions, relatively free from contamination by other cytoplasmic structures, have been isolated by differential centrifugation from homogenates of brown adipose tissue from starved rats. It was possible in such fractions to distinguish two types of mitochondria in this tissue. Type I mitochondria, when morphologically intact, are limited by a bilaminar membrane and show regular parallel cristae. In isolated fractions, a proportion of these mitochondria are swollen, vacuolation occurring within the cristae between their limiting membranes. Type II mitochondria are distinguished from the more numerous type I bodies by the opaque appearance of their matrix. They are limited by a membrane which is in part single, and in part double. They show a few, but crisply outlined internal membranes. Vacuolation of this type of mitochondrion has not been observed. Vacuolation comparable to that in brown fat mitochondria was also observed between the two laminae of the enclosing membrane and within the cristae of liver mitochondria.
The effect of context processing on different aspects of social cognition in schizophrenia.

BACKGROUND It is well known that individuals with schizophrenia have impaired social cognition. The construct of social cognition involves several components, including perception, interpretation, and the ability to integrate context (Adolphs R. The neurobiology of social cognition. Curr Opin Neurobiol. 2001;11:231-239; Brothers L. The social brain: a project for integrating primate behavior and neurophysiology in a new domain. Concepts Neurosci. 1990;1:27-61). Importantly, a number of studies have suggested that deficits in context processing underlie cognitive dysfunction in schizophrenia (Penn DL, Corrigan PW, Bentall RP, Racenstein JM, Newman L. Social cognition in schizophrenia. Psychol Bull. 1997;121(1):114-132; Green MF, Nuechterlein KH. Should schizophrenia be treated as a neurocognitive disorder? Schizophr Bull. 1999;25:309-319). Thus, the purpose of the current study was to investigate the relationship between context processing and different aspects of social cognition in schizophrenia. METHOD Individuals with schizophrenia (n = 41) and the healthy controls (n = 32) participated in this study. The participants completed 2 sections of The Awareness of Social Inference Test: (1) social inference minimal (SI-M) and (2) social inference enriched (SI-E). They also completed face and voice emotion discrimination tasks. In addition, we used the AX-Continuous Performance Test (AX-CPT) to measure context processing and the n-back task to measure working memory more generally. RESULTS AX-CPT performance in schizophrenia was positively correlated with both SI-M and SI-E performance but not with either the face or the voice discrimination. Furthermore, the correlation between AX-CPT performance and SI-M/SI-E performance was significantly stronger in individuals with schizophrenia than in controls. CONCLUSION These results suggest that impairments in context processing are related to inferential components of social cognition in schizophrenia but not to the ability to recognition facial or vocal emotion. As such, deficits in context processing may contribute to deficits in both "hot" and "cold" aspects of cognition in schizophrenia.
Molecular imaging of macrophage protease activity in cardiovascular inflammation in vivo

Summary Macrophages contribute pivotally to cardiovascular diseases (CVD), notably to atherosclerosis. Imaging of macrophages in vivo could furnish new tools to advance evaluation of disease and therapies. Proteolytic enzymes serve as key effectors of many macrophage contributions to CVD. Therefore, intravital imaging of protease activity could aid evaluation of the progress and outcome of atherosclerosis, aortic aneurysm formation, or rejection of cardiac allografts. Among the large families of proteases, matrix metalloproteinases (MMPs) and cysteinyl cathepsins have garnered the most interest because of their participation in extra-cellular matrix remodelling. These considerations have spurred the development of dedicated imaging agents for protease activity detection. Activatable fluorescent probes, radiolabelled inhibitors, and nanoparticles are currently under exploration for this purpose. While some agents and technologies may soon see clinical use, others will require further refinement. Imaging of macrophages and protease activity should provide an important adjunct to understanding pathophysiology in vivo, evaluating the effects of interventions, and ultimately aiding clinical care.
Mining Target-Oriented Sequential Patterns with Time-Intervals

A target-oriented sequential pattern is a sequential pattern with a concerned itemset in the end of pattern. A time-interval sequential pattern is a sequential pattern with time-intervals between every pair of successive itemsets. In this paper we present an algorithm to discover target-oriented sequential pattern with time-intervals. To this end, the original sequences are reversed so that the last itemsets can be arranged in front of the sequences. The contrasts between reversed sequences and the concerned itemset are then used to exclude the irrelevant sequences. Clustering analysis is used with typical sequential pattern mining algorithm to extract the sequential patterns with time-intervals between successive itemsets. Finally, the discovered time-interval sequential patterns are reversed again to the original order for searching the target patterns.
Architectural Models to Simplify Administration of Service-Oriented Applications

Pervasive applications are often executed in fluctuating conditions and need frequent adaptations to meet requirements. Autonomic computing techniques are frequently used to automate adaptations to changing execution conditions. However, some administration tasks still have to be performed by human administrators. Such tasks are very complex because of a lack of understanding of the system evolutions. In this paper, we propose to build and link models at runtime of supervised applications in order to simplify the administrators' job. Our approach is illustrated on a health application called actimetrics, developed with the Orange Labs.
Mechanical implementation of postural synergies using a simple continuum mechanism

It is known that human controls muscles for hand poses in a coordinated manner and the coordination is referred to as a postural synergy. Using postural synergies, dexterous grasping tasks could be accomplished on a prosthetic hand via only a few (usually two) control inputs. Instead of implementing postural synergies digitally, this paper presents the design of a simple continuum mechanism for implementing the postural synergies mechanically. The design, fabrication and assembly of a prosthetic hand are firstly presented, followed by the synthesis of postural synergies from various grasping poses. Referring to the extracted postural synergies, structural parameters of the continuum mechanism are calculated based on a kinematics model. Experimental verifications are also presented to demonstrate the efficacy of the proposed idea.
Predicting Intonational Phrasing From Text

Determining the relationship between the intonational characteristics of an utterance and other features inferable from its text is important both for speech recognition and for speech synthesis. This work investigates the use of text analysis in predicting the location of intonational phrase boundaries in natural speech, through analyzing 298 utterances from the DARPA Air Travel Information Service database. For statistical modeling, we employ Classification and Regression Tree (CART) techniques. We achieve success rates of just over 90%, representing a major improvement over other attempts at boundary prediction from unrestricted text.
Hybrid Planning for Partially Hierarchical Domains

Hierarchical task network and action-based planning approaches have traditionally been studied separately. In many domains, human expertise in the form of hierarchical reduction schemas exists, but is incomplete. In such domains, hybrid approaches that use both HTN and action-based planning techniques are needed. In this paper, we extend our previous work on refinement planning to include hierarchical planning. Specifically, we provide a generalized plan-space refinement that is capable of handling non-primitive actions. The generalization provides a principled way of handling partially hierarchical domains, while preserving systematicity, and respecting the user-intent inherent in the reduction schemas. Our general account also puts into perspective the many surface differences between the HTN and action-based planners, and could support the transfer of progress between HTN and action-based planning approaches.
Data Layout Transformation for Structured-Grid Codes on GPU

We present data layout transformation as an effective performance optimization for memory-bound structuredgrid applications for GPUs. Structured grid applications are a class of applications that compute grid cell values on a regu lar 2D, 3D or higher dimensional regular grid. Each output point is computed as a function of itself and its nearest neighbors . Stencil code is an instance of this application class. Examp les of structured grid applications include fluid dynamics and heat distribution that solve partial differential equations wi th an iterative solver on a dense multidimensional array. Using the information available through variable-length array syntax, standardized in C99 and other modern languages, we have enabled automatic data layout transformations for str uctured grid codes with dynamic array sizes. We first present a formulation that enables automatic data layout transformations for structured grid code in CUDA. We then model the DRAM banking and interleaving scheme of the GTX280 GPU through microbenchmarking. We developed a layout transformation methodology that guides layout transformations to statically choose a good layout given a model of the memory system. The transformation which distributes concurrent memory requests evenly to DRAM channels and banks provides substanti al speedup for structured grid application by improving their memory-level parallelism.
Complexity analysis of EEG motor movement with eye open and close subjects using multiscale permutation entropy (MPE) technique

Background: Biological systems exhibit non-linear and spatiotemporal dynamics and structures even at rest. Humans demonstrate a remarkable ability to generate accurate and appropriate motor behaviour under many different and often uncertain environmental conditions. There are many motor movement tasks like eye open and close conditions, hand movements, fist movement etc. Brain controls all motor movement tasks. Electroencephalography (EEG) is a technique used to quantify the dynamics of physiological systems using non-invasive physiological monitoring and clinical investigation. The mental simulation of motor related tasks such as opening and closing of eye, left and right fist and fingers and other motor executive brain regions are commonly cognitive nature of tasks requires analysis using EEG motor movements. Methods: To quantify and understand the dynamics of EEG motor movements tasks, we employed robust Multiscale Permutation Entropy (MPE) analysis technique to distinguish Eye Open (EO) and Eye-Closed (EC) conditions. Mann-Whitney rank test was used to find significant differences between the groups and result were considered statistically significant for p-values<0.05. The Receive Operator Curve (ROC) was also computed to find the degree of separation between the groups. Results: The finding reveals that that frontal electrodes (F2, F3, F4, F5, F6, F7, F8) and front polar electrodes gives the highest separations and significant results to distinguish the EEG Motor movements tasks between eye open and eye closed tasks. The parietal (P3, P4), occipital (O1, O2) and central (C3, C4) electrodes gives only significant results at various temporal scales. The extremely significant results were obtained at F5, Fp1 followed by F1, F4, Fp2, F6 and F7. It was also found that frontal electrodes give the highest significance results followed by parietal, occipital and central electrodes which imply that these regions accordingly will help to distinguish these conditions from EEG motor movement tasks. MPE give higher significance results and separation at all selected electrodes than MSE to discriminate the brain states during EC and EO during the motor movement/imaginary tasks.
Alternative Investments: The Case of Wine

For repeat transactions data from monthly auction hammer prices, we analyze the level and quality of Bordeaux wine returns using the Fama-French Three-Factor Model and the Capital Asset Pricing Model. Returns average up to 0.75% per month above those predicted by these models. Further, investment grade wines benefit from low exposure to market risk factors, thus offering a valuable dimension of portfolio diversification. These findings are consistent with simple theoretical considerations and support a documented growing interest in wine investments.
Cognitive techniques for Control of Dynamic Object Behavior in Group

This paper describes a possibility of applying the data mining technique to train agents for teamwork. Data mining modules based on immunological networks capable to be learned on-lin e in dynamically changing environments are proposed. These modules provide ad aptive agent behavior for teamwork, because they are able to self-tuning in n on-deterministic environments. Reinforcement learning is considered t o be the main method for training the agents during a game/ Examples of immu nological networks for agent teamwork implementation are considered and re sults of experiments are described.
Epidemic Levels of Drug Resistant Tuberculosis (MDR and XDR-TB) in a High HIV Prevalence Setting in Khayelitsha, South Africa

Background Although multidrug-resistant tuberculosis (MDR-TB) is emerging as a significant threat to tuberculosis control in high HIV prevalence countries such as South Africa, limited data is available on the burden of drug resistant tuberculosis and any association with HIV in such settings. We conducted a community-based representative survey to assess the MDR-TB burden in Khayelitsha, an urban township in South Africa with high HIV and TB prevalence. Methodology/Principal Findings A cross-sectional survey was conducted among adult clinic attendees suspected for pulmonary tuberculosis in two large primary care clinics, together constituting 50% of the tuberculosis burden in Khayelitsha. Drug susceptibility testing (DST) for isoniazid and rifampicin was conducted using a line probe assay on positive sputum cultures, and with culture-based DST for first and second-line drugs. Between May and November 2008, culture positive pulmonary tuberculosis was diagnosed in 271 new and 264 previously treated tuberculosis suspects (sample enriched with previously treated cases). Among those with known HIV status, 55% and 71% were HIV infected respectively. MDR-TB was diagnosed in 3.3% and 7.7% of new and previously treated cases. These figures equate to an estimated case notification rate for MDR-TB of 51/100,000/year, with new cases constituting 55% of the estimated MDR-TB burden. HIV infection was not significantly associated with rifampicin resistance in multivariate analyses. Conclusions/Significance There is an extremely high burden of MDR-TB in this setting, most likely representing ongoing transmission. These data highlight the need to diagnose drug resistance among all TB cases, and for innovative models of case detection and treatment for MDR-TB, in order to interrupt transmission and control this emerging epidemic.
Achilles Tendinosis

Background Understanding biochemical and structural changes of the extracellular matrix in Achilles tendinosis might be important for developing mechanism-based therapies. Hypothesis In Achilles tendinosis, changes occur in biochemical composition and collagen turnover rate. Study Design Descriptive laboratory study. Methods From 10 patients undergoing surgery for Achilles tendinopathy, 1 tendinosis biopsy specimen and 1 biopsy specimen of macroscopically healthy tendon tissue adjacent to the lesion were collected. Furthermore, biopsy samples were collected from 3 donors with asymptomatic Achilles tendons. Water content, collagen content, percentage of denatured collagen, amount of lysine hydroxylation, number of enzymatic and nonenzymatic crosslinks, matrix metalloproteinase activity, and matrix metalloproteinase and collagen gene-expression levels were analyzed. Results In tendinotic lesions, the water content was highest, and collagen content was subnormal with higher amounts of denatured/damaged collagen. Low pentosidine levels in tendinotic tissue indicated the presence of relatively young collagenous matrix. More hydroxylated lysine residues were present in tendinotic samples, but enzymatic crosslinks revealed no differences between tendinotic, adjacent, and healthy samples. In tendinotic specimens, matrix metalloproteinase activity was higher, matrix metalloproteinase gene-expression profile was altered, and collagen type I and III gene expression were upregulated. Conclusion In Achilles tendinosis, the collagen turnover rate is increased, and the natural biochemical composition of the collagenous matrix is compromised. Clinical Relevance Although tendon tissue directly adjacent to an Achilles tendinosis lesion looks macroscopically healthy, histological and biochemical degenerative changes in adjacent tissue are evident, which may have implications for surgical interventions.
Electroluminescence-detected magnetic-resonance study of polyparaphenylenevinylene (PPV)-based light-emitting diodes.

The strong electroluminescence (EL)-detected magnetic resonance of PPV-based light emitting diodes is compared to the conductivity ([sigma])- and photoluminescence (PL)-detected resonances. It provides direct evidence that polaron-to-singlet exciton conversion is responsible for the EL. In contrast to the narrow PL-enhancing resonance assigned to polaron recombination, strong EL- and [sigma]-quenching resonances are attributed to the spin-dependent polaron-to-bipolaron decay. The half-field EL- and [sigma]-detected resonances of two distinct triplet excitons is believed to result from triplet-triplet fusion to singlets.
SGuard: A lightweight SDN safe-guard architecture for DoS attacks

Software Defined Networking (SDN) is a revolutionary networking paradigm towards the future network, experiencing rapid development nowadays. However, its main characteristic, the separation of control plane and data plane, also brings about new security challenges, i.e., Denial-of-Service (DoS) attacks specific to OpenFlow SDN networks to exhaust the control plane bandwidth and overload the buffer memory of OpenFlow switch. To mitigate the DoS attacks in the OpenFlow networks, we design and implement SGuard, a security application on top of the NOX controller that mainly contains two modules: Access control module and Classification module. We employ novel six-tuple as feature vector to classify traffic flows, meanwhile optimizing classification by feature ranking and selecting algorithms. All the modules will cooperate with each other to complete a series of tasks such as authorization, classification and so on. At the end of this paper, we experimentally use Mininet to evaluate SGuard in a software environment. The results show that SGuard works efficiently and accurately without adding more overhead to the SDN networks.
Artificial Prediction Markets for Online Prediction

In this dissertation, we propose an online learning technique to predict a value of a continuous variable by (i) integrating a set of data streams from heterogeneous sources with time varying compositions including (a) changing the quality of data streams, (b) addition or deletion of data streams (ii) integrating the results of several analysis algorithms for each data source when the most suitable algorithm for a given data source is not known a priori (iii) dynamically weighting the prediction of each analysis algorithm and data source on the system prediction based on their varying quality.
Classification in biological networks with hypergraphlet kernels

Biological and cellular systems are often modeled as graphs in which vertices represent objects of interest (genes, proteins, drugs) and edges represent relational ties between these objects (binds-to, interacts-with, regulates). This approach has been highly successful owing to the theory, methodology and software that support analysis and learning on graphs. Graphs, however, suffer from information loss when modeling physical systems due to their inability to accurately represent multi-object relationships. Hypergraphs, a generalization of graphs, provide a framework to mitigate information loss and unify disparate graph-based methodologies. We present a hypergraph-based approach for modeling biological systems and formulate vertex classification, edge classification and link prediction problems on (hyper)graphs as instances of vertex classification on (extended, dual) hypergraphs. We then introduce a novel kernel method on vertex- and edge-labeled (colored) hypergraphs for analysis and learning. The method is based on exact and inexact (via hypergraph edit distances) enumeration of hypergraphlets; i.e., small hypergraphs rooted at a vertex of interest. We empirically evaluate this method on fifteen biological networks and show its potential use in a positive-unlabeled setting to estimate the interactome sizes in various species. AVAILABILITY https://github.com/jlugomar/hypergraphlet-kernels/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.