gebhart/patrepeval huggingface dataset:
Capacity Enhancement for Reconfigurable Intelligent Surface-Aided Wireless Network: From Regular Array to Irregular Array (2023)

Reconfigurable intelligent surface (RIS) is promising for future 6G wireless communications. However, the increased number of RIS elements results in the high overhead for channel acquisition and the non-negligible power consumption. Therefore, how to improve the system capacity with limited RIS elements is essential. Unlike the classical regular RIS whose elements are arranged on a regular grid, in this paper, we propose an irregular RIS structure to improve the system capacity. The key idea is to irregularly configure a given number of RIS elements on an enlarged surface, which provides extra spatial degrees of freedom compared with the regular RIS. In this way, the received signal power can be enhanced, and thus the system capacity can be improved. Then, we formulate a joint topology and precoding optimization problem to maximize the capacity for irregular RIS-aided communication systems. Accordingly, a joint optimization algorithm with low complexity is proposed to alternately optimize the RIS topology and the precoding design. Particularly, a tabu search-based method is used to design the irregular RIS topology, and a neighbor extraction-based cross-entropy method is introduced to optimize the precoding design. Simulation results demonstrate that, subject to the constraint of limited RIS elements, the proposed irregular RIS can significantly enhance the system capacity.
Critical Role for Streptococcussuis Cell Wall Modifications and Suilysin in Resistance to Complement-Dependent Killing by Dendritic Cells (2011)

Streptococcus suis is an emerging zoonotic agent of septicemia and meningitis. Knowledge on host immune responses toward S. suis and strategies used by this pathogen for subversion of these responses is scarce. Here, S. suis modulation of dendritic cell (DC) functions were assessed for the first time. Using S. suis knockout mutants in capsular polysaccharide (CPS) expression, it was shown that CPS blocks DC phagocytosis and impairs cytokine release by hindering cell wall components. Mutants impaired in D-alanylation of lipoteichoic acid (LTA) or N-deacetylation of peptidoglycan (PG) further demonstrated the importance of cell wall in modulation of DC activation. Notably, LTA/PG modifications were identified as major players in resistance to complement-dependent killing by DCs. Finally, S. suis hemolysin was partially involved in cytokine release and also contributed to bacterial escape of opsonophagocytosis. Overall, S. suis uses its arsenal of virulence factors to modulate DC functions and escape immune surveillance.
Compressive Holography (2011)

This tutorial briefly reviews the history and theory of compressive measurement and explains may be applied in holographic systems to achieve snapshot diffraction tomography and wide field sparse aperture imaging.
Equilibration of Tyrosyl Radicals (Y356•, Y731•, Y730•) in the Radical Propagation Pathway of the Escherichia coli Class Ia Ribonucleotide Reductase (2011)

Escherichia coli ribonucleotide reductase is an α2β2 complex that catalyzes the conversion of nucleotides to deoxynucleotides using a diferric tyrosyl radical (Y(122)(•)) cofactor in β2 to initiate catalysis in α2. Each turnover requires reversible long-range proton-coupled electron transfer (PCET) over 35 Å between the two subunits by a specific pathway (Y(122)(•) ⇆ [W(48)?] ⇆ Y(356) within β to Y(731) ⇆ Y(730) ⇆ C(439) within α). Previously, we reported that a β2 mutant with 3-nitrotyrosyl radical (NO(2)Y(•); 1.2 radicals/β2) in place of Y(122)(•) in the presence of α2, CDP, and ATP catalyzes formation of 0.6 equiv of dCDP and accumulates 0.6 equiv of a new Y(•) proposed to be located on Y(356) in β2. We now report three independent methods that establish that Y(356) is the predominant location (85-90%) of the radical, with the remaining 10-15% delocalized onto Y(731) and Y(730) in α2. Pulsed electron-electron double-resonance spectroscopy on samples prepared by rapid freeze quench (RFQ) methods identified three distances: 30 ± 0.4 Å (88% ± 3%) and 33 ± 0.4 and 38 ± 0.5 Å (12% ± 3%) indicative of NO(2)Y(122)(•)-Y(356)(•), NO(2)Y(122)(•)-NO(2)Y(122)(•), and NO(2)Y(122)(•)-Y(731(730))(•), respectively. Radical distribution in α2 was supported by RFQ electron paramagnetic resonance (EPR) studies using Y(731)(3,5-F(2)Y) or Y(730)(3,5-F(2)Y)-α2, which revealed F(2)Y(•), studies using globally incorporated [β-(2)H(2)]Y-α2, and analysis using parameters obtained from 140 GHz EPR spectroscopy. The amount of Y(•) delocalized in α2 from these two studies varied from 6% to 15%. The studies together give the first insight into the relative redox potentials of the three transient Y(•) radicals in the PCET pathway and their conformations.
First Trimester Pregnancy Outcomes in a Large&nbsp;ART Center From the Lombardy County (Italy) During the Peak COVID-19 Pandemic. (2021)

Abstract This study evaluates if the pandemic has influenced ART outcome in an asymptomatic infertile population treated at one of the major COVID-19 epicenters during the weeks immediately preceding the lockdown. All ART procedures during two time periods were used for comparison: November 1st, 2018 to February 28th, 2019 (non COVID-19 risk) and November 1st, 2019 to February 29th, 2020 (COVID-19 risk). We analyzed 1,749 fresh cycles (883 non COVID-19 risk and 866 COVID- 19 risk), 1,166 embryo and 63 oocytes warming cycles (538 and 37 during non COVID and 628 and 26 during COVID-19 risk respectively). Clinical pregnancies per cycle were not different: 370 (25,38%) in non COVID vs 415 (27,30%) (p=0.237) during COVID-19 risk. There were no differences in biochemical pregnancy rates 52 (3,57%) vs 38 (2,50%) (p=0.089) nor in ectopic pregnancies 4 (1,08%) vs 3 (0,72%) (p=0,594), spontaneous abortions 84 (22,70%) vs 103 (24,82%) p=0,487, intrauterine ongoing pregnancies 282 (76,22%) vs 309 (74,46%) p=0.569. A multivariate analysis investigating differences in abortion rate showed no differences between the two times frame. Our results support no differences in asymptomatic infertile couples’ ART outcomes between pre COVID and COVID-19 period in one of early and most severe pandemic area.
Apelin and the proopiomelanocortin system: a new regulatory pathway of hypothalamic α-MSH release (2011)

Neuronal networks originating in the hypothalamic arcuate nucleus (Arc) play a fundamental role in controlling energy balance. In the Arc, neuropeptide Y (NPY)-producing neurons stimulate food intake, whereas neurons releasing the proopiomelanocortin (POMC)-derived peptide α-melanocyte-stimulating hormone (α-MSH) strongly decrease food intake. There is growing evidence to suggest that apelin and its receptor may play a role in the central control of food intake, and both are concentrated in the Arc. We investigated the presence of apelin and its receptor in Arc NPY- and POMC-containing neurons and the effects of apelin on α-MSH release in the hypothalamus. We showed, by immunofluorescence and confocal microscopy, that apelin-immunoreactive (IR) neuronal cell bodies were distributed throughout the rostrocaudal extent of the Arc and that apelin was strongly colocalized with POMC, but weakly colocalized with NPY. However, there were numerous NPY-IR nerve fibers close to the apelin-IR neuronal cell bodies. By combining in situ hybridization with immunohistochemistry, we demonstrated the presence of apelin receptor mRNA in Arc POMC neurons. Moreover, using a perifusion technique for hypothalamic explants, we demonstrated that apelin-17 (K17F) increased α-MSH release, suggesting that apelin released somato-dendritically or axonally from POMC neurons may stimulate α-MSH release in an autocrine manner. Consistent with these data, hypothalamic apelin levels were found to be higher in obese db/db mice and fa/fa Zucker rats than in wild-type animals. These findings support the hypothesis that central apelin is involved in regulating body weight and feeding behavior through the direct stimulation of α-MSH release.
Particle generation during photoresist dissolution (2010)

A lattice-type Monte Carlo based mesoscale model and simulation of the lithography process has been described previously [1]. The model includes the spin coating, post apply bake, exposure, post exposure bake and development steps. This simulation has been adapted to study the insoluble particle generation that arises from statistically improbable events. These events occur when there is a connected pathway of soluble material that envelops a volume of insoluble material due to fluctuations in the deprotection profile that occur during the post exposure bake [2]. Development erodes the insoluble material into the developer stream as an insoluble particle. This process may produce a cavity on the line edge that can be far larger than a single polymer molecule. The insoluble particles generated may coalesce in developer to form large aggregates of insoluble material that ultimately deposit on the wafer surface and the tooling. The recent modifications made in mesoscale models for the PEB and dissolution steps, which have enabled this study are briefly described. An algorithm that was used for particle detection in the current study is also discussed. The effect of the resist formulation and the different lithographic steps, namely, exposure, post exposure bake and development, on the extent of particle generation is analyzed. These simulations can be used to set process variables to minimize the extent of particle generation.
Vitamin D insufficiency in the first 6 months of infancy and challenge‐proven IgE‐mediated food allergy at 1 year of age: a case‐cohort study (2017)

There was no evidence that VDI during the first 6 months of infancy is a risk factor for food allergy at 1 year of age. These findings primarily relate to egg allergy, and larger studies are required.
Adaptive point-scan imaging beyond the frame rate–resolution limit with scene-reactive scan trajectories (2022)

Point-scanned imaging modalities, such as optical coherence tomography (OCT), are subject to frame rate limits governed by image resolution, sampling rate, and scanner dynamics. For scenes with dynamic features on static backgrounds, adaptive scanning escapes these limits by visiting scan positions only as needed. We implemented adaptive scanning using a probabilistic approach that balanced re-imaging of known dynamic positions with exploration for undiscovered ones. We evaluated our approach in model systems that simulated multi-target tracking and ophthalmic surgery using a swept-source OCT system. We demonstrate frame rate speedups in excess of 5× without degradation in image quality, performance that would have otherwise required a significantly faster source.
Evaluation of x-ray irradiation on 65nm Multi-Level Cell NOR flash technologies (2010)

In this work, 65nm NOR flash memory is used for an evaluation of data retention and impact on cell charge based on varying levels of exposure to x-ray waves. A sample of 100 fully tested and configured units were programmed with a physical checkerboard pattern (half programmed, half erased) and exposed to conditions found in industrial x-ray stations. Readouts of the data pattern were done at various stages throughout the experiment and a comparison of cell Vt was performed on a population of worst case cells (lowest Vt on programmed cells, highest Vt on erased cells). Data was collected on a bit by bit basis and plotted as a cumulative probability function. Bakes were also performed to introduce any potential defects not seen initially as part of the exposure - and the readout data was collected for this stage as well. Results indicated there is a correlation on the amount of charge gain and loss seen based on the amount of total radiation incident upon the cells in extreme conditions.
Figure S1. Quantitative RT-PCR validation of ixazomib genes. from Proteasomal Inhibition by Ixazomib Induces CHK1 and MYC-Dependent Cell Death in T-cell and Hodgkin Lymphoma (2023)

&lt;p&gt;The bar graph show results (as fold-change values) from quantitative RT-PCR validation of the following genes, MYC, lysosomal - SQSTM1/p62, proteasomal function- PSMB3, PSMB5, PSMD1, PSMD2, common overlapping gene- PSAP, with RNA isolated from control and ixazomib treated Jurkat (TCL) and L540 and L428 (HL) cells, and comparison with the results from microarray experiment. The calculated the p-values (* = p &lt; 0.05, ** = p &lt; 0.01, and *** p &lt; 0.001), were determined based on the fold change values between control and ixazomib treated cells.&lt;/p&gt;
Realizing more accurate OPC models by utilizing SEM contours (2020)

The method to perform Optical Proximity Correction (OPC) model calibration with contour-based input data from both small field of view (SFoV) and large field of view (LFoV) e-beam inspection is presented. For advanced OPC models - such as Neural Network Assisted Models (NNAM) [1], pattern sampling is a critical topic, where pattern feature vectors utilized in model training, such as image parameter space (IPS) is critical to ensure accurate model prediction [2-5]. In order to improve the design space coverage, thousands of gauges with unique feature vector combinations might be brought into OPC model calibration to improve pattern coverage. The time and cost in conventional Critical Dimension Scanning Electron Microscope (CD-SEM) metrology to measure this large amount of CD gauges is costly. Hence, an OPC modeling solution with contourbased input has been introduced [6]. Built on this methodology, a single inspection image and SEM contour can include a large amount of information along polygon edges in complex logic circuit layouts. Namely, a better feature vector coverage could be expected [7]. Furthermore, much less metrology time is needed to collect the OPC modeling data comparing to conventional CD measurements. It is also shown that by utilizing large field 2D contours, which are difficult to characterize by CD measurements, in model calibration the model prediction of 2D features is improved. Finally, the model error rms of conventional SFoV modeling and LFoV contour modeling between SEM contours and simulation results are compared.
PENTRAN: A three-dimensional scalable transport code with complete phase-space decomposition (1996)

Three-dimensional S{sub N} transport modeling is gaining importance, with a rising need for detailed fluence calculations in geometries with inherent asymmetries. These calculations often cannot be adequately performed due to immense memory and storage requirements. New scalable, parallel discrete ordinates codes that take advantage of distributed memory parallel architectures are required. In general, scalable parallel algorithms minimize serial operations, maximize algorithm and data parallelism, and maximize computation to communication ratio. A leading issue in scalability is the minimization of serial input/output (I/O) in parallel algorithms. All I/O tasks should be handled efficiently by each individual processor. Further, memory-intensive arrays should be partitioned locally, distributed among processors. Regardless of parallel speedup, use of a local, partitioned memory structure is the only way that larger problems can be solved at all. In addition to using local memory allocation, efficient interprocess communicator groups for specific sets of processors should be used, allowing as needed message passing between necessary groups of processors, avoiding wasteful communications. Acceleration methods are needed because, without them, convergence can be difficult in some problems. Complete parallel decomposition of the problem should also be available; recent analysis of the transport source iteration indicates that significant parallel speedups are possible withmore » various decomposition strategies of the phase-space (angle, energy, and spatial dimensions) over a large processor array.« less
Impact of Concatenation of Digital Craniocaudal Mammography Images on a Deep-Learning Breast-Density Classifier Using Inception-V3 and ViT (2022)

Breast density is an indicator of a patient’s predisposed risk of breast cancer. Although not fully understood, increased breast density increases the likelihood of developing breast cancer. Accurate assessment of breast density from mammogram images is a challenging task for the radiologist. A patient’s breast density is assigned to one of four categories outlined by Breast Imaging and Reporting Data Systems (BIRADS). There have been efforts to identify automated approaches to assist radiologists in the classification of a patient’s breast density. The interest in using deep learning to fulfill this need for an automated approach has seen a significant increase in recent years. The preprocessing techniques used to develop these deep learning approaches often have a profound impact on the model’s accuracy and clinical viability. In this paper, we outline a novel image preprocessing technique where we concatenate individual mammogram images and compare the results using this technique between Inception-v3 and a vision transformer (ViT). The results are compared using the area under (AUC) the receiver operator characteristics (ROC) curves and traditional accuracy metrics.
Solubility of Metals in Substoichiometric TiC 1‐x (1984)

Twenty‐one elements were screened for solubility in selected titanium‐carbide compositions at 1600°C. Tin, lead, and aluminum were found to dissolve to an appreciable extent only in carbide compositions very near the titanium‐titanium carbide phase boundary. Small additions of Sn caused a substantial shift in this boundary in the direction of reduced carbon content, accompanied by reduced microhardness, indicating reduced flow stress. The degree of solubility of the alloying additions and/or excess Ti in the carbide is lower at T &lt;1400°C, as evidenced by the appearance of fine metallic precipitates on aging or slow cooling. However, a single‐phase alloyed carbide can be retained by quenching. In contrast, the solubility of Cr in TiC was found to decrease with decreasing carbon content, and the flow stress, as indicated by microhardness, to increase with increasing Cr content.
Sparks of Artificial General Intelligence: Early experiments with GPT-4 (2023)

Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.
Characterizing the Interactions of Organic Nanoparticles with Renal Epithelial Cellsin Vivo (2015)

Nanotechnology approaches are actively being pursued for drug delivery, novel diagnostics, implantable devices, and consumer products. While considerable research has been performed on the effects of these materials on targeted tumor or phagocytic cells, relatively little is known about their effects on renal cells. This becomes critical for supersmall nanoparticles (<10 nm), designed to be renally excreted. The active endocytic machinery of kidney proximal tubules avidly internalizes filtered proteins, which may also be the case for filtered nanoparticles. To test whether such interactions affect kidney function, we injected mice with either 5 nm dextran-based nanoparticles (DNP) that are similar in composition to FDA-approved materials or poly(amido amine) dendrimer nanoparticles (PNP) of comparable size. These fluorescently tagged nanoparticles were both filtered and internalized by renal tubular epithelial cells in a dose- and time-dependent fashion. The biological effects were quantitated by immunocytochemistry, measuring kidney injury markers and performing functional tests. DNP administration resulted in a dose-dependent increase in urinary output, while cellular albumin endocytosis was increased. The expression of megalin, a receptor involved in albumin uptake, was also increased, but AQP1 expression was unaffected. The effects after PNP administration were similar but additionally resulted in increased clathrin expression and increased endocytosis of dextran. We conclude that there are no major detrimental renal effects of DNP on overall kidney function, but changes in endocytosis-mediating protein expression do occur. These studies provide a framework for the testing of additional nanoparticle preparations as they become available.
Comprehensive Study of the Chemical, Physical, and Structural Evolution of Molecular Layer Deposited Alucone Films during Thermal Processing (2023)

Thermal processing of molecular layer deposited (MLD) hybrid inorganic–organic alucone thin films produced porous and low-k materials. Alucone films were deposited by MLD using trimethyl aluminum and ethylene glycol at 120 °C. Changes in the film density and thickness during annealing were monitored using in-situ X-ray reflectivity and were compared to atomic layer deposited (ALD) alumina films. The chemical evolution of the as-deposited and annealed alucone films during post-deposition heating with and without UV was probed using infrared spectroscopy, Rutherford backscattering, nuclear reaction analysis, and 15N spectroscopy, providing a detailed understanding of the induced changes. The concentration of OH groups decreased after depositing 1 nm of alumina capping layer as a barrier to moisture uptake, which also decreased the etch rate in CF4/O2 plasma. The lowest dielectric constant of the processed alucone films (kmin = 4.75) was 25% lower than the lowest values measured in ALD alumina counterparts (kmin = 6.7). Large thickness decreases for alucone films were observed at ∼200 °C of anneal temperatures. Removal of retained organic components by thermal processing of MLD films is demonstrated to be a promising and versatile route to porous thin films for a wide range of applications including low dielectric constant materials.