gebhart/patrepeval huggingface dataset:
Genetic variability in HIV-1 gp120 affects interactions with HLA molecules and T cell receptor. (1990)

The propensity of HIV-1 to undergo sequence variation, particularly in the envelope glycoprotein gp120, complicates vaccine development and may enable the virus to evade ongoing immune responses in infected individuals. We present here a molecular analysis of the effects of this variability on human T cell recognition of HIV-1 gp120. Synthetic peptides representing a defined CD4+ human T cell epitope in gp120 were used to survey gp120 molecules from various HIV-1 strains for the capacity to be recognized in the context of a single human MHC molecule, DR4. Variation affected recognition at two levels. For some strains, variation in this epitope was sufficient to alter the interaction of Ag receptors on gp120-specific human T cell clones with peptide-DR4 complexes on APC. In the case of two strains, the natural variation was sufficient to prevent the critical initial interaction between the relevant gp120 peptides and DR4 on the APC. However, these strains were highly divergent from the reference strain. Thus it is encouraging to note that the range of natural sequence variation in this T cell epitope falls, for the most part, within the range of peptide sequences that can be accommodated by the relevant human MHC molecule.
Adaptation of deep neural network acoustic models using factorised i-vectors (2014)

The use of deep neural networks (DNNs) in a hybrid configuration is becoming increasingly popular and successful for speech recognition. One issue with these systems is how to efficiently adapt them to reflect an individual speaker or noise condition. Recently speaker i-vectors have been successfully used as an additional input feature for unsupervised speaker adaptation. In this work the use of i-vectors for adaptation is extended to incorporate acoustic factorisation. In particular, separate i-vectors are computed to represent speaker and acoustic environment. By ensuring orthogonality between the individual factor representations it is possible to represent a wide range of speaker and environment pairs by simply combining i-vectors from a particular speaker and a particular environment. In this paper the i-vectors are viewed as the weights of a cluster adaptive training (CAT) system, where the underlying models are GMMs rather than HMMs. This allows the factorisation approaches developed for CAT to be directly applied. Initial experiments were conducted on a noise distorted version of the WSJ corpus. Compared to standard speaker-based i-vector adaptation, factorised i-vectors showed performance gains.
Highly efficient quasi-cw Nd:YAG laser side-pumped in a diffusive cavity by P-polarized stacked diode beams (2000)

We have demonstrated a highly efficient quasi-cw Nd:YAG laser with a novel side-pumping configuration using micro-lens free stacked-diode-bars. In this configuration, the fast-axis of the diode bars is arranged in parallel to the rod axis which means highly efficient P-polarization pumping of the Nd:YAG rod. The pumping beams are coupled into a cylindrical diffusive reflector by using wedge lenses (1-dimensional lens duct). The pump radiation in the slow axis direction is focused by the cylindrical surface of the wedge lens, and the radiation in the fast axis direction is transferred by the total internal reflection. Six micro-lens free diode bars are arranged around the Nd:YAG rod. The transfer efficiency of the wedge lens was 89%. Laser power of 270 W was obtained at the beam quality of 20 mm mrad at the electric efficiency of 18.4%. The experimental results are well explained by calculations and we consider further enhancement of the efficiency is possible by optimizing the diffusive cavity design.
Effects of quantum mechanical coupling on the optical properties of vertically stacked V-groove quantum wires (2000)

The effects of the quantum mechanical coupling on the optical properties of vertically stacked InGaAs/GaAs V-shaped quantum wires have been studied by means of photoluminescence and photoluminescence-excitation spectroscopy. The experimental results have been analyzed by a simple theoretical model based on an analytical procedure. We found that by decreasing the barrier thickness (Lb) between the wires, the vertical coupling induces a splitting of the single wire levels into symmetric and antisymmetric states characterized by a polarization anisotropy. Furthermore, a clear shift of the coupled levels and a narrowing of the spectral linewidth are observed with a decrease in Lb. These findings are consistent with the theoretical predictions.
Beyond the Petermann limit: can exceptional points increase sensor\n precision? (2020)

Experiments near the lock-in region in maximally dissipative non-Hermitian\nsystems, e.g., conventional laser gyroscopes near the deadband, have run up\nagainst the Petermann limit, where excess noise exactly cancels any\nscale-factor enhancement resulting in no overall enhancement in precision. As a\nresult, one might be tempted to conclude that exceptional points (EPs)\ngenerally cannot be used to increase the precision of laser sensors. Indeed,\nusing a linear eigenmode analysis we show that the Petermann limit applies not\njust to maximally dissipative systems, but for any type of EP, owing to the\nfact that EPs are rotationally invariant. It turns out, however, that this\nrestriction comes from the assumption of linearity. We find that nonlinearity\nbreaks the rotation symmetry such that the different types of EPs are no longer\nequivalent above threshold. In particular, for sufficiently high measurement\nfrequencies, EPs in conservatively coupled systems having a saturation\nimbalance can lead to an increase in the fundamental precision beyond the\nPetermann limit. Importantly, we find that only one mode lases under these\nconditions. We show that the beat note can be recovered by interference with an\nauxiliary mode, but that this has consequences for the quantum and classical\nnoise that depend on the recovery scheme. Thus, it remains to be seen whether\npractical experiments can be designed that can take advantage of this\nenhancement.\n
Ca<sup>2+</sup>Redistribution from Bound to Free Form Is Required for Tumor Necrosis Factor Actions in 30A5 Preadipocytes (1990)

Tumor necrosis factor/cachectin (TNF) inhibits differentiation of 30A5 preadipocytes into adipocytes. In this process, TNF inhibits the expression of the gene for acetyl-coenzyme-A carboxylase, the rate-limiting enzyme for biogenesis of long chain fatty acids. One of the early reactions caused by TNF is the Ca2+ redistribution of Ca2+ from the bound form to the free form. This Ca2+ redistribution results in a transient Ca2+ efflux. High concentrations of Mg2+ inhibit Ca2+ redistribution and efflux. This inhibition reverses the repression of acetyl-coenzyme-A carboxylase and reverses the TNF inhibition of the differentiation of 30A5 preadipocytes into adipocytes. This indicates that Ca2+ redistribution between the bound and the free form is an obligatory event in the sequence of actions caused by TNF in 30A5 cells.
Planning of Converged Optical Wireless Network and DC Infrastructures in Support of Mobile Cloud Services (2013)

This paper focuses on converged optical/wireless network and DC infrastructures supporting mobile Cloud services. Planning these infrastructures using stochastic optimization approaches and allowing virtual machine migration to address end-user mobility, facilitates significant resource efficiency improvement.
Author response: The alpha/B.1.1.7 SARS-CoV-2 variant exhibits significantly higher affinity for ACE-2 and requires lower inoculation doses to cause disease in K18-hACE2 mice (2021)

Functional characterization of the alpha/B1.1.7 SARS-CoV-2 variant revealed an eightfold affinity increase of the N501Y RBD to human ACE-2 and that even a low inoculation dose of the alpha variant induces severe disease and fast progression in transgenic hACE2 mice.
A Two-Tier Convolutional Neural Network for Combined Detection and Segmentation in Biological Imagery (2019)

Deep learning techniques have been useful for modern microscopy imaging techniques to further study and analyze biological structures and organs. Convolutional neural networks (CNN) have improved 2D object detection, localization, and segmentation. For imagery containing biological structures with depth, it is especially desirable to perform these tasks in 3D. Traditionally, performing these tasks simultaneously in 3D has proven to be computationally expensive. Currently available methodologies thus largely work to segment 3D objects from 2D images (without context from captured 3D volumes). In this work, we present a novel approach to perform fast and accurate localization, detection, and segmentation of volumes containing cells. Specifically, in our method, we modify and tune two state-of-the-art CNNs, namely 2D YOLOv2 and 3D U-Net, and combine them with a new fusion and image processing algorithms. Annotated volumes in this space are limited, and we have created synthetic data that mimics actual structures for training and testing our proposed approach. Promising results on this test data demonstrate the value of the technique and offers a methodology for 3D cell analysis in real microscopy imagery.
A Cognitive Architecture Realization of Event Cognition Capabilities (2022)

Intelligent agents that interact with our world need to store, retrieve, and use knowledge of their experiences similar to human knowledge of events. Humans leverage knowledge of events when engaged in summarizing experience, learning action models, and performing prospective cognition. Research on human event cognition describes how humans represent and think about events. In pursuit of artificial agents with human-level cognitive capabilities, this thesis theoretically unifies humanlike event cognition with general cognition. The theory characterizes human event cognition as data structures for representing event knowledge and processing for learning and using those data structures. The data structures that characterize human mental representations of events are event schemas, event models, and the working event model. These each represent different types of knowledge that humans maintain for events. Given these data structures, humanlike event cognition has the potential to be implemented as data structures and processing within many existing cognitive architectures' memory systems. Episodic memory stores previous experience and can potentially support knowledge of event models that represent specific previously experienced events. Semantic memory stores regularities or facts and can potentially support knowledge of event schemas that represent classes of events. Working memory represents the current situation and can include a working event model that guides action and describes how the current situation is expected to proceed. This suggests a general theoretical framework that uses cognitive architecture processing of event knowledge to account for event cognition capabilities. To test this theory, I extend the Soar cognitive architecture's episodic memory mechanisms and develop an artificial agent that uses those mechanisms. The agent implementation includes a restricted form of summarization that uses episodic memory to retrieve an event model representation of the agent's interaction with the environment. That summarization supports a task general form of instance-based action model learning, where action models are learned as event schema representations. Together, these demonstrate the utility of event knowledge within a cognitive architecture. Action model learning is developed and evaluated in two domains, providing an initial test of the broader theory.
Supplementary Table 2 from Resistance Mechanisms to Targeted Therapies in &lt;i&gt;ROS1&lt;/i&gt;&lt;sup&gt;+&lt;/sup&gt; and &lt;i&gt;ALK&lt;/i&gt;&lt;sup&gt;+&lt;/sup&gt; Non–small Cell Lung Cancer (2023)

&lt;p&gt;Read depths for ALK and ROS1 used in NGS&lt;/p&gt;
Analysis of electron trajectories in a gyrotron resonator (1998)

A detailed mathematical analysis of the equation describing the electron interaction with the high-frequency field in a gyrotron resonator is presented. Electron trajectories in the phase space are classified. It is proven that in the cold-cavity approximation when the high-frequency field is represented by a Gaussian-type function, the solutions of the gyrotron equation are asymptotically equal to the solutions of the corresponding unforced equation. This means that chaos, which, in principle, can develop in a resonator for some values of control parameters, can be only transient, i.e., electrons again follow regular trajectories once they leave the interaction space. Understanding of the distribution of electron trajectories is important, both from the theoretical and the practical point of view. As an example, detailed numerical computations of electron trajectories are performed for those values of control parameters which correspond to the maximum efficiency.
Effects of Mechanical Dyssynchrony on Coronary Flow: Insights From a Computational Model of Coupled Coronary Perfusion With Systemic Circulation (2020)

Mechanical dyssynchrony affects left ventricular (LV) mechanics and coronary perfusion. Due to the confounding effects of their bi-directional interactions, the mechanisms behind these changes are difficult to isolate from experimental and clinical studies alone. Here, we develop and calibrate a closed-loop computational model that couples the systemic circulation, LV mechanics, and coronary perfusion. The model is applied to simulate the impact of mechanical dyssynchrony on coronary flow in the left anterior descending artery (LAD) and left circumflex artery (LCX) territories caused by regional alterations in perfusion pressure and intramyocardial pressure (<i>IMP</i>). We also investigate the effects of regional coronary flow alterations on regional LV contractility in mechanical dyssynchrony based on prescribed contractility-flow relationships without considering autoregulation. The model predicts that LCX and LAD flows are reduced by 7.2%, and increased by 17.1%, respectively, in mechanical dyssynchrony with a systolic dyssynchrony index of 10% when the LAD's <i>IMP</i> is synchronous with the arterial pressure. The LAD flow is reduced by 11.6% only when its <i>IMP</i> is delayed with respect to the arterial pressure by 0.07 s. When contractility is sensitive to coronary flow, mechanical dyssynchrony can affect global LV mechanics, <i>IMP</i>s and contractility that in turn, further affect the coronary flow in a feedback loop that results in a substantial reduction of <i>dP</i> <sub><i>LV</i></sub> /<i>dt</i>, indicative of ischemia. Taken together, these findings imply that regional <i>IMP</i>s play a significant role in affecting regional coronary flows in mechanical dyssynchrony and the changes in regional coronary flow may produce ischemia when contractility is sensitive to the changes in coronary flow.
Dive: End-to-End Speech Diarization Via Iterative Speaker Embedding (2021)

We introduce DIVE, an end-to-end speaker diarization sys-tem. DIVE presents the diarization task as an iterative pro-cess: it repeatedly builds a representation for each speaker before predicting their voice activity conditioned on the ex-tracted representations. This strategy intrinsically resolves the speaker ordering ambiguity without requiring the classi-cal permutation invariant training loss. In contrast with prior work, our model does not rely on pretrained speaker represen-tations and jointly optimizes all parameters of the system with a multi-speaker voice activity loss. DIVE does not require the training speaker identities and allows efficient window-based training. Importantly, our loss explicitly excludes unreliable speaker turn boundaries from training, which is adapted to the standard collar-based Diarization Error Rate (DER) eval-uation. Overall, these contributions yield a system redefining the state-of-the-art on the CALLHOME benchmark, with 6.7% DER compared to 7.8% for the best alternative.
Biomechanical Simulation Integrating Swallowing and Breathing (2018)

The breath is to inhale and exhale the air from the nose or mouth to the lung through the throat. Swallowing is to transport food from the mouth to the esophagus via the throat. That means the throat has an intrinsic risk to be obstructed by food known as suffocation or to transport food to the lung as a mistake occurring aspiration pneumonia. In order to avoid both of suffocation and aspiration, breathing and swallowing must act under a precise coordination mechanism to fulfill each function. However, the mechanism has never been revealed yet, since even the latest medical imaging technology cannot visualize the whole motion of the breathing and swallowing at all. The purpose of this study is to produce a numerical simulation model that integrates breathing with swallowing and is to visualize the whole movement of the breathing and swallowing to clarify the coordination mechanism. First, we combined two numerical models of Lung 4Cer CFD for breathing and Swallow Vision® for swallowing. The two models were connected at the larynx of Swallow Vision®. Then the connected model had anatomical structures of the mouth, throat and esophagus as well as trachea, bronchi and lung. To validate the connected model, the lung movement and the airflow during breathing were analyzed. As a result, the flow rate of inflow/outflow surface was confirmed to equal the volume change of the lung.
Photons and Atoms Cooperate, Virtually (2012)

We present an experimental measurement of the cooperative Lamb shift and the Lorentz shift using a nanothickness atomic vapor layer with tunable thickness and atomic density. The cooperative Lamb shift arises due to the exchange of virtual photons between identical atoms. The interference between the forward and backward propagating virtual fields is confirmed by the thickness dependence of the shift, which has a spatial frequency equal to twice that of the optical field. The demonstration of cooperative interactions in an easily scalable system opens the door to a new domain for nonlinear optics.
Analysis of a proportionally fair and locally adaptive Spatial Aloha in Poisson Networks (2014)

The proportionally fair sharing of the capacity of a Poisson network using Spatial-Aloha leads to closed-form performance expressions in two extreme cases: (1) the case without topology information, where the analysis boils down to a parametric optimization problem leveraging stochastic geometry; (2) the case with full network topology information, which was recently solved using shot-noise techniques. We show that there exists a continuum of adaptive controls between these two extremes, based on local stopping sets, which can also be analyzed in closed form. We also show that these control schemes are implementable, in contrast to the full information case which is not. As local information increases, the performance levels of these schemes are shown to get arbitrarily close to those of the full information scheme. The analytical results are combined with discrete event simulation to provide a detailed evaluation of the performance of this class of medium access controls.
Oxidation/reduction Reactions of molybdate ions in sodium sulfate at 1200 K (1987)

Abstract Cyclic voltammetric studies have been used to elucidate the electrochemical and coupled chemical reactions of solute molybdate ions on sodium sulfate at 1200 K in different melt basicities fixed by SO 2 ‐O 2 atmospheres. The reaction mechanism was determined by use of the Nicholson and Shain diagnostic criteria in combination with the phase stability diagram. Within the regime of sodium sulfate stability, three reduction reactions occur. In a highly acidic melt, the reduction of molybdate ions (Mo in + 6 oxidation state) to Mo as + 4 is coupled to an irreversible chemical reaction whereby molybdenum dioxide precipitates. Molybdenum in oxidation state + 4 is reduced to Mo‐(+2)‐species which are immediately reoxidized to Mo‐(+4)‐species by an oxidizing agent in the melt, a so‐called catalytic reaction. The reduction of Mo‐(+2)‐species to molybdenum follows in a very narrow potential range close to the previous reaction and has the same catalytic reaction mechanism. The reduction reactions are reversible. The reverse oxidation of molybdenum to the molybdate ion proceeds in three electron transfer steps.
A Wireless Implantable Potentiostat for Programmable Electrochemical Drug Delivery (2021)

Implantable drug delivery systems can enable targeted, personalized, and on-demand treatments while minimizing the risk of side effects. In this work, we present an ultrasonically powered implantable potentiostat with bidirectional communication for programmable electrochemically controlled drug delivery. The system consists of piezoelectric transducers for wireless power and data, a 180 nm CMOS integrated circuit, and a custom drug delivery module containing drug-loaded electroresponsive nanoparticles. The chip includes power and downlink data recovery circuits for simultaneous power and data transfer up to 50 kbps, a programmable wide-range potentiostat capable of providing potentials up to ±1.5 V and sensing current up to ±100 µA, and a 125 kbps data transmitter. Wirelessly powered and controlled fluorescein release is used to demonstrate the drug delivery system.
Transition-State Analysis of Trypanosoma cruzi Uridine Phosphorylase-Catalyzed Arsenolysis of Uridine (2011)

Uridine phosphorylase catalyzes the reversible phosphorolysis of uridine and 2'-deoxyuridine to generate uracil and (2-deoxy)ribose 1-phosphate, an important step in the pyrimidine salvage pathway. The coding sequence annotated as a putative nucleoside phosphorylase in the Trypanosoma cruzi genome was overexpressed in Escherichia coli , purified to homogeneity, and shown to be a homodimeric uridine phosphorylase, with similar specificity for uridine and 2'-deoxyuridine and undetectable activity toward thymidine and purine nucleosides. Competitive kinetic isotope effects (KIEs) were measured and corrected for a forward commitment factor using arsenate as the nucleophile. The intrinsic KIEs are: 1'-(14)C = 1.103, 1,3-(15)N(2) = 1.034, 3-(15)N = 1.004, 1-(15)N = 1.030, 1'-(3)H = 1.132, 2'-(2)H = 1.086, and 5'-(3)H(2) = 1.041 for this reaction. Density functional theory was employed to quantitatively interpret the KIEs in terms of transition-state structure and geometry. Matching of experimental KIEs to proposed transition-state structures suggests an almost synchronous, S(N)2-like transition-state model, in which the ribosyl moiety possesses significant bond order to both nucleophile and leaving groups. Natural bond orbital analysis allowed a comparison of the charge distribution pattern between the ground-state and the transition-state models.
A Co-registration Pipeline for Multimodal MALDI and Confocal Imaging Analysis of Stem Cell Colonies (2020)

Multimodal mass spectrometry imaging (MSI) data presents unique big data challenges in handling and analysis. Here, we present a pipeline for co-registering matrix-assisted laser desorption/ionization MSI and confocal immunofluorescence imaging data for extracting single-cell metabolite signatures. We further describe methods and introduce software for the simultaneous analysis of these concatenated data sets, which are designed to establish a connection between cell traits of interest (shape metrics, position within sample) and the cells' own metabolic signatures.
Technical Assessment of Compressed Hydrogen Storage Tank Systems for Automotive Applications (2010)

This technical report describes DOE's assessment of the performance and cost of compressed hydrogen storage tank systems for automotive applications. The on-board performance (by Argonne National Lab) and high-volume manufacturing cost (by TIAX LLC) were estimated for compressed hydrogen storage tanks. The results were compared to DOE's 2010, 2015, and ultimate full fleet hydrogen storage targets. The Well-to-Tank (WTT) efficiency as well as the off-board performance and cost of delivering compressed hydrogen were also documented in the report.