Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Precision in the Mammalian Circadian System: A Reliable Clock from Less Reliable Neurons

The mammalian SCN contains a biological clock that drives remarkably precise circadian rhythms in vivo and in vitro. This study asks whether the cycle-to-cycle variability of behavioral rhythms in mice can be attributed to precision of individual circadian pacemakers within the SCN or their interactions. The authors measured the standard deviation of the cycle-to-cycle period from 7-day recordings of running wheel activity, Period1 gene expression in cultured SCN explants, and firing rate patterns of dispersed SCN neurons. Period variability of the intact tissue and animal was lower than single neurons. The median variability of running wheel and Period1 rhythms was less than 40 min per cycle compared to 2.1 h in firing rate rhythms of dispersed SCN neurons. The most precise SCN neuron, with a period deviation of 1.1 h, was 10 times noisier than the most accurate SCN explant (0.1 h) or mouse (0.1 h) but comparable to the least stable explant (2.1 h) and mouse (1.1 h). This variability correlated with intrinsic period in mice and SCN explants but not with single cells. Precision was unrelated to the amplitude of rhythms and did not change significantly with age up to 1 year after birth. Analysis of the serial correlation of cycle-to-cycle period revealed that approximately half of this variability is attributable to noise outside the pacemaker. These results indicate that cell-cell interactions within the SCN reduce pacemaker noise to determine the precision of circadian rhythms in the tissue and in behavior.
Absolute and relative caloric responses

The statistical features of absolute-based and percentage-based post-caloric EOG response measures were investigated on ten normal and twenty pathological cases. Criteria for the best EOG response measure are introduced. It is concluded that, using the method adopted in this study, the mean interbeat interval can be regarded as the measure of choice for quantifying the post-caloric EOG response.
Distributed parameter bioprocess plant identification and I-term control using centralized recurrent neural network models

In the paper, it is proposed to use a recurrent neural network model, and a real-time Levenberg-Marquardt algorithm of its learning for centralized data-based modeling, identification and control of an anaerobic digestion bioprocess, carried out in a fixed bed and a recirculation tank of a wastewater treatment system. The analytical model of the digestion bioprocess, used as process data generator, represented a distributed parameter system, which is reduced to a lumped system using the orthogonal collocation method, applied in four collocation points plus one- in the recirculation tank. The paper proposed to use three types of I-term adaptive control: direct adaptive integral plus states neural control, indirect adaptive I-term sliding mode control and real-time I-term optimal control. The comparative graphical simulation results of the digestion wastewater treatment system control, exhibited a good convergence and precise reference tracking, giving slight priority to the direct control with respect to the other methods of control applied.
Influence of abiotic and biotic factors on maturation of oocytes (mammalian eggs) in vitro conditions

In last two decades it is obvious that mammalian somatic cells, and according to our investigation oocytes, deliver paracrine substances in microenvironment, which save them from pathogens. At the same time, many investigations have been done, among others and ours, about influence of different micro-organisms, elements and compounds, organic or inorganic, on in vitro maturation of oocytes (model system for in vivo meiotic maturation). Reciprocal impact between micro-organisms and oocytes as well as different substances and oocytes are obvious, although biochemical and molecular biological mechanisms of these influences is complex and not enough investigated. Therefore, many questions about oocytes and IVM are in front of scientists.
Eating patterns and nutritional characteristics associated with sleep duration

Abstract Objective To identify major meal and snack eating patterns, and examine their relationships with sleep duration. Design The analyses included 27 983 participants in a prospective cohort study of women aged 35 to 74 years in the USA or Puerto Rico. Results The principal component analysis of eight meal and snack frequency items at different times across the day yielded two major eating patterns: (i) eating during conventional eating hours (defined as times from breakfast to dinner); and (ii) dominance of snacks over meals. Comparing the identified eating patterns among women with varying sleep duration (<5, 5–5·9, 6–6·9, 7–7·9, 8–8·9, 9–9·9 and ≥10 h daily), the tendency for eating during conventional eating hours decreased with decreasing sleep duration: adjusted mean score of −0·54 (95 % CI –0·68, –0·41) in women sleeping for <5 h daily v. 0·08 (95 % CI 0·06, 0·11) among those with 7–7·9 h of sleep daily. The extent of snack dominance over meals increased in women with shorter sleep. Women with long (≥10 h) sleep duration had eating patterns similar to those with short (<6 h) sleep duration. Lower tendency for eating during conventional eating hours and greater snack dominance over meals were also related to higher intakes of fat and sweets for energy and lower intakes of fruits and vegetables. Conclusions Disrupted eating patterns and diet of poor nutritional quality may exacerbate the development of obesity and metabolic diseases in habitual short and very long sleepers.
Understanding Astrophysical Noise from Stellar Surface Magneto-Convection

To obtain cm/s precision, stellar surface magneto-convection must be disentangled from observed radial velocities (RVs). In order to understand and remove the convective signature, we create Sun-as-a-star model observations based on a 3D magnetohydrodynamic solar simulation. From these Sun-as-a-star model observations, we find several line characteristics are correlated with the induced RV shifts. The aim of this campaign is to feed directly into future high precision RV studies, such as the search for habitable, rocky worlds, with forthcoming spectrographs such as ESPRESSO.
Globally optimal solution to multi-object tracking with merged measurements

Multiple object tracking has been formulated recently as a global optimization problem, and solved efficiently with optimal methods such as the Hungarian Algorithm. A severe limitation is the inability to model multiple objects that are merged into a single measurement, and track them as a group, while retaining optimality. This work presents a new graph structure that encodes these multiple-match events as standard one-to-one matches, allowing computation of the solution in polynomial time. Since identities are lost when objects merge, an efficient method to identify groups is also presented, as a flow circulation problem. The problem of tracking individual objects across groups is then posed as a standard optimal assignment. Experiments show increased performance on the PETS 2006 and 2009 datasets compared to state-of-the-art algorithms.
Strong Ligand-Protein Interactions Derived from Diffuse Ligand Interactions with Loose Binding Sites

Many systems in biology rely on binding of ligands to target proteins in a single high-affinity conformation with a favorable ΔG. Alternatively, interactions of ligands with protein regions that allow diffuse binding, distributed over multiple sites and conformations, can exhibit favorable ΔG because of their higher entropy. Diffuse binding may be biologically important for multidrug transporters and carrier proteins. A fine-grained computational method for numerical integration of total binding ΔG arising from diffuse regional interaction of a ligand in multiple conformations using a Markov Chain Monte Carlo (MCMC) approach is presented. This method yields a metric that quantifies the influence on overall ligand affinity of ligand binding to multiple, distinct sites within a protein binding region. This metric is essentially a measure of dispersion in equilibrium ligand binding and depends on both the number of potential sites of interaction and the distribution of their individual predicted affinities. Analysis of test cases indicates that, for some ligand/protein pairs involving transporters and carrier proteins, diffuse binding contributes greatly to total affinity, whereas in other cases the influence is modest. This approach may be useful for studying situations where “nonspecific” interactions contribute to biological function.
Making High-Tech Service Robot Platforms Available

The development of reliable, robust and capable service robot platforms is expensive and time consuming. On the other hand, stable hardware is a prerequisite to be able to concentrate on the targeted research area. This paper proposes a strategy to make such high-end service robot platforms available as research platform, by means of the example of Care-O-bot(R) 3 developed at Fraunhofer IPA. Several methods to provide robotic hardware for research are introduced, starting from the maintenance of a common open-source repository, to the making available of simulation models of the hardware components and the possibility of remote access via a web-interface. It is shown, that Care-O-bot(R) 3 is particularly suited as research platform through the modular structure of its hardware components. Thus, a very flexible set-up for Care-O-bot(R) 3 is possible, making it easy to adapt to the specific requirements of different robotic research fields.
Inhibition of Murine Splenic and Mucosal Lymphocyte Function by Enteric Bacterial Products

ABSTRACT Previously we showed that lysates of enteropathogenicEscherichia coli (EPEC) inhibit lymphokine production by mitogen-activated human peripheral blood and lamina propria mononuclear cells. The aims of the present study were to determine whether EPEC-inhibitory factors have similar effects on murine lymphoid populations in order to further delineate the mechanisms of alteration of cytokine production. Preexposure to EPEC lysates inhibited mitogen-stimulated interleukin-2 (IL-2), IL-4, and gamma interferon (IFN-γ) production by murine spleen cells, but IL-10 production was increased. The inhibition was not due to increased apoptosis and was not blocked by neutralizating antibodies against IL-10 or transforming growth factor β (TGF-β). EPEC lysates also inhibited mitogen-stimulated IL-2 and IFN-γ production by CD11b-depleted spleen cells, IL-2 and IL-4 production by intraepithelial and Peyer’s patch lymphocytes, IL-2 production by the human T-cell line Jurkat, and antigen-stimulated IL-2 production by murine spleen cells. Lysates obtained from Shiga-like toxin-producing E. coli, E. coli RDEC-1, Citrobacter rodentium, and an EPECespB insertion mutant all inhibited IL-2 and IL-4 production by mitogen-stimulated lymphoid cells. In conclusion, lysates of EPEC and related bacteria directly inhibit cytokine production by lymphoid cells from multiple sites by a mechanism that does not increase apoptosis or result from secondary effects of IL-10 or TGF-β.
A survey of computer vision applied in Aerial robotic Vehicles

Unmanned Aerial Vehicles (UAVs) are expected to serve as aerial robotic vehicles to perform tasks on their own. Computer vision is applied in UAVs to improve their autonomies both in flight control and perception of environment around them. A survey of researches in such a field is presented. Based on images and videos captured by on-board camera(s), vision measures, such as stereo vision, optical flow fields etc. extract useful features which can be integrated with flight control system to form visual servoing. Three main interests in this field are discussed: visual navigation, aerial surveillance and airborne visual Simultaneous Localization and Mapping (SLAM).
Combining Multiple Resting-State fMRI Features during Classification: Optimized Frameworks and Their Application to Nicotine Addiction

Machine learning techniques have been applied to resting-state fMRI data to predict neurological or neuropsychiatric disease states. Existing studies have used either a single type of resting-state feature or a few feature types (<4) in the prediction model. However, resting-state data can be processed in many different ways, yielding different feature types containing complementary and/or novel information, leaving uncertain the most informative features to provide to the classifier. In this study, multiple resting-state features were calculated from two main analytical categories: local measures and network measures. Feature selection was adopted using an optimized grid-search approach selecting top ranked features from statistical tests. We then tested three optimized frameworks: feature combination, kernel combination, and classifier combination, all using the support vector machine as an elementary classifier, to combine these resting-state feature types. When applied to nicotine addiction, with a cohort size of 100 smokers and 100 non-smokers, via a 10-fold cross-validation procedure, the feature combination and the classifier combination achieved an accuracy of 75.5%, while the kernel combination achieved a 73.0% accuracy; all three combination frameworks improved classification performance compared to the single feature type based results (best accuracy 70.5%). This study not only reveals the discriminative power of resting-state data, but also demonstrates the efficiency of combining multiple features from one data phenotype to improve classification performance.
Using Regression Error Characteristic Curves for Model Selection in Ensembles of Neural Networks

Regression Error Characteristic (REC) analysis is a technique for evaluation and comparison of regression models that facilitates the visualization of the performance of many regression functions simultaneously in a single graph. The objective of this work is to present a new approach for model selection in ensembles of Neural Networks, in which we propose the use of REC curves in order to select a good threshold value, so that only residuals greater than that value are considered as errors. The algorithm was empirically evaluated and its results were analyzed also by means of REC curves.
Relationship of chromatic visual‐evoked potentials and the changes of foveal photoreceptor layer in central serous chorioretinopathy patients

Citation information: Wu F, Yang Y, Li H & Odom JV. Relationship of chromatic visual‐evoked potentials and the changes of foveal photoreceptor layer in central serous chorioretinopathy patients. Ophthalmic Physiol Opt 2011, 31, 381–388. doi: 10.1111/j.1475‐1313.2011. 00839.x
Metal Ion Activation of Clostridium sordellii Lethal Toxin and Clostridium difficile Toxin B

Lethal Toxin from Clostridium sordellii (TcsL) and Toxin B from Clostridium difficile (TcdB) belong to the family of the “Large clostridial glycosylating toxins. ” These toxins mono-O-glucosylate low molecular weight GTPases of the Rho and Ras families by exploiting UDP-glucose as a hexose donor. TcsL is casually involved in the toxic shock syndrome and the gas gangrene. TcdB—together with Toxin A (TcdA)—is causative for the pseudomembranous colitis (PMC). Here, we present evidence for the in vitro metal ion activation of the glucosyltransferase and the UDP-glucose hydrolysis activity of TcsL and TcdB. The following rating is found for activation by divalent metal ions: Mn2+ > Co2+ > Mg2+ >> Ca2+, Cu2+, Zn2+. TcsL and TcdB thus require divalent metal ions providing an octahedral coordination sphere. The EC50 values for TcsL were estimated at about 28 µM for Mn2+ and 180 µM for Mg2+. TcsL and TcdB further require co-stimulation by monovalent K+ (not by Na+). Finally, prebound divalent metal ions were dispensible for the cytopathic effects of TcsL and TcdB, leading to the conclusion that TcsL and TcdB recruit intracellular metal ions for activation of the glucosyltransferase activity. With regard to the intracellular metal ion concentrations, TcsL and TcdB are most likely activated by K+ and Mg2+ (rather than Mn2+) in mammalian target cells.
On the ICP algorithm

We present upper and lower bounds for the number of iterations performed by the Iterative Closest Point (ICP) algorithm. This algorithm has been proposed by Besl and McKay [4] as a successful heuristics for pattern matching under translation, where the input consists of two point sets in d-space, for d≥1, but so far it seems not to have been rigorously analyzed. We consider two standard measures of resemblance that the algorithm attempts to optimize: The RMS (root mean squared distance) and the (one-directional) Hausdorff distance. We show that in both cases the number of iterations performed by the algorithm is polynomial in the number of input points. In particular, this bound is quadratic in the one-dimensional problem, for which we present a lower bound construction of Ω(n logn) iterations under the RMS measure, where n is the overall size of the input. Under the Hausdorff measure, this bound is only O(n) for input point sets whose spread is polynomial in n, and this is tight in the worst case.We also present several structural geometric properties of the algorithm under both measures. For the RMS measure, we show that at each iteration of the algorithm the cost function monotonically and strictly decreases along the vector Δt of the relative translation. As a result, we conclude that the polygonal path π, obtained by concatenating all the relative translations that are computed during the execution of the algorithm, does not intersect itself. In particular, in the one-dimensional problem all the relative translations of the ICP algorithm are in the same (left or right) direction. For the Hausdorff measure, some of these properties continue to hold (such as monotonicity in one dimension), whereas others do not.
Gender-predicted attitudes toward gangs in non-metropolitan communities

Little research exists that has examined the predictors of attitudes toward gangs held by young adults in non-metropolitan communities. A predictive correlational research study was conducted to determine the significant predictors of attitudes toward gangs held by micropolitan and small-metropolitan community young adults between the ages of eighteen and 25. By collecting surveys from this sample population, the researchers found that gender was a significant predictor of attitudes toward gangs within this sample population. Implications and recommendations for gang prevention and reduction measures are suggested along with recommendations for future research examining attitudes toward gangs.
Correlation between self-reported mood states and objectively measured social interactions at work: A pilot study

A number of clinical studies investigated associations between mood states and environmental factors. However, they mostly rely on self-reporting methods to describe past activities which, due to recall difficulties, may not be reliable. In this pilot study, we attempted to measure the amount of social interaction at workplace in an objective way and to investigate correlations with mood states. The results show correlation between social interactions and mood states both in the beginning and at the end of monitored intervals.
Contribution of Stereo Information to Feature-Based Pattern Classification for Audio Semantic Analysis

The current paper focuses on the evaluation of the contribution of stereo information to feature-based pattern classification for audio semantic analysis. In radio productions, where multiple speakers' voices and music coexist, it is quite important to detect voice and music patterns, in order to achieve efficient classification of the audio content. Recent research confirmed that the process of audio feature extraction and selection is critical for the overall system performance. In the current work, the feature extraction procedure is applied in each audio channel of stereo radio programmes separately in order to exploit the differences of audio information between channels. Feature ranking algorithms are employed, aiming to investigate the saliency of the feature differences. The classification performance of the implemented artificial neural systems revealed that the stereo differences have to be taken into consideration during audio semantic analysis.
Peracute Sodium Toxicity in Free-ranging Black-bellied Whistling Duck Ducklings

From 23 to 25 July 2002, 98–103 newly hatched black-bellied whistling ducks (Dendrocygna autumnalis) were observed alive at an inland saline lake (La Sal Vieja) in Willacy County, Texas (USA). Seventy-one (71%) died after showing signs indicative of sodium toxicity within 5 hr of entering the water; some died within minutes. Six carcasses were sent to the United States Geological Survey, National Wildlife Health Center (Madison, Wisconsin, USA) for analysis, and brain sodium levels of all ducklings were above 2,000 parts per million wet weight. More black-bellied whistling duck ducklings are likely to have been affected, but they were not observed after hatching.
IMGT-Choreography for immunogenetics and immunoinformatics

IMGT, the international ImMunoGeneTics information system (http://imgt.cines.fr), was created in 1989 at Montpellier, France. IMGT is a high quality integrated knowledge resource specialized in immunoglobulins (IG), T cell receptors (TR), major histocompatibility complex (MHC) of human and other vertebrates, and related proteins of the immune system (RPI) which belong to the immunoglobulin superfamily (IgSF) and MHC superfamily (MhcSF). IMGT provides a common access to standardized data from genome, proteome, genetics and three-dimensional structures. The accuracy and the consistency of IMGT data are based on IMGT-ONTOLOGY, a semantic specification of terms to be used in immunogenetics and immunoinformatics. IMGT-ONTOLOGY has been formalized using XML Schema (IMGT-ML) for interoperability with other information systems. We are developing Web services to automatically query IMGT databases and tools. This is the first step towards IMGT-Choreography which will trigger and coordinate dynamic interactions between IMGT Web services to process complex significant biological and clinical requests. IMGT-Choreography will further increase the IMGT leadership in immunogenetics and immunoinformatics for medical research (repertoire analysis of the IG antibody sites and of the TR recognition sites in autoimmune and infectious diseases, AIDS, leukemias, lymphomas, myelomas), veterinary research (IG and TR repertoires in farm and wild life species), genome diversity and genome evolution studies of the adaptive immune responses, biotechnology related to antibody engineering (single chain Fragment variable (scFv), phage displays, combinatorial libraries, chimeric, humanized and human antibodies), diagnostics (detection and follow up of residual diseases) and therapeutical approaches (grafts, immunotherapy, vaccinology). IMGT is freely available at http://imgt.cines. fr.
SPIHT image compression with transmission of auxiliary information and selective modification of high frequency coefficients

The SPIHT algorithm is a popular algorithm using wavelet techniques for image compression. In this paper, we present modifications to the algorithm by: 1) sending additional information to the decoder about the largest magnitudes in the frequency subbands; and 2) modifying selected coefficients in the high frequency subbands. The proposed modifications give an improvement to the algorithm performance without incurring much additional complexity.