Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Temperature Magnetism as a Probe for Structural and Compositional Uniformity in Ligand-Capped Magnetite Nanoparticles

To investigate magnetostructural relationships in colloidal magnetite (Fe3O4) nanoparticles (NPs) at high temperature (300–900 K), we measured the temperature dependence of magnetization (M) of oleate-capped magnetite NPs ca. 20 nm in size. Magnetometry revealed an unusual irreversible high-temperature dependence of M for these NPs, with dip and loop features observed during heating–cooling cycles. Detailed characterizations of as-synthesized and annealed Fe3O4 NPs as well as reference ligand-free Fe3O4 NPs indicate that both types of features in M(T) are related to thermal decomposition of the capping ligands. The ligand decomposition upon the initial heating induces a reduction of Fe3+ to Fe2+ and the associated dip in M, leading to more structurally and compositionally uniform magnetite NPs. Having lost the protective ligands, the NPs continually sinter during subsequent heating cycles, resulting in divergent M curves featuring loops. The increase in M with sintering proceeds not only through elimination of a magnetically dead layer on the particle surface, as a result of a decrease in specific surface area with increasing size, but also through an uncommonly invoked effect resulting from a significant change in Fe3+/Fe2+ ratio with heat treatment. The interpretation of irreversible features in M(T) indicates that reversible M(T) behavior, conversely, can be expected only for ligand-free, structurally and compositionally uniform magnetite NPs, suggesting a general applicability of high-temperature M(T) measurements as an analytical method for probing the structure and composition of magnetic nanomaterials.
Oxygen Supply by Photosynthesis to an Implantable Islet Cell Device

Abstract Transplantation of islet cells is an effective treatment for type 1 diabetes with critically labile metabolic control. However, during islet isolation, blood supply is disrupted, and the transport of nutrients/metabolites to and from the islet cells occurs entirely by diffusion. Adequate oxygen supply is essential for function/survival of islet cells and is the limiting factor for graft integrity. Recently, we developed an immunoisolated chamber system for transplantation of human islets without immunosuppression. This system depended on daily oxygen supply. To provide independence from this external source, we incorporated a novel approach based on photosynthetically-generated oxygen. The chamber system was packed sandwich-like with a slab of immobilized photosynthetically active microorganisms (Synechococcus lividus) on top of a flat light source (LEDs, red light at 660 nm, intensity of 8 μE/m2/s). Islet cells immobilized in an alginate slab (500–1 000 islet equivalents/cm2) were mounted on the photosynthetic slab separated by a gas permeable silicone rubber-Teflon membrane, and the complete module was sealed with a microporous polytetrafluorethylene (Teflon) membrane (pore size: 0.4 μm) to protect the contents from the host immune cells. Upon illumination, oxygen produced by photosynthesis diffused via the silicone Teflon membrane into the islet compartment. Oxygen production from implanted encapsulated microorganisms was stable for 1 month. After implantation of the device into diabetic rats, normoglycemia was achieved for 1 week. Upon retrieval of the device, blood glucose levels returned to the diabetic state. Our results demonstrate that an implanted photosynthetic bioreactor can supply oxygen to transplanted islets and thus maintain islet viability/functionality.
Multi-scale phase-based local features

Local feature methods suitable for image feature based object recognition and for the estimation of motion and structure are composed of two steps, namely the 'where' and 'what' steps. The 'where' step (e.g., interest point detector) must select image points that are robustly localizable under common image deformations and whose neighborhoods are relatively informative. The 'what' step (e.g., local feature extractor) then provides a representation of the image neighborhood that is semi-invariant to image deformations, but distinctive enough to provide model identification. We present a quantitative evaluation of both the 'where' and the 'what' steps for three recent local feature methods: a) phase-based local features (Carneiro and Jepson, 2002), b) differential invariants (Schmid and Mohr, 1997), and c) the scale invariant feature transform (SIFT) (Lowe, 1999). Moreover, in order to make the phase-based approach more comparable to the other two approaches, we also introduce a new form of multi-scale interest point detector to be used for its 'where' step. The results show that the phase-based local features lead to better performance than the other two approaches when dealing with common illumination changes, 2D rotation, and sub-pixel translation. On the other hand, the phase-based local features are somewhat more sensitive to scale and large shear changes than the other two methods. Finally, we demonstrate the viability of the phase-based local feature in a simple object recognition system.
Characterization of Flip Chip Bump Failure Mode by using High Frequency 230MHz MP and CP4 Transducer

Acoustic Micro Imaging has been used over the past decade as a successful nondestructive technique to detect discontinuities within materials and interconnects. This paper focused on the capability of this nondestructive tool in detecting defect bumps by using high frequency 230 MHz MP and CP4 transducer. Characterization work was done on both the transducer and it was found that the MP acoustic image was able to give a clearer indication of defect bumps than CP4 but has edge effect limitation. The CP4 acoustic image however was able to give a clear assessment of the edge bumps but was unable to highlight the defect bump clearly. Therefore in this study, defect bumps identification was established between CP4 and MP acoustic image to have full capability for assessing the bumps integrity. All the suspected defect bumps identified from acoustic image was then verified with physical cross sectioning and followed by standard optical scoped and scanning electron microscopy (SEM) to inspect the failing interfaces. These results demonstrate the capabilities of micro imaging as a nondestructive tool to capture defect bumps in early stages of assembly. This application also helps failure analysis to eliminate the need to destruct the unit for physical cross sectioning and reduce analysis cycle time.
Computational Methods in Ice Sheet Modeling for Next-Generation Climate Simulations

: In its fourth assessment report (AR4), the Intergovernmental Panel on Climate Change (IPCC) declined to include estimates of future sea-level rise from ice sheet dynamics due to the inability of ice sheet models to mimic or explain observed dynamic behaviors, such as the acceleration and thinning then occurring on several of Greenland’s large outlet glaciers [1]. In recent years, there has been a push to develop “next generation” community-supported ice sheet models and codes: codes that (1) are able to perform realistic, high-resolution, continental scale simulations, (2) are robust, efficient and scalable on high-performance hybrid (CPU+GPU) architectures, and (3) possess built-in advanced analysis capabilities (e.g., sensitivity analysis, optimization, uncertainty quantification), in particular, the capability to provide uncertainty ranges for future sea-level rise due to ice sheet mass loss. This talk will give an overview of the Albany/FELIX (Finite Elements for Land Ice eXperiments) [5] next-generation land-ice dynamical core (dycore) that is currently under development at Sandia National Laboratories as a part of the PISCEES (Predicting Ice Sheet and Climate Evolution at Extreme Scales) project. This dycore is currently being integrated into the U.S. Department of Energy’s (DOE’s) new global Earth System Model (ESM), which will be used to calculate anticipated sea-level rise predictions during the twenty-first century, including uncertainty bounds. It is widely accepted that ice sheets behave like an incompressible non-Newtonian fluid, modeled by the Stokes equations with a nonlinear viscosity. The Albany/FELIX dycore implements an approximation to these equations, known as the “First-Order Stokes” model [2], an attractive alternative to the full Stokes model because of its reduced computational cost. Following a description of the PISCEES project, the First-Order Stokes model for ice sheets and its implementation within the Albany [4] code base using Trilinos [3] components will be detailed. Also discussed will be the coupling of Albany/FELIX to other land ice dycores, which enables dynamic simulations of ice sheet evolution, and facilitates the integration of the Albany/FELIX
Speaker recognition via sparse representations using orthogonal matching pursuit

The objective of this paper is to demonstrate the effectiveness of sparse representation techniques for speaker recognition. In this approach, each feature vector from unknown utterance is expressed as linear weighted sum of a dictionary of feature vectors belonging to many speakers. The weights associated with feature vectors in the dictionary are evaluated using orthogonal matching pursuit algorithm, which is a greedy approximation to l0 optimization. The weights thus obtained exhibit high level of sparsity, and only a few of them will have nonzero values. The feature vectors which belong to the correct speaker carry significant weights. The proposed method gives an equal error rate (EER) of 10.84% on NIST-2003 database, whereas the existing GMM-UBM system gives an EER of 9.67%. By combining evidence from both the systems an EER of 8.15% is achieved, indicating that both the systems carry complimentary information.
Medication error in anaesthesia and critical care: A cause for concern

Medication error is a major cause of morbidity and mortality in medical profession, and anaesthesia and critical care are no exception to it. Man, medicine, machine and modus operandi are the main contributory factors to it. In this review, incidence, types, risk factors and preventive measures of the medication errors are discussed in detail.
Human recombinant domain antibodies against multiple sclerosis antigenic peptide CSF114(Glc)

Multiple sclerosis (MS) is a chronic auto‐immune disease characterized by a damage to the myelin component of the central nervous system. Self‐antigens created by aberrant glycosylation have been described to be a key component in the formation of auto‐antibodies. CSF114(Glc) is a synthetic glucopeptide detecting in vitro MS‐specific auto‐antibodies, and it is actively used in diagnostics and research to monitor and quantify MS‐associated Ig levels. We reasoned that antibodies raised against this probe could have been relevant for MS. We therefore screened a human Domain Antibody library against CSF114(Glc) using magnetic separation as a panning method. We obtained and described several clones, and the one with the highest signals was produced as a 6×His‐tagged protein to properly study the binding properties as a soluble antibody. By surface plasmon resonance measurements, we evidenced that our clone recognized CSF114(Glc) with high affinity and specific for the glucosylated peptide. Kinetic parameters of peptide–clone interaction were calculated obtaining a value of KD in the nanomolar range. Harboring a human framework, this antibody should be very well tolerated by human immune system and may represent a valuable tool for MS diagnosis and therapy, paving the way to new research strategies. Copyright © 2014 John Wiley & Sons, Ltd.
Modeling Supravalvular Aortic Stenosis Syndrome With Human Induced Pluripotent Stem Cells

Background— Supravalvular aortic stenosis (SVAS) is caused by mutations in the elastin (ELN) gene and is characterized by abnormal proliferation of vascular smooth muscle cells (SMCs) that can lead to narrowing or blockage of the ascending aorta and other arterial vessels. Having patient-specific SMCs available may facilitate the study of disease mechanisms and development of novel therapeutic interventions. Methods and Results— Here, we report the development of a human induced pluripotent stem cell (iPSC) line from a patient with SVAS caused by the premature termination in exon 10 of the ELN gene resulting from an exon 9 four-nucleotide insertion. We showed that SVAS iPSC-derived SMCs (iPSC-SMCs) had significantly fewer organized networks of smooth muscle &agr;-actin filament bundles, a hallmark of mature contractile SMCs, compared with control iPSC-SMCs. The addition of elastin recombinant protein or enhancement of small GTPase RhoA signaling was able to rescue the formation of smooth muscle &agr;-actin filament bundles in SVAS iPSC-SMCs. Cell counts and BrdU analysis revealed a significantly higher proliferation rate in SVAS iPSC-SMCs than control iPSC-SMCs. Furthermore, SVAS iPSC-SMCs migrated at a markedly higher rate to the chemotactic agent platelet-derived growth factor compared with the control iPSC-SMCs. We also provided evidence that elevated activity of extracellular signal-regulated kinase 1/2 is required for hyperproliferation of SVAS iPSC-SMCs. The phenotype was confirmed in iPSC-SMCs generated from a patient with deletion of elastin owing to Williams-Beuren syndrome. Conclusions— SVAS iPSC-SMCs recapitulate key pathological features of patients with SVAS and may provide a promising strategy to study disease mechanisms and to develop novel therapies.
Facilitating Stable Representations: Serial Dependence in Vision

We tested whether the intervening time between multiple glances influences the independence of the resulting visual percepts. Observers estimated how many dots were present in brief displays that repeated one, two, three, four, or a random number of trials later. Estimates made farther apart in time were more independent, and thus carried more information about the stimulus when combined. In addition, estimates from different visual field locations were more independent than estimates from the same location. Our results reveal a retinotopic serial dependence in visual numerosity estimates, which may be a mechanism for maintaining the continuity of visual perception in a noisy environment.
Antifungal Susceptibility Assessment of Innovative and Non-Conventional Lime Mortars Incorporating Almond-Shell Powder Bio-Waste Subjected to Particle-Dispersion Technique

A favorable environment for fungi colonization in building materials’ surfaces can emerge when certain hygrothermal conditions occur. Thus, reducing fungal growth susceptibility is of major interest. Furthermore, if the integration of bio-wastes is performed in parallel with the development of innovative materials for this purpose, a more sustainable and environmentally friendly material can be obtained. In this study, the fungal susceptibility of lime mortars incorporating almond-shell powder (ASP) microparticles (2 and 4%, wt.–wt. in relation to the binder content) was evaluated. The particle-dispersion technique was employed to prepare the bio-waste introduced in the mixtures. The fungal susceptibility of ASP samples was compared with nanotitania (n-TiO2) with recognized antifungal properties. Mechanical strength, water absorption, and wettability tests were also performed for a better characterization of the composites. Although the addition of 2% ASP led to mechanical properties reduction, an increase in the compressive and flexural strength resulted for 4% of the ASP content. Difficulties in fungal growth were observed for the samples incorporating ASP. No fungal development was detected in the mortar with 2% of ASP, which may be correlated with an increase in the surface hydrophobic behavior. Furthermore, mortars with ASP revealed a reduction in water absorption by capillarity ability, especially with 4% content, suggesting changes in the microstructure and pore characteristics. The results also demonstrated that an improvement in the physical and mechanical properties of the lime mortars can be achieved when ASP microparticles are previously subjected to dispersion techniques.
Exploiting Application-Level Information to Reduce Memory Bandwidth Consumption

As processors continue to deliver higher levels of performance and as memory latency tolerance techniques become widespread to address the increasing cost of accessing memory, memory bandwidth will emerge as a major performance bottleneck. Rather than rely solely on wider and faster memories to address memory bandwidth shortages, an alternative is to use existing memory bandwidth more eciently. A promising approach is hardware-based selective subblocking [12, 1]. In this technique, hardware predictors track the portions of cache blocks that are referenced by the processor. On a cache miss, the predictors are consulted and only previously referenced portions are fetched into the cache, thus conserving memory bandwidth. This paper proposes a software-centric (and hence more complexity-eective) approch to selective subblocking. We make the key observation that wasteful data fetching inside long cache blocks arises due to certain sparse memory references, and that such memory references can be identied in the application source code. Rather than use hardware predictors to discover sparse memory reference patterns from the dynamic memory reference stream, our approach relies on the programmer or compiler to identify the sparse memory references statically, and to use special annotated memory instructions to specify the amount of spatial reuse associated with such memory references. At runtime, the size annotations select the amount of data to fetch on each cache miss, thus fetching only data that will likely be accessed by the processor. Our results show annotated memory instructions remove between 54% and 71% of cache trac for 7 applications, reducing more trac than hardware selective sub-blocking using a 32 Kbyte predictor on all applications, and reducing as much trac as hardware selective sub-blocking using an 8 Mbyte predictor on 5 out of 7 applications. Overall, annotated memory instructions achieve a 17% performance gain when used alone, and a 22.3% performance gain when combined with software prefetching, compared to a 7.2% performance degradation when prefetching without annotated memory instructions.
Welfare state regimes, unemployment and health: a comparative study of the relationship between unemployment and self-reported health in 23 European countries

Background: The relationship between unemployment and increased risk of morbidity and mortality is well established. However, what is less clear is whether this relationship varies between welfare states with differing levels of social protection for the unemployed. Methods: The first (2002) and second (2004) waves of the representative cross-sectional European Social Survey (37 499 respondents, aged 25–60 years). Employment status was main activity in the last 7 days. Health variables were self-reported limiting long-standing illness (LI) and fair/poor general health (PH). Data are for 23 European countries classified into five welfare state regimes (Scandinavian, Anglo-Saxon, Bismarckian, Southern and Eastern). Results: In all countries, unemployed people reported higher rates of poor health (LI, PH or both) than those in employment. There were also clear differences by welfare state regime: relative inequalities were largest in the Anglo-Saxon, Bismarckian and Scandinavian regimes. The negative health effect of unemployment was particularly strong for women, especially within the Anglo-Saxon (ORLI 2.73 and ORPH 2.78) and Scandinavian (ORLI 2.28 and ORPH 2.99) welfare state regimes. Discussion: The negative relationship between unemployment and health is consistent across Europe but varies by welfare state regime, suggesting that levels of social protection may indeed have a moderating influence. The especially strong negative relationship among women may well be because unemployed women are likely to receive lower than average wage replacement rates. Policy-makers’ attention therefore needs to be paid to income maintenance, and especially the extent to which the welfare state is able to support the needs of an increasingly feminised European workforce.
Character Recognition Using Neural Network

In the present paper, we are use the neural network to recognize the character. In this paper it is developed 0ff-line strategies for the isolated handwritten English character (A TO Z) and (0 to 9) . This method improves the character recognition method. Preprocessing of the Character is used binarization, thresolding and segmentation method . The proposed method is based on the use of feed forward back propagation method to classify the characters. The ANN is trained using the Back Propagation algorithm. In the proposed system, English nue-merical letter is represented by binary numbers that are used as input then they are fed to an ANN. Neural network followed by the Back Propagation Algorithm which compromises Training. Index Terms—Neural network, back propagation method Segmentation, image processing toolbox ,matlab .
Single-molecule spectroscopy reveals photosynthetic LH2 complexes switch between emissive states

Photosynthetic organisms flourish under low light intensities by converting photoenergy to chemical energy with near unity quantum efficiency and under high light intensities by safely dissipating excess photoenergy and deleterious photoproducts. The molecular mechanisms balancing these two functions remain incompletely described. One critical barrier to characterizing the mechanisms responsible for these processes is that they occur within proteins whose excited-state properties vary drastically among individual proteins and even within a single protein over time. In ensemble measurements, these excited-state properties appear only as the average value. To overcome this averaging, we investigate the purple bacterial antenna protein light harvesting complex 2 (LH2) from Rhodopseudomonas acidophila at the single-protein level. We use a room-temperature, single-molecule technique, the anti-Brownian electrokinetic trap, to study LH2 in a solution-phase (nonperturbative) environment. By performing simultaneous measurements of fluorescence intensity, lifetime, and spectra of single LH2 complexes, we identify three distinct states and observe transitions occurring among them on a timescale of seconds. Our results reveal that LH2 complexes undergo photoactivated switching to a quenched state, likely by a conformational change, and thermally revert to the ground state. This is a previously unobserved, reversible quenching pathway, and is one mechanism through which photosynthetic organisms can adapt to changes in light intensities.
Human Brain Microvascular Endothelial Cells and Umbilical Vein Endothelial Cells Differentially Facilitate Leukocyte Recruitment and Utilize Chemokines for T Cell Migration

Endothelial cells that functionally express blood brain barrier (BBB) properties are useful surrogates for studying leukocyte-endothelial cell interactions at the BBB. In this study, we compared two different endothelial cellular models: transfected human brain microvascular endothelial cells (THBMECs) and human umbilical vein endothelial cells (HUVECs). With each grow under optimal conditions, confluent THBMEC cultures showed continuous occludin and ZO-1 immunoreactivity, while HUVEC cultures exhibited punctate ZO-1 expression at sites of cell-cell contact only. Confluent THBMEC cultures on 24-well collagen-coated transwell inserts had significantly higher transendothelial electrical resistance (TEER) and lower solute permeability than HUVECs. Confluent THBMECs were more restrictive for mononuclear cell migration than HUVECs. Only THBMECs utilized abluminal CCL5 to facilitate T-lymphocyte migration in vitro although both THBMECs and HUVECs employed CCL3 to facilitate T cell migration. These data establish baseline conditions for using THBMECs to develop in vitro BBB models for studying leukocyte-endothelial interactions during neuroinflammation.