gebhart/patrepeval huggingface dataset:
Chimeric Helper-Dependent Adenoviruses Transduce Retinal Ganglion Cells and Müller Cells in Human Retinal Explants (2021)

<b><i>Purpose:</i></b> Despite numerous recent advances in retinal gene therapy using adeno-associated viruses (AAVs) as delivery vectors, there remains a crucial need to identify viral vectors with the ability to transduce specific retinal cell types and that have a larger carrying capacity than AAV. In this study, we evaluate the retinal tropism of 2 chimeric helper-dependent adenoviruses (HDAds), helper-dependent adenovirus serotype 5 (HDAd5)/3 and HDAd5/35, both <i>ex vivo</i> using human retinal explants and <i>in vivo</i> using rats. <b><i>Methods:</i></b> We transduced cultured human retinal explants with HDAd5/3 and HDAd5/35 carrying an eGFP vector and evaluated tropism and transduction efficiency using immunohistochemistry. To assess <i>in vivo</i> transduction efficiency, subretinal injections were performed in wild-type Sprague-Dawley rats. For both explants and subretinal injections, we delivered 10 μL (1 × 10<sup>6</sup> vector genomes/mL) and assessed tropism at 7- and 14-days post-transduction, respectively. <b><i>Results:</i></b> HDAd5/3 and HDAd5/35 both transduced human retinal ganglion cells (RGCs) and Müller cells, but not photoreceptors, in human retinal explants. However, subretinal injections in albino rats resulted in transduction of the retinal pigmented epithelium only, highlighting species-specific differences in retinal tropism and the value of a human explant model when testing vectors for eventual human gene therapy. <b><i>Conclusions:</i></b> Chimeric HDAds are promising candidates for the delivery of large genes, multiple genes, or neuroprotective factors to Müller cells and RGCs. These vectors may have utility for targeted therapy of neurodegenerative diseases primarily involving retinal ganglion or Müller cell types, such as glaucoma or macular telangiectasia type 2.
Fundamental Study on the Segmented Flow Injection - Multiphase Flow Formation Towards Microchip-Based Multi-Ion Sensing (2003)

A new fluid flow inside the microchannel was successfully developed. The flow created here involves segmented flow injection of plural organic phases into a microchannel followed by contact with a single aqueous phase to form stable organic-aqueous two-layer flow inside the microchannel. Fundamental study on the developed flow inside the microchannel was performed by monitoring the dye-doped segmented organic phases by thermal lens microscopy (TLM). Excellent repeatability and very small injection volume in developing segmented flow were realized. The new fluid flow created here is expected to allow us multi-ion sensing, which is not easily demonstrated by conventional ion sensor technology using a solvent polymeric membrane, by combining with neutral ionophore-based ion pair extraction using plural numbers of organic phases containing different ionophore molecules.
Development of a Composite Measure of Product Adherence, Protocol Compliance, and Semen Exposure Using DNA and Protein Biomarkers for Topical HIV Prevention Studies (2014)

Utilizing a composite, triple marker based panel of DNA, protein, and drug present on the surface of returned vaginal gel applicators, it is possible to determine, objectively and non-invasively, product adherence, protocol compliance, and semen exposure in microbicide trials.
Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates (2019)

Differential voltage analysis (DVA) is a conventional approach for estimating capacity degradation in batteries. During charging, a graphite electrode goes through several phase transitions observed as plateaus in the voltage response. The transitions between these plateaus emerge as observable peaks in the differential voltage. The DVA method utilizes these peaks for estimating cell degradation. Unfortunately, at higher C-rates (above C/2) the peaks flatten and become unobservable. In this work, we show that, unlike the differential voltage, the peaks in the 2nd derivative of the expansion with respect to capacity remain observable up to 1C and thus make possible diagnostic algorithms at these charging rates. To understand why that is the case, we have developed an electrochemical and expansion model suitable for model-based estimation. In particular, we demonstrate that the single particle modeling methodology is not able to capture the peak smoothing effect, therefore a multi-particle approach for the graphite electrode is needed. Additionally, model parameters are identified using experimental data from a graphite/NMC pouch cell. The proposed model produces an excellent fit for the observed electric and mechanical swelling response of the cells and could enable physics-based data-driven degradation studies at practical charging rates.
Streptococcus suis, an important pig pathogen and emerging zoonotic agent—an update on the worldwide distribution based on serotyping and sequence typing (2014)

Streptococcus suis is an important pathogen causing economic problems in the pig industry. Moreover, it is a zoonotic agent causing severe infections to people in close contact with infected pigs or pork-derived products. Although considered sporadic in the past, human S. suis infections have been reported during the last 45 years, with two large outbreaks recorded in China. In fact, the number of reported human cases has significantly increased in recent years. In this review, we present the worldwide distribution of serotypes and sequence types (STs), as determined by multilocus sequence typing, for pigs (between 2002 and 2013) and humans (between 1968 and 2013). The methods employed for S. suis identification and typing, the current epidemiological knowledge regarding serotypes and STs and the zoonotic potential of S. suis are discussed. Increased awareness of S. suis in both human and veterinary diagnostic laboratories and further establishment of typing methods will contribute to our knowledge of this pathogen, especially in regions where complete and/or recent data is lacking. More research is required to understand differences in virulence that occur among S. suis strains and if these differences can be associated with specific serotypes or STs.
USE OF COAL DRYING TO REDUCE WATER CONSUMED IN PULVERIZED COAL POWER PLANTS (2005)

the superficial air velocity ranging from 0.2 m/s to 1.4 m/s. Drying rate is shown to be a strong function of air velocity, increasing seven-fold from 0.2 m/s to 1.4 m/s. Increases in velocity from 0.75 m/s (minimum fluidization velocity) to 1.4 m/s resulted in a doubling of the drying rate.
Multiwave computing circuits using integrated opto-electronic devices (2002)

VLSI technology provides the means for implementing parallel systems with large numbers of computing elements. However, the high cost of communication in area, time, and power, relative to that of logic and storage, constrains the design and engineering of large-scale tightly-coupled systems. The problem severity increases as die size increases, and is at its worst in wafer-scale integration (WSI) and multichip module (MCM) packaging. Multiwave optical computing, where discrete wavelengths are employed as multiplexable information carriers, presents an interesting solution to the communication crisis in next-generation integrated systems. A computer architecture using multiwavelength opto-electronic integrated circuits (multiwave OEICs) provides the wavelength space as an extra dimension of freedom for parallel processing. A key feature is that several independent computations can be performed in a single optical circuit using wavelength space, as if it were several computing circuits operating in parallel.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>
Glycosylation of neuraminidase determines the neurovirulence of influenza A/WSN/33 virus (1993)

The neuraminidase (NA) gene of influenza A/WSN/33 (WSN) virus has previously been shown to be associated with neurovirulence in mice and growth in Madin-Darby bovine kidney (MDBK) cells. Nucleotide sequence analysis has indicated that the NA of WSN virus lacks a conserved glycosylation site at position 130 (corresponding to position 146 in the N2 subtype). To investigate the role of this carbohydrate in viral pathogenicity, we used reverse genetics methods to generate a Glyc+ mutant virus, in which the glycosylation site Asn-130 was introduced into the WSN virus NA. Unlike the wild-type WSN virus, the Glyc+ mutant virus did not undergo multicycle replication in MDBK cells in the absence of trypsin, presumably because of lack of cleavage activation of infectivity. In contrast, revertant viruses derived from the Glyc+ mutant were able to replicate in MDBK cells without exogenous protease. Nucleotide sequence analysis revealed that the NAs of the revertant viruses had lost the introduced glycosylation site. In contrast to wild-type and revertant viruses, the Glyc+ mutant virus was not able to multiply in mouse brain. These results suggest that the absence of a glycosylation site at position 130 of the NA plays a key role in the neurovirulence of WSN virus in mice.
Efficient GPU-based training of recurrent neural network language models using spliced sentence bunch (2014)

Copyright © 2014 ISCA. Recurrent neural network language models (RNNLMs) are becoming increasingly popular for a range of applications including speech recognition. However, an important issue that limits the quantity of data, and hence their possible application areas, is the computational cost in training. A standard approach to handle this problem is to use class-based outputs, allowing systems to be trained on CPUs. This paper describes an alternative approach that allows RNNLMs to be efficiently trained on GPUs. This enables larger quantities of data to be used, and networks with an unclustered, full output layer to be trained. To improve efficiency on GPUs, multiple sentences are spliced together for each mini-batch or bunch in training. On a large vocabulary conversational telephone speech recognition task, the training time was reduced by a factor of 27 over the standard CPU-based RNNLM toolkit. The use of an unclustered, full output layer also improves perplexity and recognition performance over class-based RNNLMs.
ChemInform Abstract: 6‐ALKYLBENZOXAZOLINONES. CHEMICAL AND PHARMACODYNAMIC STUDIES (1985)

Abstract 6‐Acyl‐benzoxazolinone (I) können mittels Clemmensen‐Reduktion nur teilweise, mittels Reduktion mit Triethylsilan/Trifluoressigsäure aber in guten Ausbeuten in die 6‐Alkyl‐benzoxazolinone (II) übergeführt werden.
S-parameter Modeling and Optimization using Deep Gaussian Processes (2022)

In this work, a new methodology based on deep Gaussian processes (DGP) is proposed for the modeling and optimization of the S-parameter response of a microwave device. The DGP is used as a surrogate model to directly predict the magnitude (or phase) of the S-parameter as a function over the frequency and over the design parameters of the device. Subsequently an objective probability distribution is retrieved and maximized in a Bayesian optimization (BO) scheme. The new strategy overcomes the limitation of the standard Bayesian optimization that employs an objective function model: simple objective functions are easy to model but may lead to sub-optimal responses, while complicated objective functions may require more powerful and less efficient models. An adequate microwave example demonstrates the increased optimization accuracy of the proposed approach, comparing to standard BO.
Epsilon-Near-Zero Al-Doped ZnO for Ultrafast Switching at Telecom Wavelengths: Outpacing the Traditional Amplitude-Bandwidth Trade-Off (2015)

Transparent conducting oxides have recently gained great attention as CMOS-compatible materials for applications in nanophotonics due to their low optical loss, metal-like behavior, versatile/tailorable optical properties, and established fabrication procedures. In particular, aluminum doped zinc oxide (AZO) is very attractive because its dielectric permittivity can be engineered over a broad range in the near infrared and infrared. However, despite all these beneficial features, the slow (&gt; 100 ps) electron-hole recombination time typical of these compounds still represents a fundamental limitation impeding ultrafast optical modulation. Here we report the first epsilon-near-zero AZO thin films which simultaneously exhibit ultra-fast carrier dynamics (excitation and recombination time below 1 ps) and an outstanding reflectance modulation up to 40% for very low pump fluence levels (&lt; 4 mJ/cm2) at the telecom wavelength of 1.3 μm. The unique properties of the demonstrated AZO thin films are the result of a low temperature fabrication procedure promoting oxygen vacancies and an ultra-high carrier concentration. As a proof-of-concept, an all-optical AZO-based plasmonic modulator achieving 3 dB modulation in 7.5 μm and operating at THz frequencies is numerically demonstrated. Our results overcome the traditional "modulation depth vs. speed" trade-off by at least an order of magnitude, placing AZO among the most promising compounds for tunable/switchable nanophotonics.
Wet spinning of continuous polymer-free carbon-nanotube fibers with high electrical conductivity and strength (2016)

Abstract We report on the fabrication of polymer-free carbon nanotube (CNT) fibers by a novel wet spinning method combined with a very easy and straightforward fabrication process. These fibers exhibited high electrical conductivity (14,284 ± 169 S·cm −1 ) and tensile strength (887 ± 37 MPa). Such high performance was made possible by the preparation of free-standing CNT fibers from a surfactant solution containing uniformly dispersed CNTs, despite the use of an organic coagulating solvent and subsequent stretching to align the CNTs in the fiber.
Capturability-based analysis and control of legged locomotion, Part 1: Theory and application to three simple gait models (2012)

This two-part paper discusses the analysis and control of legged locomotion in terms of N-step capturability: the ability of a legged system to come to a stop without falling by taking N or fewer steps. We consider this ability to be crucial to legged locomotion and a useful, yet not overly restrictive criterion for stability. In this part (Part 1), we introduce a theoretical framework for assessing N-step capturability. This framework is used to analyze three simple models of legged locomotion. All three models are based on the 3D Linear Inverted Pendulum Model. The first model relies solely on a point foot step location to maintain balance, the second model adds a finite-sized foot, and the third model enables the use of centroidal angular momentum by adding a reaction mass. We analyze how these mechanisms influence N-step capturability, for any N &gt; 0. Part 2 will show that these results can be used to control a humanoid robot.
Integrins Form an Expanding Diffusional Barrier that Coordinates Phagocytosis (2020)

Phagocytosis is initiated by lateral clustering of receptors, which in turn activates Src-family kinases (SFKs). Activation of SFKs requires depletion of tyrosine phosphatases from the area of particle engagement. We investigated how the major phosphatase CD45 is excluded from contact sites, using single-molecule tracking. The mobility of CD45 increased markedly upon engagement of Fcγ receptors. While individual CD45 molecules moved randomly, they were displaced from the advancing phagocytic cup by an expanding diffusional barrier. By micropatterning IgG, the ligand of Fcγ receptors, we found that the barrier extended well beyond the perimeter of the receptor-ligand engagement zone. Second messengers generated by Fcγ receptors activated integrins, which formed an actin-tethered diffusion barrier that excluded CD45. The expanding integrin wave facilitates the “zippering” of Fcγ receptors onto the target and integrates the information from sparse receptor-ligand complexes, coordinating the progression and ultimate closure of the phagocytic cup.
A performance and energy comparison of FPGAs, GPUs, and multicores for sliding-window applications (2012)

With the emergence of accelerator devices such as multicores, graphics-processing units (GPUs), and field-programmable gate arrays (FPGAs), application designers are confronted with the problem of searching a huge design space that has been shown to have widely varying performance and energy metrics for different accelerators, different application domains, and different use cases. To address this problem, numerous studies have evaluated specific applications across different accelerators. In this paper, we analyze an important domain of applications, referred to as sliding-window applications, when executing on FPGAs, GPUs, and multicores. For each device, we present optimization strategies and analyze use cases where each device is most effective. The results show that FPGAs can achieve speedup of up to 11x and 57x compared to GPUs and multicores, respectively, while also using orders of magnitude less energy.
Improvement of the Cycling Performance of LiNi0.6Co0.2Mn0.2O2 Cathode Active Materials by a Dual-Conductive Polymer Coating (2014)

LiNi0.6Co0.2Mn0.2O2 cathode materials were surface-modified by coating with a dual conductive poly(3,4-ethylenedioxythiophene)-co-poly(ethylene glycol) (PEDOT-co-PEG) copolymer, and their resulting electrochemical properties were investigated. The surface-modified LiNi0.6Co0.2Mn0.2O2 cathode material exhibited a high discharge capacity and good high rate performance due to enhanced transport of Li(+) ions as well as electrons. The presence of a protective conducting polymer layer formed on the cathode also suppressed the growth of a resistive layer and inhibited the dissolution of transition metals from the active cathode materials, which resulted in more stable cycling characteristics than the pristine LiNi0.6Co0.2Mn0.2O2 cathode material at 55 (o)C.
Quasi-Passive and Reconfigurable Node for Optical Access Network (2010)

We propose a quasi-passive reconfigurable power/wavelength distribution device for optical access network. It can adapt to deployment condition and optimize the energy consumption. Novel tri-state non-volatile optical switches are designed in this device to preserve the semi-passive operation.
Purification of a Variant-specific Surface Protein of Giardia lamblia and Characterization of its Metal-binding Properties (1995)

Giardia lamblia, an intestinal parasite of humans and other vertebrates, undergoes surface antigenic variation by modulating the expression of different variant-specific surface proteins (VSP). VSPs are cysteine-rich surface proteins that bind zinc and other heavy metals in vitro. We developed an immunoaffinity chromatographic method to purify a VSP in order to determine its biochemical properties. The sequences of two different proteolytic fragments agreed with the sequence deduced from the cloned gene, and amino-terminal sequence indicated the removal of a 14-residue signal peptide, consistent with the transport of VSP to the cell surface. The protein is not glycosylated and has an isoelectric point of 5.3. X-ray microanalyses indicated that the major metals in Giardia trophozoites, as well as purified VSP, are zinc and iron. The zinc concentration in Giardia cells was found to be 0.43 mM and the iron concentration 0.80 mM when compared with standard samples (zinc) or calculated from a known physical constants (iron). We propose that metal coordination stabilizes VSPs, rendering them resistant to proteolytic attack in the upper small intestine. Moreover, the ability to bind ions by Giardia may play a role in nutritional deficiency and/or malabsorption in heavily infected persons.
Analysis of Partial Power DC–DC Converters for Two-Stage Photovoltaic Systems (2018)

Two-stage photovoltaic (PV) configurations have become increasingly popular due to the decoupling between the inverter dc-link voltage and the PV voltage, adding flexibility to extend the maximum power point tracking range. However, the additional dc-dc converter increases the power converter losses. The concept of partial power converters (PPCs), which reduce the amount of power handled by the dc stage, can mitigate this effect. However, the type of topology, its power and voltage rating, efficiency, and an operating range can vary significantly depending on the function (boosting or reducing voltage) and type of PV application and scale (micro-, string-, or multi-sting inverter). This paper analyzes the possible configuration of connections of PPC depending on the application and scale of the PV system and introduces a new buck-type PPC. Three solutions for practical PV systems are further elaborated, including experimental validation. Results show that the PPC concept greatly improves the overall PV system efficiency with the added benefit that the dc-dc stage power ratings achieved are only a fraction of the PV system, reducing size and cost of the power converter without affecting the system performance.
Generalized Pose-and-Scale Estimation using 4-Point Congruence\n Constraints (2020)

We present gP4Pc, a new method for computing the absolute pose of a\ngeneralized camera with unknown internal scale from four corresponding 3D\npoint-and-ray pairs. Unlike most pose-and-scale methods, gP4Pc is based on\nconstraints arising from the congruence of shapes defined by two sets of four\npoints related by an unknown similarity transformation. By choosing a novel\nparametrization for the problem, we derive a system of four quadratic equations\nin four scalar variables. The variables represent the distances of 3D points\nalong the rays from the camera centers. After solving this system via Groebner\nbasis-based automatic polynomial solvers, we compute the similarity\ntransformation using an efficient 3D point-point alignment method. We also\npropose a specialized variant of our solver for the case of coplanar points,\nwhich is computationally very efficient and about 3x faster than the fastest\nexisting solver. Our experiments on real and synthetic datasets, demonstrate\nthat gP4Pc is among the fastest methods in terms of total running time when\nused within a RANSAC framework, while achieving competitive numerical\nstability, accuracy, and robustness to noise.\n
CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data (2021)

Learning temporal patterns from multivariate longitudinal data is challenging especially in cases when data is sporadic, as often seen in, e.g., healthcare applications where the data can suffer from irregularity and asynchronicity as the time between consecutive data points can vary across features and samples, hindering the application of existing deep learning models that are constructed for complete, evenly spaced data with fixed sequence lengths. In this paper, a novel deep learning-based model is developed for modeling multiple temporal features in sporadic data using an integrated deep learning architecture based on a recurrent neural network (RNN) unit and a continuous-time autoregressive (CAR) model. The proposed model, called CARRNN, uses a generalized discrete-time autoregressive model that is trainable end-to-end using neural networks modulated by time lags to describe the changes caused by the irregularity and asynchronicity. It is applied to multivariate time-series regression tasks using data provided for Alzheimer's disease progression modeling and intensive care unit (ICU) mortality rate prediction, where the proposed model based on a gated recurrent unit (GRU) achieves the lowest prediction errors among the proposed RNN-based models and state-of-the-art methods using GRUs and long short-term memory (LSTM) networks in their architecture.