gebhart/patrepeval huggingface dataset:
Self-Similar Dynamics of a Flexible Ring Polymer in a Fixed Obstacle Environment: A Coarse-Grained Molecular Model (2009)

In this contribution we concern ourselves with an interesting problem, namely, the dynamics of ideal flexible ring polymers constrained in an array of fixed obstacles. The fundamental issue in this problem is to understand how a topologically constrained polymer chain is able to relax its conformation in the absence of chain ends. The key physics was provided in an elegant scaling theory by Rubinstein and co-workers (Obukhov, S. P.; Rubinstein, M.; Duke, T. Dynamics of a Ring Polymer in a Gel. Phys. Rev. Lett. 1994, 73, 1263−1267). In this work we develop a coarse-grained mean-field model based on the physical arguments of the scaling theory and derive constitutive relations for rings in fixed obstacle and melt environments. The model is composed of three distinct steps. In the first step the dynamics of an arbitrary section of a ring chain is worked out based on fractal Blob−Spring (BS) dynamics, and the center of mass diffusion and the relaxation spectrum of this section are determined. In the second step the center of mass diffusion obtained using the BS dynamics is used to model the one-dimensional diffusion of the section in a topologically constrained environment. In the final step we invoke the idea of dynamic self-similarity and argue that the dynamics described in the first and the second step, for any arbitrary section of the chain, applies to all sections of the chain. The constitutive relation is obtained consequently as the superposition of dynamic response of all sections of the ring chain.
B-Lymphocyte Cells in Lymphatic Tissue of the Duck, Anas platyrhynchos (1981)

SUMMARY Fluorescent-antibody techniques were used to identify Blymphocytes in the duck. These cells were restricted primarily to the germinal centers of a number of lymphatic tissues. In some instances, these germinal centers were associated with stromal or vessel-like structures.
Pathogenic bacterium Helicobacter pylori alters the expression profile of p53 protein isoforms and p53 response to cellular stresses (2012)

The p53 protein plays a central role in the prevention of tumorigenesis. Cellular stresses, such as DNA damage and aberrant oncogene activation, trigger induction of p53 that halts cellular proliferation and allows cells to be repaired. If cellular damage is beyond the capability of the repair mechanisms, p53 induces apoptosis or cell cycle arrest, preventing damaged cells from becoming cancerous. However, emerging evidence suggests that the function of p53 needs to be considered as isoform-specific. Here, we report that the expression profile of p53 can be shifted toward inhibitory p53 isoforms by the pathogenic bacterium Helicobacter pylori, which is known for its strong association with gastric cancer and gastric mucosa-associated lymphoid tissue lymphoma. We found that interaction of H. pylori with gastric epithelial cells, mediated via the cag pathogenicity island, induces N-terminally truncated Δ133p53 and Δ160p53 isoforms in human cells. Induction of an orthologous p53 isoform, Δ153p53, was also found in H. pylori-infected Mongolian gerbils. The p53 isoforms inhibit p53 and p73 activities, induce NF-κB, and increase survival of infected cells. Expression of Δ133p53, in response to H. pylori infection, is regulated by phosphorylation of c-Jun and activation of activator protein-1-dependent transcription. Together, these results provide unique insights into the regulation of p53 protein and may contribute to the understanding of tumorigenesis associated with H. pylori.
Addressing cancer signal transduction pathways with antisense and siRNA oligonucleotides (2020)

Signal transduction pathways play key roles in the initiation, progression and dissemination of cancer. Thus, signaling molecules are attractive targets for cancer therapeutics and enormous efforts have gone into the development of small molecule inhibitors of these pathways. However, regrettably, there has been only moderate progress to date, primarily in connection with the RAS signaling pathway. Oligonucleotide-based drugs potentially offer several advantages for addressing signaling pathways, including their exquisite selectivity and their ability to exploit both enzymatic and nonenzymatic targets. Nonetheless, there are problems inherent in the oligonucleotide approach, not the least being the challenge of effectively delivering these complex molecules to intracellular sites within tumors. This survey article will provide a selective review of recent studies where oligonucleotides were used to address cancer signaling and will discuss both positive aspects and limitations of those studies. This will be set in the context of an overview of various cancer signaling pathways and small molecule approaches to regulate those pathways. The survey will also evaluate the challenges and opportunities implicit in the oligonucleotide-based approach to cancer signaling and will point out several possibilities for future research.
Real-time prediction of the microseismic cloud size: A comparison between a physics-based model and a machine learning approach (2021)

The spatiotemporal distribution of hydraulic fracturinginduced microseismicity is complicated and depends on various mechanical and diffusional parameters. Hydraulic fracture modeling can aid in understanding fluid-induced microseismicity. Nevertheless, the interaction of several physical processes occurring within and around the fracture adds complexity in developing tools for microseismic prediction. We apply a physics-based approach relying on diffusivity estimates derived from the microseismic observations and a convolutional neural network (CNN) trained with the engineering curves to forecast the microseismic cloud size in real-time.
These Maps are Made for Walking: Real-Time Terrain Property Estimation for Mobile Robots (2022)

The equations of motion governing mobile robots are dependent on terrain properties such as the coefficient of friction, and contact model parameters. Estimating these properties is thus essential for robotic navigation. Ideally any map estimating terrain properties should run in real time, mitigate sensor noise, and provide probability distributions of the aforementioned properties, thus enabling risk-mitigating navigation and planning. This letter addresses these needs and proposes a Bayesian inference framework for semantic mapping which recursively estimates both the terrain surface profile and a probability distribution for terrain properties using data from a single RGB-D camera. The proposed framework is evaluated in simulation against other semantic mapping methods and is shown to outperform these state-of-the-art methods in terms of correctly estimating simulated ground-truth terrain properties when evaluated using a precision-recall curve and the Kullback-Leibler divergence test. Additionally, the proposed method is deployed on a physical legged robotic platform in both indoor and outdoor environments, and we show our method correctly predicts terrain properties in both cases. The proposed framework runs in real-time and includes a ROS interface for easy integration.
Prefrontal DA Transmission at D1 Receptors and the Pathology of Schizophrenia (2003)

The current view on the dopamine (DA) hypothesis of schizophrenia postulates a cortical/subcortical imbalance: subcortical mesolimbic DA projections might be hyperactive, resulting in hyperstimulation of D2 receptors and positive symptoms, whereas mesocortical DA projections to the prefrontal cortex might be hypoactive, resulting in hypostimulation of D1 receptors, negative symptoms, and cognitive impairment. Although the subcortical abnormalities are relatively well established now, the evidence for cortical hypodopaminergia is just emerging. This article will review current evidence for prefrontal hypodopaminergia in schizophrenia, with special emphasis on positron emission tomography (PET) studies measuring cortical D1 receptors in schizophrenia. The presentation of the clinical data will be introduced by a brief overview of the function of prefrontal DA systems, both at the cellular and cognitive level. The impact of antipsychotic drugs on prefrontal DA function will also be reviewed. We will conclude with the formulation of several models of altered prefrontal DA transmission at D1 receptors in schizophrenia.
Combined Use of Diffusion Tractography and Advanced Intraoperative Imaging for Resection of Cervical Intramedullary Spinal Cord Neoplasms: A Case Series and Technical Note (2019)

This is a proof of concept application where multi-modal imaging technology was utilized for safest maximal ISCN resection.
Growth of triglycine sulfate (TGS) crystals by solution technique (1982)

The growth of crystals from solution is greatly influenced by buoyancy driven convection. In a low-g environment, convection is greatly suppressed and diffusion becomes the predominant mechanism for thermal and mass transport. An experiment to grow TGS crystals by solution technique during the orbital Spacelab III mission has been designed. Crystals are grown by a new and unique technique of extracting heat from the crystal through a sting. The cooling at the sting tip is responsible for the desired supersaturation near the growing crystal. Calculations indicate that the cooled sting technique for solution crystal growth is necessary in low-g to maintain a maximum growth rate of 1 mm/day. Results of groundbased work in support of the flight experiment are discussed.
π covalency in the halogen bond (2020)

Halogen bonds are a highly directional class of intermolecular interactions widely employed in chemistry and chemical biology. This linear interaction is commonly viewed to be analogous to the hydrogen bond because hydrogen bonding models also intuitively describe the σ-symmetric component of halogen bonding. The possibility of π-covalency in a halogen bond is not contemplated in any known models. Here we present evidence of π-covalency being operative in halogen bonds formed between chloride and halogenated triphenylamine-based radical cations. We reach this conclusion through computational analysis of chlorine K-edge X-ray absorption spectra recorded on these halogen bonded pairs. In light of this result, we contend that halogen bonding is better described by analogy to metal coordination bonds rather than hydrogen bonds. Our revised description of the halogen bond suggests that these interactions could be employed to influence the electronic properties of conjugated molecules in unique ways.
Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting (2020)

Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water resources. In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). For this purpose, daily observational flow data are used during the period 2012–2018, and all models are coded in Python software programming language. Only delays of streamflow time series are used as the input of models. Then, based on the correlation coefficient (CC), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NS), results of deep-learning architectures are compared with one another and with an artificial neural network (ANN) with two hidden layers. Results indicate that the accuracy of deep-learning RNN methods are better and more accurate than ANN. Among methods used in deep learning, the LSTM method has the best accuracy, namely, the simulated streamflow to the dam reservoir with 90% accuracy in the training stage and 87% accuracy in the testing stage. However, the accuracies of ANN in training and testing stages are 86% and 85%, respectively. Considering that the Ermenek Dam is used for hydroelectric purposes and energy production, modeling inflow in the most realistic way may lead to an increase in energy production and income by optimizing water management. Hence, multi-percentage improvements can be extremely useful. According to results, deep-learning methods of RNNs can be used for estimating streamflow to the Ermenek Dam reservoir due to their accuracy.
Antiangiogenic activity of berberine is mediated through the downregulation of hypoxia-inducible factor-1, VEGF, and proinflammatory mediators (2011)

Berberine, a naturally occurring isoquinoline alkaloid, is present in a number of important medicinal plants. Berberine has a wide range of biochemical and pharmacological effects, including anticancer effects. In this study, we elucidated the mechanism of antiangiogenic activity of berberine using in vivo and in vitro models. In vivo antiangiogenic activity was studied using B16F-10 melanoma cells and induced capillary formation in C57BL/6 mice. Berberine, at 10 mg/kg body weight, showed significant inhibition in tumor-directed capillary formation and in various proangiogenic factors, such as vascular endothelial growth factor (VEGF), and proinflammatory mediators, such as interleukin (IL)-1β, IL-6, tumor necrosis factor alpha (TNF-α), and granulocyte macrophage colony-stimulating factor (GM-CSF), which are involved in tumor angiogenesis. At the same time, it could also increase antitumor factors, such as IL-2 and tissue-inhibitor metalloproteinase (TIMP) levels in the serum. Berberine could also inhibit endothelial motility, migration, tube formation, and vessel sprouting from rat aortic ring in vitro. Further, berberine inhibited various transcription factors involved in tumor development and angiogenesis, such as NF-ĸB, c-Fos, CREB, and ATF-2. mRNA expression levels of proangiogenic factors, such as cyclooxygenase-2 (COX-2), inducible nitric oxide synthase (iNOS), and hypoxia-inducible factor (HIF), were also downregulated in tumor cells after treatment with berberine. Drastically elevated expressions of HIF and VEGF mRNA by tumor cells under hypoxic conditions were also decreased after treatment with berberine. This result clearly demonstrates that the antiangiogenic activity of berberine is mainly mediated through the inhibition of various proinflammatory and pro-angiogenic factors and the major ones are HIF, VEGF, COX-2, NO, NF-ĸB, and proinflammatory cytokines.
Endogenous Retroviruses Transcriptional Modulation After Severe Infection, Trauma and Burn (2019)

Although human endogenous retroviruses (HERVs) expression is a growing subject of interest, no study focused before on specific endogenous retroviruses loci activation in severely injured patients. Yet, HERV reactivation is observed in immunity compromised settings like some cancers and auto-immune diseases. Our objective was to assess the transcriptional modulation of HERVs in burn, trauma and septic shock patients. We analyzed HERV transcriptome with microarray data from whole blood samples of a burn cohort (<i>n</i> = 30), a trauma cohort (<i>n</i> = 105) and 2 septic shock cohorts (<i>n</i> = 28, <i>n</i> = 51), and healthy volunteers (HV, <i>n</i> = 60). We described expression of the 337 probesets targeting HERV from U133 plus 2.0 microarray in each dataset and then we compared HERVs transcriptional modulation of patients compared to healthy volunteers. Although all 4 cohorts contained critically ill patients, the majority of the 337 HERVs was not expressed (around 74% in mean). Each cohort had differentially expressed probesets in patients compared to HV (from 19 to 46). Strikingly, 5 HERVs were in common in all types of severely injured patients, with 4 being up-modulated in patients. We highlighted co-expressed profiles between HERV and nearby CD55 and CD300LF genes as well as autonomous HERV expression. We suggest an inflammatory-specific HERV transcriptional response, and importantly, we introduce that the HERVs close to immunity-related genes might have a role on its expression.
Facile Synthesis of CeO2 Nanosheets Decorated upon BiOI Microplate: A Surface Oxygen Vacancy Promoted Z-Scheme-Based 2D-2D Nanocomposite Photocatalyst with Enhanced Photocatalytic Activity (2017)

Construction of a visible light driven Z-scheme-based photocatalytic system is a hot topic of research because of the potential to alleviate both energy and environmental issues. In the present study a series of surface vacancy mediated BiOI–CeO2 nanocomposites were prepared and their crystallographic, morphological, optical, and electrochemical behavior were characterized through XRD, TEM, FESEM, UV–vis DRS, PL, ESI, and Mott–Schottky techniques. From the XPS analysis, it could be said that a high amount of oxygen defects and metallic bismuth were present on the composite than neat CeO2 which was the major cause of enhancement in the photocatalytic activity. Further the photocatalytic efficiency of the as prepared samples was tested toward RhB decolorization/phenol oxidation as well as for O2 gas evolution. It was observed that 40 wt % BiOI–CeO2 nanocomposite exhibited highest photocatalytic activity among neat and other composites, i.e., 89% decolorization of 100 ppm RhB and produces 323 μmol/2h of O2 under visible light illumination. To justify the enhanced photocatalytic activity of the material a Z-scheme-based charge transfer mechanism was proposed. Where surface oxygen vacancy exists on CeO2, I3–/I– reversibility pair through Bi metal on BiOI, scavenger experiment, PL spectra, and Nyquist plot provide solid evidence toward a Z-scheme charge transfer pathway. This work will provide some useful information in course of developing of Z-scheme-based photocatalyst without an external mediator.
RACE: A Rate Adaptive Channel Estimation Approach for Millimeter Wave MIMO Systems (2016)

In this paper, we consider the channel estimation problem in millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent sparse nature of the mmWave channel, we develop a novel rate-adaptive channel estimation (RACE) algorithm, which can adaptively adjust the number of required channel measurements based on an expected probability of estimation error (PEE). To this end, we design a maximum likelihood (ML) estimator to optimally extract the path information and the associated probability of error from the increasing number of channel measurements. Based on the ML estimator, the algorithm is able to measure the channel using a variable number of beam patterns until the receiver believes that the estimated direction is correct. This is in contrast to the existing mmWave channel estimation algorithms, in which the number of measurements is typically fixed. Simulation results show that the proposed algorithm can significantly reduce the number of channel estimation measurements while still retaining a high level of accuracy, compared to existing multi-stage channel estimation algorithms.
Data Report: Hydraulic Conductivity Measurements on Discrete Samples Collected from Leg 141, Site 863 (1995)

Measurements made on samples collected across a prominent zone of cementation occurring at depths below ~400 mbsf at Site 863 suggest that this zone is associated with a 100 fold reduction in hydraulic conductivity. The cements are related to significant smectite and zeolite deposition and cause sharp changes in sonic velocity, porosity, and thermal conductivity. The current pore fluid chemistry profile suggests cementation is continuing in-situ at the present time.
On the use of frame-based slice size for the robust transmission of MPEG video over ATM networks (2000)

We study the performance requirements for the transport of MPEG video streams over ATM networks. A control scheme is introduced to limit the propagation of impairments through MPEG-based video sequences as a result of cell losses. The scheme assigns different slice sizes to the video frames based on their type and order of appearance within the group of pictures. The main aim of the proposed scheme is to improve the robustness of the video delivery process at a minimum cost. Our results demonstrate the effectiveness of this scheme in reducing the propagation of impairments due to the loss of information during the transmission of MPEG video streams over ATM networks.
The remote organ effects of acute kidney injury in a porcine model (2014)

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
Multiple Myeloma Patient Tumors With High Levels of Cereblon Exon-10 Deletion Splice Variant Upregulate Clinically Targetable Pro-Inflammatory Cytokine Pathways (2022)

Immunomodulatory drugs (IMiDs), including lenalidomide and pomalidomide, are used in the routine treatment for multiple myeloma (MM) patients. Cereblon (CRBN) is the direct molecular target of IMiDs. While CRBN is not an essential gene for MM cell proliferation, the frequency of CRBN genetic aberrations, including mutation, copy number loss, and exon-10 (which includes a portion of the IMiD-binding domain) splicing, have been reported to incrementally increase in later-line patients. CRBN exon-10 splicing has also been shown to be associated with decreased progression-free survival in both newly diagnosed and relapsed refractory MM patients. Although we did not find significant general splicing defects among patients with CRBN exon-10 splice variant when compared to those expressing the full-length transcript, we identified upregulated TNFA signaling via NFKB, inflammatory response, and IL-10 signaling pathways in patients with exon-10 splice variant across various data sets-all potentially promoting tumor growth via chronic growth signals. We examined master regulators that mediate transcriptional programs in CRBN exon-10 splice variant patients and identified BATF, EZH2, and IKZF1 as the key candidates across the four data sets. Upregulated downstream targets of BATF, EZH2, and IKZF1 are components of TNFA signaling via NFKB, IL2/STAT5 signaling pathways, and IFNG response pathways. Previously, BATF-mediated transcriptional regulation was associated with venetoclax sensitivity in MM. Interestingly, we found that an EZH2 sensitivity gene expression signature also correlated with high BATF or venetoclax sensitivity scores in these tumors. Together, these data provide a rationale for investigating EZH2 inhibitors or venetoclax in combination with the next generation CRBN-targeting agents, such as CELMoDs, for patients overexpressing the CRBN exon-10 splice variant.
Fake News Early Detection (2020)

Massive dissemination of fake news and its potential to erode democracy has increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that aim to detect fake news by exploring how it propagates on social networks. Nevertheless, to detect fake news at an early stage, i.e., when it is published on a news outlet but not yet spread on social media, one cannot rely on news propagation information as it does not exist. Hence, there is a strong need to develop approaches that can detect fake news by focusing on news content. In this article, a theory-driven model is proposed for fake news detection. The method investigates news content at various levels: lexicon-level, syntax-level, semantic-level, and discourse-level. We represent news at each level, relying on well-established theories in social and forensic psychology. Fake news detection is then conducted within a supervised machine learning framework. As an interdisciplinary research, our work explores potential fake news patterns, enhances the interpretability in fake news feature engineering, and studies the relationships among fake news, deception/disinformation, and clickbaits. Experiments conducted on two real-world datasets indicate the proposed method can outperform the state-of-the-art and enable fake news early detection when there is limited content information.