gebhart/patrepeval huggingface dataset:
Immuno-PET Imaging of HER3 in a Model in which HER3 Signaling Plays a Critical Role (2015)

HER3 is overexpressed in various carcinomas including colorectal cancer (CRC), which is associated with poor prognosis, and is involved in the development of therapy resistance. Thus, an in vivo imaging technique is needed to evaluate the expression of HER3, an important therapeutic and diagnostic target. Here, we report successful HER3 PET imaging using a newly generated anti-human HER3 monoclonal antibody, Mab#58, and a mouse model of a HER3-overexpressing xenograft tumor. Furthermore, we assessed the role of HER3 signaling in CRC cancer tissue-originated spheroid (CTOS) and applied HER3 imaging to detect endogenous HER3 in CTOS-derived xenografts. Cell binding assays of 89Zr-labeled Mab#58 using the HER3-overexpressing cell line HER3/RH7777 demonstrated that [89Zr]Mab#58 specifically bound to HER3/RH7777 cells (Kd = 2.7 nM). In vivo biodistribution study in mice bearing HER3/RH7777 and its parent cell xenografts showed that tumor accumulation of [89Zr]Mab#58 in HER3/RH7777 xenografts was significantly higher than that in the control from day 1 to day 4, tending to increase from day 1 to day 4 and reaching 12.2 ± 4.5%ID/g. Radioactivity in other tissues, including the control xenograft, decreased or remained unchanged from day 1 to day 6. Positron emission tomography (PET) in the same model enabled clear visualization of HER3/RH7777 xenografts but not of RH7777 xenografts. CTOS growth assay and signaling assay revealed that CRC CTOS were dependent on HER3 signaling for their growth. In PET studies of mice bearing a CRC CTOS xenograft, the tumor was clearly visualized with [89Zr]Mab#58 but not with the 89Zr-labeled control antibody. Thus, tumor expression of HER3 was successfully visualized by PET with 89Zr-labeled anti-HER3 antibody in CTOS xenograft-bearing mice, a model that retains the properties of the patient tumor. Non-invasive targeting of HER3 by antibodies is feasible, and it is expected to be useful for cancer diagnosis and treatment.
A New Deep Learning Engine for CoralNet (2021)

CoralNet is a cloud-based website and platform for manual, semi-automatic and automatic analysis of coral reef images. Users access CoralNet through optimized web-based workflows for common tasks, and other systems can interface through API’s. Today, marine scientists are widely using CoralNet, and nearly 3,000 registered users have up-loaded 1,741,855 images from 2,040 distinct sources with over 65 million annotations. CoralNet is hosted on AWS, is free for users, and the code is open source <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . In January 2021, we released CoralNet 1.0 which has a new machine learning engine. This paper provides an overview of that engine, and the process of choosing the particular architecture, its training, and a comparison to some of the most promising architectures. In a nutshell, CoralNet 1.0 uses transfer learning with an EfficientNet-B0 backbone that is trained on 16M labelled patches from benthic images and a hierarchical Multi-layer Perceptron classifier that is trained on source-specific labelled data. When evaluated on a hold-out test set of 26 sources, the error rate of CoralNet 1.0 was 18.4% (relative) lower than CoralNet Beta.
Wind turbines structural loads during fault ride-through operation (2008)

Emphasis in this paper is on the fault ride-through operation impact on the wind turbines structural loads. Grid faults are typically simulated in power system simulation tools using simplified drive train mechanical model, approach which doesn't allow a thorough investigation of structural loads. This paper presents a two-steps simulation procedure, where two complimentary tools are combined, i.e. power system simulation tool and advanced aeroelastic simulation code. The goal is to investigate the whole integrated wind turbine design and to provide insight both into the structural as well as the electrical design of the wind turbine response during grid faults. The two-step simulation procedure is assessed by means of a simulation example. The effect of a grid fault on the structural part of a typical fixed speed wind turbine, equipped with an induction generator, is assessed.
Privacy-Enhancing Trust Infrastructure for Process Mining (2017)

Threats to a society and its social infrastructure are inevitable and endanger human life and welfare. Resilience is a core concept to cope with such threats in strengthening risk management. A resilient system adapts to an incident in a timely manner before it would result in a failure. This paper discusses the secondary use of personal data as a key element in such conditions and the relevant process mining in order to reduce IT risk on safety. It realizes completeness for such a proof on data breach in an acceptable manner to mitigate the usability problem of soundness for resilience. Acceptable soundness is still required and realized in our scheme for a fundamental privacy-enhancing trust infrastructure. Our proposal achieves an IT baseline protection and properly treats personal data on security as Ground Truth for deriving acceptable statements on data breach. An important role plays reliable broadcast by means of the block chain. This approaches a personal IT risk management with privacy-enhancing cryptographic mechanisms and Open Data without trust as belief in a single-point-of-failure. Instead it strengthens communities of trust.
CURD: a dynamic CUDA race detector (2018)

As GPUs have become an integral part of nearly every pro- cessor, GPU programming has become increasingly popular. GPU programming requires a combination of extreme levels of parallelism and low-level programming, making it easy for concurrency bugs such as data races to arise. These con- currency bugs can be extremely subtle and di cult to debug due to the massive numbers of threads running concurrently on a modern GPU. While some tools exist to detect data races in GPU pro- grams, they are often prohibitively slow or focused only on a small class of data races in shared memory. Compared to prior work, our race detector, CURD, can detect data races precisely on both shared and global memory, selects an appropriate race detection algorithm based on the synchronization used in a program, and utilizes efficient compiler instrumentation to reduce performance overheads. Across 53 benchmarks, we find that using CURD incurs an aver- age slowdown of just 2.88x over native execution. CURD is 2.1x faster than Nvidia’s CUDA-Racecheck race detector, de- spite detecting a much broader class of races. CURD finds 35 races across our benchmarks, including bugs in established benchmark suites and in sample programs from Nvidia.
Multi-Chip Module Test and Diagnostic Methodology (1983)

The development of a manufacturing test and diagnostic methodology for multi-chip modules as used in the IBM 4300 processor models involves determining the most attractive compromise among a number of conflicting factors: a) high test coverage, b) high diagnostic resolution, c) test generation, d) test equipment, and e) test application and diagnosis. This paper describes a set of solutions which were developed to create a high-volume, low-cost manufacturing test operation for the product in question. This paper examines the role of the testing methodology in productivity and product quality, details the diagnostic approach chosen, and provides an example of the overall manufacturing system performance achieved by analyzing a large module production sample.
Transition nuclear proteins are required for normal chromatin condensation and functional sperm development (2004)

The histone-to-protamine transition is important in the formation of spermatozoa. In mammals this involves two steps: replacement of histones by transition nuclear proteins (TPs) and replacement of TPs by protamines. To determine the functions of the TPs and their importance for sperm development, we generated mice lacking both TPs, since mice lacking only TP1 or TP2 were fertile. Our results indicated that TP1 and TP2 had partially complemented each other. In mice lacking both TPs, nuclear shaping, transcriptional repression, histone displacement, and protamine deposition proceeded relatively normally, but chromatin condensation was irregular in all spermatids, many late spermatids showed DNA breaks, and protamine 2 was not posttranslationally processed. Nevertheless, genomic integrity was maintained in mature spermatids, since efficient fertilization and production of offspring were achieved by intracytoplasmic sperm injection. However, many mature spermatids were retained in the testis, epididymal spermatozoa were drastically reduced in number and were highly abnormal, and the mice were sterile. Most epididymal spermatozoa were incapable of fertilization even using intracytoplasmic sperm injection. Thus, in mammals TPs are required for normal chromatin condensation, for reducing the number of DNA breaks, and for preventing the formation of secondary defects in spermatozoa, eventual loss of genomic integrity, and sterility.
Ecological intensification by integrating biogas production into nutrient cycling: Modeling the case of Agroecological Symbiosis (2018)

There is growing demand to produce both food and renewable energy in a sustainable manner, while avoidingcompetition between food and energy production. In our study, we investigated the potential of harnessingbiogas production into nutrient recycling in an integrated system of organic food production and food proces-sing. We used the case of Agroecological Symbiosis (AES) at Palopuro, which is a combination of three farms, abiogas plant, and a bakery, as a case to explore how biogas production using feedstocks from the farms can beused to improve nutrient cycling, and to calculate how much energy could be produced from the within-systemfeedstocks. The current system (CS) used in organic farms, and the integrated farm and food processing AESsystem, were analyzed using Substance Flow analysis. In the AES, annual nitrogen (N) and phosphorus (P)surpluses were projected to be reduced from 95 kg ha−1to 36 kg ha−1and from 3.4 kg ha−1to −0.5 kg ha−1 respectively, compared to the CS. Biogas produced from green manure leys as the major feedstock, produced2809 MWh a−1. This was 70% more than the energy consumed (1650 MWh a−1) in the systemand thus the AESsystem turned out to be a net energy producer. Results demonstrated the potential of biogas production toenhance the transition to bioenergy, nutrient recycling, and crop productivity in renewable localized farmingand food systems
Ellipsoidal particles at fluid interfaces (2008)

For partially wetting, ellipsoidal colloids trapped at a fluid interface, their effective, interface-mediated interactions of capillary and fluctuation-induced type are analyzed. For contact angles different from 90( degrees ) , static interface deformations arise which lead to anisotropic capillary forces that are substantial already for micrometer-sized particles. The capillary problem is solved using an efficient perturbative treatment which allows a fast determination of the capillary interaction for all distances between and orientations of two particles. Besides static capillary forces, fluctuation-induced forces caused by thermally excited capillary waves arise at fluid interfaces. For the specific choice of a spatially fixed three-phase contact line, the asymptotic behavior of the fluctuation-induced force is determined analytically for both the close-distance and the long-distance regime and compared to numerical solutions.
Development and external validation of a deep learning model for predicting response to HER2-targeted neoadjuvant therapy from pretreatment breast MRI. (2019)

593 Background: HER2-targeted neoadjuvant chemotherapy (NAC) possesses heterogeneous outcomes and currently lacks clinically-accepted markers of response. A means of predicting which patients will benefit prior to the treatment could reduce toxicity and the delay to effective intervention. Computational analysis of MRI via a deep neural network has shown promise in identifying NAC responders among mixed receptor subtype and treatment regimen cohorts, but faces challenges due to reproducibility across institutions and has not yet been explored in the context of HER2-targeted therapy. Here we present a deep learning approach for predicting response to HER2-targeted NAC from pre-treatment MRI. Methods: 100 HER2+ breast cancer patients who received NAC with docetaxel, carboplatin, trastuzumab, and pertuzumab at Cleveland Clinic (CCF) and had pre-treatment contrast-enhanced MRI’s were included in this analysis. 49 patients achieved pathological complete response (pCR, ypT0/is), while 51 patients retained presence of residual disease following NAC (non-pCR). 85 patients were used to train a convolutional neural network to predict pCR based on pre- and post-contrast MRI images, and the model design was optimized based on performance within a 15 patient internal validation cohort. An external, held-out testing dataset consisting of 28 patients (16 pCR, 12 non-pCR) imaged and treated at University Hospitals (UH) Cleveland Medical Center was used to validate the performance of the model. Performance was assessed by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. A multivariable model incorporating age, hormone receptor status, stage, and tumor size was developed and similarly evaluated. Results: The neural network was able to predict the response to HER2-targeted NAC in the internal validation cohort (AUC = 0.93) as well as in an independent cohort from a separate institution (AUC = 0.85). This model offered superior performance compared to a multivariate clinical model, which achieved AUC = 0.67 and AUC = 0.52, in internal validation and external held-out testing cohorts, respectively. Conclusions: Deep learning analysis of contrast-enhanced MRI could be used to better target anti-HER2 therapy by pre-treatment prediction of response.[Table: see text]
Valacyclovir Decreases Plasma HIV-1 RNA in HSV-2 Seronegative Individuals: A Randomized Placebo-Controlled Crossover Trial (2015)

These data indicate that the effects of valACV on HIV-1 replication are not related to the suppression of HSV-2-mediated inflammation and are consistent with a direct effect of ACV on HIV-1 replication.
Structure Elucidation and Computationally Guided Synthesis of SSZ‐43: A One‐Dimensional 12‐Ring Zeolite with Unique Sinusoidal Channels (2022)

Abstract The structure of zeolite SSZ‐43 was determined by 3D electron diffraction, synchrotron X‐ray powder diffraction, and high‐resolution transmission electron microscopy. The SSZ‐43 framework forms one‐dimensional, sinusoidal 12‐ring channels from 5 4 6 1 butterfly units commonly found in other zeolites, but with unique 6.5×6.5 Å apertures and 12‐ring 6.5×8.9 Å windows perpendicular to the channels. SSZ‐43 crystals are intergrowths of two polytypes: ≈ 90 % orthorhombic polytype A with ABAB stacking of the 12‐rings, and ≈10 % monoclinic polytype B with ABCABC stacking. Molecular modeling performed on the idealized Si‐SSZ‐43 structure along with empirical relationships for zeolite selectivity in boron‐ and aluminum‐containing synthesis gels were used in a combined approach to design new di‐quaternary ammonium organic structure‐directing agents (OSDAs). Experimental trials demonstrated that the new OSDAs produced SSZ‐43 over a broader range of compositions than previous mono‐quaternary OSDAs.
A low-power integrated magneto-optic modulator on silicon for cryogenic applications (2021)

A fundamental challenge of the quantum revolution is to efficiently interface the quantum computing systems operating at cryogenic temperatures with room temperature electronics and media for high data-rate communication. Current approaches to control and readout of such cryogenic computing systems use electrical cables, which prevent scalability due to their large size, heat conduction, and limited bandwidth1. A more viable approach is to use optical fibers which allow high-capacity transmission and thermal isolation. A key component in implementing photonic datalinks is a cryogenic optical modulator for converting data from the electrical to the optical domain at high speed and with low power consumption, while operating at temperatures of 4 K or lower. Cryogenic modulators based on the electro-optic effect have been demonstrated in a variety of material platforms, however they are voltage driven components while superconducting circuits are current based, resulting in a large impedance mismatch. Here, we present the first demonstration of an integrated current-driven modulator based on the magneto-optic effect operating over a wide temperature range that extends down to less than 4 K. The modulator works at data rates up to 2 Gbps with energy consumption below 4 pJ/bit, and we show that this figure can be reduced to less than 40 fJ/bit with optimized design and fabrication. This modulator is a hybrid device, where a current-driven magneto-optically active crystal (cerium substituted yttrium iron garnet, or Ce:YIG) is bonded to a high-quality silicon microring resonator. Because of its potential for extremely low power consumption under cryogenic conditions, the class of magneto-optical modulators demonstrated here has the potential to enable efficient data links in large-scale systems for quantum information processing.
The migration mechanism of transition metal ions in LiNi0.5Mn1.5O4 (2015)

<italic>In situ</italic> high-energy X-ray diffraction and neutron diffraction were deployed to trace the migration of transition metal ions in LiNi<sub>0.5</sub>Mn<sub>1.5</sub>O<sub>4</sub>.
Artificial Intelligence for COVID-19 Risk Classification in Kidney Disease: Can Technology Unmask an Unseen Disease? (2020)

Abstract Background We developed two unique machine learning (ML) models that predict risk of: 1) a major COVID-19 outbreak in the service county of a local HD population within following week, and 2) a hemodialysis (HD) patient having an undetected SARS-CoV-2 infection that is identified after following 3 or more days. Methods We used county-level data from United States population (March 2020) and HD patient data from a network of clinics (February-May 2020) to develop two ML models. First was a county-level model that used data from general and HD populations (21 variables); outcome of a COVID-19 outbreak in a dialysis service area was defined as a clinic being located in one of the national counties with the highest growth in COVID-19 positive cases (number and people per million (ppm)) in general population during 22-28 Mar 2020. Second was a patient-level model that used HD patient data (82 variables) to predict an individual having an undetected SARS-CoV-2 infection that is identified in subsequent ≥3 days. Results Among 1682 counties with dialysis clinics, 82 (4.9%) had a COVID-19 outbreak during 22-28 Mar 2020. Area under the receiver operating characteristic curve (AUROC) for the county-level model was 0.86 in testing dataset. Top predictor of a county experiencing an outbreak was the COVID-19 positive ppm in the general population in the prior week. In a select group (n=11,664) used to build the patient-level model, 28% of patients had COVID-19; prevalence was by design 10% in the testing dataset. AUROC for the patient-level model was 0.71 in the testing dataset. Top predictor of an HD patient having a SARS-CoV-2 infection was mean pre-HD body temperature in the prior week. Conclusions Developed ML models appear suitable for predicting counties at risk of a COVID-19 outbreak and HD patients at risk of having an undetected SARS-CoV-2 infection.
Semi-Blind Channel Estimation in MIMO Systems With Discrete Priors on Data Symbols (2021)

In this work, we addressthe MIMO semi-blindchannel estimation problem. We propose an eigenvalue decomposition based technique to significantly reduce the dimensionality of the EM based algorithm when the imposed prior on the data is Gaussian, greatly lowering the computational complexity. In addition to that, we apply the Minimum Power Distortionless Response (MPDR) decoupling principle to derive a tractable EM algorithm that uses the actual discrete prior of the data symbols. Our results show that the proposed MPDR based algorithm has superior performance over other EM based algorithms in both low and high SNR regions. The results also show that a faster version of the algorithm can be obtained by initializing it using the eigenvalue decomposition based Gaussian algorithm.
Mechanism of formation of Grignard reagents. The rate of reaction of cyclopentyl bromide with magnesium is transport limited in diethyl ether (1980)

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTMechanism of formation of Grignard reagents. The rate of reaction of cyclopentyl bromide with magnesium is transport limited in diethyl etherHarold R. Rogers, John Deutch, and George M. WhitesidesCite this: J. Am. Chem. Soc. 1980, 102, 1, 226–231Publication Date (Print):January 1, 1980Publication History Published online1 May 2002Published inissue 1 January 1980https://pubs.acs.org/doi/10.1021/ja00521a035https://doi.org/10.1021/ja00521a035research-articleACS PublicationsRequest reuse permissionsArticle Views596Altmetric-Citations47LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose Get e-Alerts
Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES (2022)

Abstract Summary Recent years have seen the release of several toolsets that reveal cell-cell interactions from single-cell data. However, all existing approaches leverage mean celltype gene expression values, and do not preserve the single-cell fidelity of the original data. Here, we present NICHES ( N iche I nteractions and C ommunication H eterogeneity in E xtracellular S ignaling), a tool to explore extracellular signaling at the truly single-cell level. NICHES allows embedding of ligand-receptor signal proxies to visualize heterogeneous signaling archetypes within cell clusters, between cell clusters, and across experimental conditions. When applied to spatial transcriptomic data, NICHES can be used to reflect local cellular microenvironment. NICHES can operate with any list of ligand-receptor signaling mechanisms and is compatible with existing single-cell packages and pseudotime techniques. NICHES is also a user friendly and extensible program, allowing rapid analysis of cell-cell signaling at single-cell resolution. Availability and implementation NICHES is an open-source software implemented in R under academic free license v3.0 and it is available at github.com/msraredon/NICHES. Use-case vignettes are available at https://msraredon.github.io/NICHES/ . Contact michasam.raredon@yale.edu ; yuval.kluger@yale.edu