Miracle-dz/UniFAR-FTUs huggingface dataset:
Instrumentation for the remote monitoring of physiological and behavioral variables.

Few commercial products are available for investigators who wish to monitor multiple physiological and behavioral variables in unrestrained subjects. When telemetry is not practical, e.g., in studies of at-sea diving physiology, one of the only options is to design and build a custom data-logging instrument. This paper describes how a data logger was developed for the successful long-term monitoring of dive depth, swim speed, heart rate, water temperature, and multiple body temperatures from free-ranging northern elephant seals. The task was facilitated by using a commercially available single-board computer designed specifically for portable multichannel data acquisition and, where possible, off-the-shelf sensors/transducers available with integrated signal-conditioning circuits. A smaller data logger for monitoring the electrocardiogram, body temperature, and dive behavior of double-crested cormorants is also described to illustrate the flexibility and simplicity of this approach. Although it is customized for diving animals, with incorporation of the appropriate sensors the basic design should be applicable to studies of comparative, environmental, or exercise physiology involving most medium-to-large animals, including humans.
Age-Related Differences in Functional Nodes of the Brain Cortex – A High Model Order Group ICA Study

Functional MRI measured with blood oxygen dependent (BOLD) contrast in the absence of intermittent tasks reflects spontaneous activity of so-called resting state networks (RSN) of the brain. Group level independent component analysis (ICA) of BOLD data can separate the human brain cortex into 42 independent RSNs. In this study we evaluated age-related effects from primary motor and sensory, and, higher level control RSNs. One hundred sixty-eight healthy subjects were scanned and divided into three groups: 55 adolescents (ADO, 13.2 ± 2.4 years), 59 young adults (YA, 22.2 ± 0.6 years), and 54 older adults (OA, 42.7 ± 0.5 years), all with normal IQ. High model order group probabilistic ICA components (70) were calculated and dual-regression analysis was used to compare 21 RSN's spatial differences between groups. The power spectra were derived from individual ICA mixing matrix time series of the group analyses for frequency domain analysis. We show that primary sensory and motor networks tend to alter more in younger age groups, whereas associative and higher level cognitive networks consolidate and re-arrange until older adulthood. The change has a common trend: both spatial extent and the low frequency power of the RSN's reduce with increasing age. We interpret these result as a sign of normal pruning via focusing of activity to less distributed local hubs.
Isolation and characterization of a haploid germ cell specific sperm associated antigen 9 (SPAG9) from the baboon

Previously, we cloned and sequenced a sperm specific antigen, designated as HSS (EMBL nomenclature human sperm associated antigen 9: hSPAG9) from human testis (Shankar et al.: Biochem Biophys Res Commun 243:561–565, 1998). The present study was conducted to isolate baboon proteomic homologue in order to find out whether the baboon can provide a suitable model for examining its immunocontraception effects. Baboon SPAG9 (bSPAG9) was cloned and sequenced from the baboon testis cDNA library. The baboon cDNA contained open reading frame encoding 760 amino acids. A 90.6 and 96.8% homology between baboon and human SPAG9 was found at protein and DNA levels. Analysis for tissue specificity by Northern blot procedure using various baboon tissues indicated that bSPAG9 was specifically expressed only in the baboon testis. Further, cell type expression analysis by in situ hybridization in baboon testis demonstrated the expression of bSPAG9 mRNA transcript only in the round spermatid suggesting haploid germ cell expression. Anti‐human SPAG9 antibodies recognized the acrosomal compartment region of baboon sperm in indirect immunofluorescence (IIF). Flow cytometry analysis showed surface localization of bSPAG9 in live baboon sperm. The amino acid sequence data for nonhuman primate SPAG9 suggest that antibodies generated by vaccinating baboon with hSPAG9 will recognize nonhuman primate SPAG9, supporting the testing of SPAG9 contraceptive vaccine based on hSPAG9 in the nonhuman primate model. Mol. Reprod. Dev. 69: 186–193, 2004. © 2004 Wiley‐Liss, Inc.
Sunlight Induced Rapid Oil Absorption and Passive Room‐Temperature Release: An Effective Solution toward Heavy Oil Spill Cleanup

Rapid cleanup and easy recovery of spilled heavy oils is always a great challenge due to their high viscosity (>103 mPa s). One of the efficient methods to absorb highly viscous oils is to reduce their viscosity by increasing their temperature. In this work, the authors integrate the sunlight‐induced light‐to‐heat conversion effect of polypyrrole (PPy) and thermoresponsive property of poly(N‐isopropylacrylamide) (PNIPAm) into the melamine sponge, which successfully delivers a fast heavy oil absorption under sunlight and passive oil release underwater at room temperature. Thanks to the rationally designed functionalities, the PNIPAm/PPy functionalized sponges possess oleophilicity and hydrophobicity under sunlight. Due to the photothermal effect of PPy, the sponges locally heat up contacting heavy oil under sunlight and reduce its viscosity to a point where the oil voluntarily flow into the pores of the sponge. The material in this work is able to rapidly absorb the heavy oil with room temperature viscosity as high as ≈1.60 × 105 mPa s. The absorbed oil can be passively forced out the sponge underwater at room temperature due to the hydrophilicity of PNIPAm. The sunlight responsive and multifunctional sponge represents a meaningful attempt in coming up with a sustainable solution toward heavy oil spill.
Transmit beamforming for physical-layer multicasting

This paper considers the problem of downlink transmit beamforming for wireless transmission and downstream precoding for digital subscriber wireline transmission, in the context of common information broadcasting or multicasting applications wherein channel state information (CSI) is available at the transmitter. Unlike the usual "blind" isotropic broadcasting scenario, the availability of CSI allows transmit optimization. A minimum transmission power criterion is adopted, subject to prescribed minimum received signal-to-noise ratios (SNRs) at each of the intended receivers. A related max-min SNR "fair" problem formulation is also considered subject to a transmitted power constraint. It is proven that both problems are NP-hard; however, suitable reformulation allows the successful application of semidefinite relaxation (SDR) techniques. SDR yields an approximate solution plus a bound on the optimum value of the associated cost/reward. SDR is motivated from a Lagrangian duality perspective, and its performance is assessed via pertinent simulations for the case of Rayleigh fading wireless channels. We find that SDR typically yields solutions that are within 3-4 dB of the optimum, which is often good enough in practice. In several scenarios, SDR generates exact solutions that meet the associated bound on the optimum value. This is illustrated using measured very-high-bit-rate Digital Subscriber line (VDSL) channel data, and far-field beamforming for a uniform linear transmit antenna array.
Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks

This paper demonstrates the use of qualitative probabilistic networks (QPNs) to aid Dynamic Bayesian Networks (DBNs) in the process of learning the structure of gene regulatory networks from microarray gene expression data. We present a study which shows that QPNs define monotonic relations that are capable of identifying regulatory interactions in a manner that is less susceptible to the many sources of uncertainty that surround gene expression data. Moreover, we construct a model that maps the regulatory interactions of genetic networks to QPN constructs and show its capability in providing a set of candidate regulators for target genes, which is subsequently used to establish a prior structure that the DBN learning algorithm can use and which 1) distinguishes spurious correlations from true regulations, 2) enables the discovery of sets of coregulators of target genes, and 3) results in a more efficient construction of gene regulatory networks. The model is compared to the existing literature using the known gene regulatory interactions of Drosophila Melanogaster.
Nitrite and Nitrate Levels in Individual Molluscan Neurons: Single‐Cell Capillary Electrophoresis Analysis

Abstract: Cell and tissue concentrations of NO2− and NO3− are important indicators of nitric oxide synthase activity and crucial in the regulation of many metabolic functions, as well as in nonenzymatic nitric oxide release. We adapted the capillary electrophoresis technique to quantify NO2− and NO3− levels in single identified buccal neurons and ganglia in the opisthobranch mollusc Pleurobranchaea californica, a model system for the study of the chemistry of neuron function. Neurons were injected into a 75‐µm separation capillary and the NO2− and NO3− were separated electrophoretically from other anions and detected by direct ultraviolet absorbance. The limits of detection for NO2− and NO3− were <200 fmol (<4 µM in the neurons under study). The NO2− and NO3− levels in individual neurons varied from 2 mM (NO2−) and 12 mM (NO3−) in neurons histochemically positive for NADPH‐diaphorase activity down to undetectable levels in many NADPH‐diaphorase‐negative cells. These results affirm the correspondence of histochemical NADPH‐diaphorase activity and nitric oxide synthase in molluscan neurons. NO2− was not detected in whole ganglion homogenates or in hemolymph, whereas hemolymph NO3− averaged 1.8 ± 0.2 × 10−3M. Hemolymph NO3− in Pleurobranchaea was appreciably higher than values measured for the freshwater pulmonate Lymnaea stagnalis (3.2 ± 0.2 × 10−5M) and for another opisthobranch, Aplysia californica (3.6 ± 0.7 × 10−4M). Capillary electrophoresis methods provide utility and convenience for monitoring NO2−/NO3− levels in single cells and small amounts of tissue.
Peptide Modulation of Class I Major Histocompatibility Complex Protein Molecular Flexibility and the Implications for Immune Recognition*

Background: Peptide modulation of MHC flexibility can affect recognition by immune receptors. Results: Different peptides alter the flexibility of the MHC protein at sites around the peptide-binding groove. Conclusion: Peptide modulation of MHC flexibility is not limited to specific peptides or isolated regions. Significance: Peptide modulation of MHC flexibility indicates an extension of antigenicity from the peptide to the MHC. T cells use the αβ T cell receptor (TCR) to recognize antigenic peptides presented by class I major histocompatibility complex proteins (pMHCs) on the surfaces of antigen-presenting cells. Flexibility in both TCRs and peptides plays an important role in antigen recognition and discrimination. Less clear is the role of flexibility in the MHC protein; although recent observations have indicated that mobility in the MHC can impact TCR recognition in a peptide-dependent fashion, the extent of this behavior is unknown. Here, using hydrogen/deuterium exchange, fluorescence anisotropy, and structural analyses, we show that the flexibility of the peptide binding groove of the class I MHC protein HLA-A*0201 varies significantly with different peptides. The variations extend throughout the binding groove, impacting regions contacted by TCRs as well as other activating and inhibitory receptors of the immune system. Our results are consistent with statistical mechanical models of protein structure and dynamics, in which the binding of different peptides alters the populations and exchange kinetics of substates in the MHC conformational ensemble. Altered MHC flexibility will influence receptor engagement, impacting conformational adaptations, entropic penalties associated with receptor recognition, and the populations of binding-competent states. Our results highlight a previously unrecognized aspect of the “altered self” mechanism of immune recognition and have implications for specificity, cross-reactivity, and antigenicity in cellular immunity.
Virulence markers associated with Trueperella pyogenes infections in livestock and companion animals

Trueperella pyogenes is an opportunistic pathogen that causes diverse pyogenic infections in livestock. The genes that encode the exotoxin pyolysin (plo) and other putative factors that promote adhesion of pathogen to host cells (fimbriae fimA, fimC, fimE, fimG, neuraminidases nanH, nanP, and collagen‐binding protein cbpA) have been associated with virulence, particularly in mastitis and uterus infections of dairy cows. However, the role of these virulence markers in the pathogenicity of the agent in domestic animals infections still is incompletely understood. The genes plo, fimA, fimC, fimE, fimG, nanH, nanP, and cbpA were investigated in 71 T. pyogenes strains recovered from cattle, sheep, goats, dogs, equines, and a pig, recovered from mastitis (n = 35), and non‐mastitis (n = 36) cases (abscesses, reproductive tract diseases, pneumonia, lymphadenitis, encephalitis). The most common genes harboured by the isolates were: plo (71/71 = 100·0%), fimA (70/71 = 98·6%), nanP (56/71 = 78·9%), fimE (53/71 = 74·6%), fimC (46/71 = 64·8%) and nanH (45/71 = 63·4%), whereas cbpA (6/71 = 8·4%) and fimG (4/71 = 5·6%) were uncommon. The most frequent genotypes were plo/fimA/fimE/fimC/nanH/nanP (17/71 = 23·9%), plo/fimA/fimE/nanH/nanP (13/71 = 18·3%), and plo/fimA/fimE/fimC/nanP (11/71 = 15·5%). No association was observed between the presence of genes vs clinical signs or host species. To the best of our knowledge, this is the first report on aforementioned virulence factors of pathogen detected in diseased horses and dogs.
Distributed Requirements Elicitation Using a Spatial Hypertext Wiki

In Global Software Development (GSD), distributed stakeholders (e.g. team members, customers, etc) have to collaborate and communicate in an efficient and effective way to share, create and discuss knowledge. Nowadays, a challenge is to provide integrated collaborative tools that implement creativity techniques which allow distributed stakeholders to externalize their knowledge through brainstorming and share and store knowledge in a common repository. The Requirements Elicitation (RE) process is a clear example where this kind of support is needed in the software development process. This paper presents the Spatial Hypertext Wiki as a collaborative tool for supporting creativity in the RE process. The Spatial Hypertext characteristics of the wiki provide a virtual board where distributed stakeholders can share, brainstorm, negotiate, or prioritize the knowledge involved in RE.
Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center

We present Mesos, a platform for sharing commodity clusters between multiple diverse cluster computing frameworks, such as Hadoop and MPI. Sharing improves cluster utilization and avoids per-framework data replication. Mesos shares resources in a fine-grained manner, allowing frameworks to achieve data locality by taking turns reading data stored on each machine. To support the sophisticated schedulers of today's frameworks, Mesos introduces a distributed two-level scheduling mechanism called resource offers. Mesos decides how many resources to offer each framework, while frameworks decide which resources to accept and which computations to run on them. Our results show that Mesos can achieve near-optimal data locality when sharing the cluster among diverse frameworks, can scale to 50,000 (emulated) nodes, and is resilient to failures.
Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes.

BACKGROUND Many alcohol research questions require methods that take a person-centered approach because the interest is in finding heterogeneous groups of individuals, such as those who are susceptible to alcohol dependence and those who are not. A person-centered focus also is useful with longitudinal data to represent heterogeneity in developmental trajectories. In alcohol, drug, and mental health research the recognition of heterogeneity has led to theories of multiple developmental pathways. METHODS This paper gives a brief overview of new methods that integrate variable- and person-centered analyses. Methods discussed include latent class analysis, latent transition analysis, latent class growth analysis, growth mixture modeling, and general growth mixture modeling. These methods are presented in a general latent variable modeling framework that expands traditional latent variable modeling by including not only continuous latent variables but also categorical latent variables. RESULTS Four examples that use the National Longitudinal Survey of Youth (NLSY) data are presented to illustrate latent class analysis, latent class growth analysis, growth mixture modeling, and general growth mixture modeling. Latent class analysis of antisocial behavior found four classes. Four heavy drinking trajectory classes were found. The relationship between the latent classes and background variables and consequences was studied. CONCLUSIONS Person-centered and variable-centered analyses typically have been seen as different activities that use different types of models and software. This paper gives a brief overview of new methods that integrate variable- and person-centered analyses. The general framework makes it possible to combine these models and to study new models serving as a stimulus for asking research questions that have both person- and variable-centered aspects.
Channel Estimation Technique for Amplify and Forward Based Wireless Relay Networks Using BLUE Approach

In this paper, best linear unbiased estimation (BLUE) approach based algorithm is proposed to estimate the overall channel impulse response between the source and destination of amplify and forward (AF) strategy based wireless relay networks (WRN). BLUE is most suitable for practical implementations and can be determined with knowledge of only the first and second moments of the probability density function (pdf). It exhibits the properties of linearity and unbiased. The simulation results show that mean square error (MSE) performance of the channel estimation algorithm using BLUE is better than the conventional least squares (LS) and minimum mean square error (MMSE) based approaches at lower signal to noise ratio (SNR). Cramer-Rao lower bound (CRLB) is derived to validate the MSE performance. The proposed channel estimation algorithm approaches CRLB as the length of the training sequence and number of relays increases.
A Generic load model for simulation studies of microgrids

In an islanded microgrid, loads impose strong transient impacts on the DER units and can also dynamically interact with the DER units. Thus, adequate load models are important for studying the dynamics as well as steady-state performance of an islanded microgrid. This paper presents the structure of a generic load model that can emulate the steady-state and dynamic behaviors of different loads. The load is simulated by three dependent current sources, one per phase, whose control signals are determined from a dq- to abc-frame transformation block. Thus, the d- and q-axis components of the load current are dynamically determined based on the d- and q-axis components of the load voltage. The parameters of the load model can be selected for different dynamic and steady-state characteristics. An example is given to show that the proposed load model can emulate the dynamic and steady-state characteristic of an RL load.
K-disjointness paradigm with application to symmetry detection for incompletely specified functions

In this paper, we propose a K-disjointness paradigm that can effectively search all pairs of minterms with Hamming distance K between two Boolean functions. By this paradigm, we correlate it with symmetry detection problem and propose an efficient symmetry detection algorithm for Boolean functions. Our algorithm can not only handle completely specified functions but also incompletely specified functions. Experimental results on a set of MCNC and ISCAS benchmarking circuits show that our algorithm is indeed very effective and efficient for detecting symmetries of large Boolean functions.
Magnetic-Thermal-Fluidic Analysis for Cooling Performance of Magnetic Nanofluids Comparing With Transformer Oil and Air by Using Fully Coupled Finite Element Method

Magnetic-thermal-fluidic analyses were conducted to assess the cooling performance of magnetic nanofluids by comparing the performance with that of transformer oil and air using the fully coupled finite element method (FEM) considering the magnetoconvection phenomena. Magnetic nanofluids (MNFs) have been studied extensively for bio- and nanotechnology applications. In particular, some studies reported that the MNF has good characteristics for thermal management and electric insulation in experiments. With this motivation, this study focused on the cooling performance of MNFs including the experimental and numerical approaches. Until now, research on the cooling effect of MNF has focused mainly on heat propagation without any real magnetic system, in which the magnetic force plays a key role in driving fluidic flow. This flow driven by the magnetic force density was related to the magnetoconvection effect. To analyze this effect quantitatively, a coupled analysis technique should be developed using the magnetic-thermal-fluidic equations. To validate the cooling performance of MNFs numerically, the numerical results were verified by a comparison with those from the experimental tests in the air, transformer oil, and MNF. After confirming the numerical setup, some experiments were conducted with a vertical solenoid coil immersed in a MNF with different volume fractions of magnetic nanoparticles. The temperature at the inside part of the coil decreased dramatically by approximately 5$^{\circ}$ C in the 7 vol. % MNF compared to the transformer oil. These temperatures were also predicted well using the proposed numerical setup.
Algorithms for Optimizing the Ratio of Submodular Functions

We investigate a new optimization problem involving minimizing the Ratio of two Submodular (RS) functions. We argue that this problem occurs naturally in several real world applications. We then show the connection between this problem and several related problems including minimizing the difference between submodular functions (Iyer & Bilmes, 2012b; Narasimhan & Bilmes, 2005), and to submodular optimization subject to submodular constraints (Iyer & Bilmes, 2013). We show that RS optimization can be solved with bounded approximation factors. We also provide a hardness bound and show that our tightest algorithm matches the lower bound up to a log factor. Finally, we empirically demonstrate the performance and good scalability properties of our algorithms.