gebhart/patrepeval huggingface dataset:
hpaA mutants of Xanthomonas campestris pv. vesicatoria are affected in pathogenicity but retain the ability to induce host‐specific hypersensitive reaction (1998)

Xanthomonas campestris pv. vesicatoria is the causal agent of bacterial spot disease on pepper and tomato plants. We reported previously that the main hrp (hypersensitive reaction and pathogenicity) gene cluster in X. c. pv. vesicatoria contains six transcription units, designated hrpA to hrpF. We present here the sequence of the hrpD operon and an analysis of non-polar mutants in each of the six genes. Three genes, hrcQ, hrcR and hrcS, are predicted to encode conserved components of type III protein secretion systems in plant and mammalian pathogenic bacteria. For hrpD5 and hrpD6, homologues have only been found in Ralstonia solanacearum. Interestingly, the hrpD operon contains one gene, hpaA (for hrp-associated), which is specifically required for disease development. hpaA mutants are affected in pathogenicity, but retain in part the ability to induce avirulence gene-mediated, host-specific hypersensitive reaction (HR). In addition, HpaA was found to contain two functional nuclear localization signals, which are important for the interaction with the plant. We propose that HpaA is an effector protein that may be translocated into the host cell via the Hrp secretion pathway.
Self-Chemical Ionization (SELF-CI) (2016)

This document contains recommendations for terminology in mass spectrometry. Development of standard terms dates back to 1974 when the IUPAC Commission on Analytical Nomenclature issued recommendations on mass spectrometry terms and definitions. In 1978, the IUPAC Commission on Molecular Structure and Spectroscopy updated and extended the recommendations and made further recommendations regarding symbols, acronyms, and abbreviations. The IUPAC Physical Chemistry Division Commission on Molecular Structure and Spectroscopy’s Subcommittee on Mass Spectroscopy revised the recommended terms in 1991 and appended terms relating to vacuum technology. Some additional terms related to tandem mass spectrometry were added in 1993 and accelerator mass spectrometry in 1994. Owing to the rapid expansion of the field in the intervening years, particularly in mass spectrometry of biomolecules, a further revision of the recommendations has become necessary. This document contains a comprehensive revision of mass spectrometry terminology that represents the current consensus of the mass spectrometry community.
Generative AI meets Responsible AI: Practical Challenges and Opportunities (2023)

Generative AI models and applications are being rapidly developed and deployed across a wide spectrum of industries and applications ranging from writing and email assistants to graphic design and art generation to educational assistants to coding to drug discovery. However, there are several ethical and social considerations associated with generative AI models and applications. These concerns include lack of interpretability, bias and discrimination, privacy, lack of model robustness, fake and misleading content, copyright implications, plagiarism, and environmental impact associated with training and inference of generative AI models.
Accurate Prediction of Thermal Runaway at Blocking Conditions in Drives Applications (2019)

One promising way to increased power densities of IGBTs and higher output currents of modules is to increase the maximum junction temperatures. With higher junction temperatures and with respect to the used load profiles, the impact of leakage current generated during blocking becomes more and more important. An extension of a power-loss calculation framework is proposed. It enables a fast and accurate calculation of temperature-dependent switching, conduction and blocking losses for a single-phase PWM inverter application for various overload profiles, predicting thermal runaway. Numerical implementation of the algorithm is described, and a critical amount of chip leakage current is determined as a function of different thermal heatsink capacitances.
Cognitive radar antenna selection via deep learning (2019)

Direction‐of‐arrival (DoA) estimation of targets improves with the number of elements employed by a phased array radar antenna. Since larger arrays have high associated cost, area and computational load, there is a recent interest in thinning the antenna arrays without loss of far‐field DoA accuracy. In this context, a cognitive radar may deploy a full array and then select an optimal subarray to transmit and receive the signals in response to changes in the target environment. Prior works have used optimisation and greedy search methods to pick the best subarrays cognitively. In this study, deep learning is leveraged to address the antenna selection problem. Specifically, they construct a convolutional neural network (CNN) as a multi‐class classification framework, where each class designates a different subarray. The proposed network determines a new array every time data is received by the radar, thereby making antenna selection a cognitive operation. Their numerical experiments show that the proposed CNN structure provides 22% better classification performance than a support vector machine and the resulting subarrays yield 72% more accurate DoA estimates than random array selections.
Targeting Type-I Eicosacationic [70]Fullerene-(Light Harvesting Antenna) PDI Agents for Efficient Photokilling of Multiantibiotics-Resistant Bacteria (2016)

We are particularly interested in mediating broad-spectrum killing of pathogenic Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli bacteria targets. Systematic evaluation was taken to evaluate a structure–function relationship of fullerene-(light harvesting antenna) conjugative diads and triads on the influence of their biological activity, aiming to achieve the optimized photodynamic inactivation (PDI) efficiency against multiantibiotics-resistant bacteria. Accordingly, we found a rational design of effective fullerene-based nano-photosensitizers by chemical modification of the carbon cage with two cell-targeting moieties each with a well-defined high number of cationic charges that allowed us to demonstrate the feasibility of these compounds giving cytotoxicity toward both Gram-positive and Gram-negative species. We also uncovered the large enhancement of PDI efficiency in photokilling of infectious bacteria by replacing the C 60 cage with a C 70 leading to novel type-I eicosacationic [70]fullerenes as potential nano-PDI agents.
Buffy coat (top/bottom)‐ and whole‐blood filtration (top/top)‐produced red cell concentrates differ in size of extracellular vesicles (2015)

Haemolysis and EVs increase in BC and WBF RCCs during storage. Differences in the size characteristics of the EVs in WBF and BC RCCs suggest that non-RBC EVs are more prevalent in WBF products. Understanding the impact that manufacturing has on the characteristics of the different populations of EVs in RCCs will aid quality improvement efforts.
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition (2022)

We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a million hours of audio. We find that the combination of pre-training, self-training and scaling up model size greatly increases data efficiency, even for extremely large tasks with tens of thousands of hours of labeled data. In particular, on an ASR task with 34 k hours of labeled data, by fine-tuning an 8 billion parameter pre-trained Conformer model we can match state-of-the-art (SoTA) performance with only 3% of the training data and significantly improve SoTA with the full training set. We also report on the universal benefits gained from using big pre-trained and self-trained models for a large set of downstream tasks that cover a wide range of speech domains and span multiple orders of magnitudes of dataset sizes, including obtaining SoTA performance on many public benchmarks. In addition, we utilize the learned representation of pre-trained networks to achieve SoTA results on non-ASR tasks.
Comportement sismique d'un système formé de murs couplés tronqués et d'un cadre (2001)

This paper presents results of a numerical investigation on the nonlinear seismic behavior of a reinforced concrete system made of truncated coupled shear walls (CSWs) and a frame. The objective of the study is twofold: (a) examine the seismic behavior of CSWframe systems with setbacks and (b) verify the adequacy of the National Building Code (NBC) as far as the design of such systems. The study considers two types of CSWs, coupled and partially coupled, two Canadian seismic zones, 4 and 6, six different levels of setbacks, and 10 historic seismic records encompassing a wide range of frequency content. Results of the study show that the CSWframe system with no setback offers a satisfactory seismic behavior with regards to the sequence of plastification and the ductility demand. For the truncated systems, the NBC recommendations that the static method is not adequate is justified. Also, in the presence of setbacks, the columns show a higher degree of plastification with a possibility of formation of plastic mechanism (soft story). The ductility demand is also higher and can exceed the accepted practical limit in certain cases.Key words: coupled shear walls, frame, reinforced concrete, setback, seismic behavior, code.
GENE EXPRESSION WITHIN A HUMAN CHOROIDAL NEOVASCULAR MEMBRANE USING SPATIAL TRANSCRIPTOMICS (2023)

Macular neovascularization is a relatively common and potentially visually devastating complication of age-related macular degeneration. In macular neovascularization, pathologic angiogenesis can originate from either the choroid or the retina, but we have limited understanding of how different cell types become dysregulated in this dynamic process. In this study, we performed spatial RNA sequencing on a human donor eye with macular neovascularization as well as a healthy control donor. We identified genes enriched within the area of macular neovascularization and used deconvolution algorithms to predict the originating cell type of these dysregulated genes. Within the area of neovascularization, endothelial cells were predicted to increase expression of genes related to Rho family GTPase signaling and integrin signaling. Likewise, VEGF and TGFB1 were identified as potential upstream regulators that could drive the observed gene expression changes produced by endothelial and retinal pigment epithelium cells in the macular neovascularization donor. These spatial gene expression profiles were compared to previous single-cell gene expression experiments in human age-related macular degeneration as well as a model of laser-induced neovascularization in mice. As a secondary aim, we also investigated spatial gene expression patterns within the macular neural retina and between the macular and peripheral choroid. We recapitulated previously described regional-specific gene expression patterns across both tissues. Overall, this study spatially analyzes gene expression across the retina, retinal pigment epithelium, and choroid in health and describes a set of candidate molecules that become dysregulated in macular neovascularization.
Photoperiodically Induced Changes in Seed Growth Rate of Soybean as Related to Endogenous Concentrations of ABA and Sucrose in Seed Tissues (1990)

Short-day photoperiods can increase the partitioning of assimilates to filling seeds of soybean (Glycine max L. Merr.), resulting in higher seed growth rates. The plant growth substance ABA has been implicated in the regulation of assimilate transfer within filling soybean seeds. Thus, we hypothesized that an increased concentration of endogenous ABA in seeds may enhance sucrose accumulation and seed growth rate of soybeans exposed to short-day photoperiods. Plants of cv. Hood 75 were grown in a greenhouse under an 8-h short-day photoperiod (SD) until 11 d after anthesis (DAA) of the first flower, when half of the plants were transferred to a night-interruption (NI) treatment (3 h of low-intensity light inserted into the middle of the dark period). Plants remaining in SD throughout seed development had seed growth rates 43% higher than that of plants shifted to NI (7·6 mg seed−1 d−1vs. 5·3 mg seed−1 d−1). On a tissue-water basis, the concentration of ABA in SD seeds increased rapidly from 7.6 μmol l−1 at 11 DAA to 65·2 μmol l−1 at 18 DAA, but then declined to 6·6 μmol l−1 by 39 DAA. In contrast, the concentration of ABA increased more slowly in NI seeds, reaching only 47·4 μmol l−1 by 18 DAA, peaking at 57·0 μmol l−1 on 25 DAA, and declining to 10·2 μmol l−1 by 39 DAA. The concentration of sucrose in SD embryos peaked at 73·5 mmol l−1 on 25 DAA and remained relatively constant for the remainder of the seed-filling period. In NI, the concentration of sucrose reached only 38·3 mmol 1−1 by 25 DAA, and peaked at 61·5 μmol l−1 on 32 DAA. Thus in both SD and NI, sucrose accumulated in embryos only after the peak in ABA concentration, suggesting that ABA may have stimulated sucrose movement to the seeds. The earlier accumulation of ABA and sucrose in SD suggests that ABA may have increased assimilate availability during the critical cell-division period, thus regulating cotyledon cell number and subsequent seed growth rate for the remainder of the seed-filling period.
Are There Disparities in Transplant-Related Morbidity and Mortality after Hematopoietic Stem Cell Transplant: An Institution-Based Analysis of Autologous and Allogeneic Transplant Recipients (2014)

Abstract Background: Previous national data demonstrate racial/ethnic survival disparities among hematopoietic stem cell transplant recipients. It is unclear, however, if these disparities persist in large academic settings. Therefore, we sought to investigate racial/ethnic transplant-related mortality and morbidity disparities after autologous and allogeneic transplant in our single-institution major transplant center, adjusting for the influence of patient factors, clinical factors, insurance type, and neighborhood socioeconomic status. Methods: We conducted a retrospective chart review of all patients who received a hematopoietic stem cell transplant (autologous and allogeneic) at Stanford University during the years of 1998-2012. Kaplan Meier method predicted survival by race/ethnicity stratified by transplant type. Cox proportional hazard models estimated mortality and transplant-related morbidity (graft-versus host disease for allogeneic transplant recipients) adjusted by demographic (age, gender, race/ethnicity), clinical (diagnosis, year of transplant, treatment regimen), and socioeconomic (insurance, neighborhood socioeconomic status) factors. Results: There were a total of 3,407 patients included in the analysis who received a hematopoietic stem cell transplant during the years of study. Unadjusted Kaplan Meier Curves showed a statistically significant improved survival for Hispanics and Asian/Pacific Islander (API) patients compared with NWH allogeneic transplant recipients (p = 0.01), however, there were no differences in survival for allogeneic transplant recipients by race/ethnicity (p=0.77). Cox proportional hazard models, among autologous transplant recipients, after adjustment for demographic, clinical, and socioeconomic status factors, demonstrated an increased mortality for API patients compared with non-Hispanic white (NHW) patients (HR 1.25 95% CI (1.01-1.57). Non-Hispanic black (NHB) had an equivalent mortality and Hispanics had a non-statistically significant mortality compared with NHW populations (HR 0.97 95% CI (0.69-1.35); HR 0.82 95% CI (0.61-1.10), respectively). Among allogeneic transplant recipients, we found no mortality disparities by race/ethnicity in fully adjusted models. However, we found transplant-related morbidity disparities with an increased odds of graft-versus-host disease among API recipients compared with NHW recipients. (OR 1.57; 95% CI (1.06-2.31)). Conclusions: After adjustment for individual, clinical, socioeconomic and insurance status factors, we found increased mortality after autologous transplant and a higher odds of treatment-related morbidity after allogeneic transplant among API patients compared with other racial/ethnic groups. The worse outcomes among these groups despite equal access to post-transplant related care in our institution requires further investigation. Disclosures No relevant conflicts of interest to declare.
Interrogation of Mammalian Protein Complex Structure, Function, and Membership Using Genome-Scale Fitness Screens (2018)

Protein complexes are assemblies of subunits that have co-evolved to execute one or many coordinated functions in the cellular environment. Functional annotation of mammalian protein complexes is critical to understanding biological processes, as well as disease mechanisms. Here, we used genetic co-essentiality derived from genome-scale RNAi- and CRISPR-Cas9-based fitness screens performed across hundreds of human cancer cell lines to assign measures of functional similarity. From these measures, we systematically built and characterized functional similarity networks that recapitulate known structural and functional features of well-studied protein complexes and resolve novel functional modules within complexes lacking structural resolution, such as the mammalian SWI/SNF complex. Finally, by integrating functional networks with large protein-protein interaction networks, we discovered novel protein complexes involving recently evolved genes of unknown function. Taken together, these findings demonstrate the utility of genetic perturbation screens alone, and in combination with large-scale biophysical data, to enhance our understanding of mammalian protein complexes in normal and disease states.
Analyzing the Effect of Air Capacitor Turbocharging Single Cylinder Engines on Fuel Economy and Emissions Through Modeling and Experimentation (2018)

Turbocharging can provide a cost effective means for increasing the power output and fuel economy of an internal combustion engine. A turbocharger added to an internal combustion engine consists of a coupled turbine and compressor. Currently, turbocharging is common in multi-cylinder engines, but it is not commonly used on single-cylinder engines due to the phase mismatch between the exhaust stroke (when the turbocharger is powered) and the intake stroke (when the engine intakes the compressed air). The proposed method adds an air capacitor, an additional volume in series with the intake manifold, between the turbocharger compressor and the engine intake, to buffer the output from the turbocharger compressor and deliver pressurized air during the intake stroke. This research builds on previous work where it was shown experimentally that a power gain of 29% was achievable and that analytically a power gain of 40-60% was possible using a turbocharger and air capacitor system. The goal of this study is to further analyze the commercial viability of this technology by analyzing the effect of air capacitor turbocharging on emissions, fuel economy, and power density. An experiment was built and conducted that looked at how air capacitor sizing affected emissions, fuel economy, and the equivalence ratio. The experimental data was then used to calibrate a computational model built in Ricardo Wave. Finally this model was used to evaluate strategies to further improve the performance of a single cylinder diesel turbocharged engine with an air capacitor.
Convergence of a neural network classifier (1990)

It is shown that the LVQ (learning vector quantization) learning algorithm converges to locally asymptotic stable equilibria of an ordinary differential equation. It is demonstrated that the learning algorithm performs stochastic approximation. Convergence of the Voronoi vectors is guaranteed under the appropriate conditions on the underlying statistics of the classification problem. The authors also present a modification to the learning algorithm which, it is argued, results in convergence of the LVQ for a larger set of initial conditions. Finally, it is shown that the LVQ is a general histogram classifier and that its risk converges to the Bayesian optimal risk as the appropriate parameters go to infinity with the number of past observations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>
Human herpesvirus 6 (1997)

Human herpesvirus 6 (HHV-6) is a T-lymphotropic herpesvirus, which infects almost all children by the age of two years and persists lifelong. Two distinct variants of HHV-6, HHV-6A and HHV-6B, have been described, and the latter has been shown to be a common cause of acute febrile illnesses in young children, including exanthem subitum (roseola). HHV-6 has also been associated with a number of neurological disorders, including encephalitis and seizures, and the virus has been postulated to play a role in acquired immunodeficiency syndrome (AIDS), multiple sclerosis (MS) and chronic fatigue immunodeficiency syndrome (CFIDS). This review provides a critical summary of research conducted on HHV-6.
A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis (2022)

In recent years, fuel cell (FC) technology has seen a promising increase in its proportion in stationary power production. Several pilot projects are in operation across the world, with the number of running hours steadily rising, either as stand-alone units or as part of integrated gas turbine–electric energy plants. FCs are a potential energy source with great efficiency and zero emissions. To ensure the best performance, they normally function within a confined temperature and humidity range; nevertheless, this makes the system difficult to regulate, resulting in defects and hastened deterioration. For diagnosis, there are two primary approaches: restricted input information, which gives an unobtrusive, rapid yet restricted examination, and advanced characterization, which provides a more accurate diagnosis but frequently necessitates invasive or delayed tests. Artificial Intelligence (AI) algorithms have shown considerable promise in providing accurate diagnoses with quick data collecting. This work focuses on software models that allow the user to evaluate many different possibilities in the shortest amount of time and is a vital method for proper and dynamic analysis of such entities. The artificial neural network, genetic algorithm, particle swarm optimization, random forest, support vector machine, and extreme learning machine are common AI approaches discussed in this review. This article examines the modern practice and provides recommendations for future machine learning methodologies in fuel cell diagnostic applications. In this study, these six AI tools are specifically explained with results for a better understanding of the fuel cell diagnosis. The conclusion suggests that these approaches are not only a popular and beneficial tool for simulating the nature of an FC system, but they are also appropriate for optimizing the operational parameters necessary for an ideal FC device. Finally, observations and ideas for future research, enhancements, and investigations are offered.