gebhart/patrepeval huggingface dataset:
Enhancing Marine Natural Product Structural Diversity and Bioactivity through Semisynthesis and Biocatalysis (2003)

In the last several decades the plants, animals and microbes from the marine environment have revealed a portion of what is clearly a tremendous resource for structurally diverse and bioactive secondary metabolites. Many of these extraordinarily sophisticated and bioactive natural products can be isolated in significant quantities without great difficulty. As a result these readily available bioactive natural products provide valuable starting materials for the rational generation of libraries of compounds prepared through semisynthesis and biocatalysis. A review of our work using marine natural products to generate rationally designed compound libraries and their biological activity against infectious diseases, cancer and neurological targets is presented. The marine natural products utilized to date as starting materials consist of compounds from a variety of structural classes and include: aureol, puupehenone, sarcophine, palinurin, and the manzamine alkaloids. The possibility to generate diverse bioactive products beginning with a marine natural product scaffold is a direct result of improvements made in the technologies to harvest samples from the ocean, purify and characterize complex natural products quickly and complete chemical reactions and biotransformations in parallel. As a result the vast resources of the ocean can now be utilized routinely to design and produce countless products to be evaluated as part of drug discovery and development programs.
Supplementary Figure 4 from miR-30-5p Functions as a Tumor Suppressor and Novel Therapeutic Tool by Targeting the Oncogenic Wnt/β-Catenin/BCL9 Pathway (2023)

&lt;p&gt;PDF file - 2071KB, MiR-30s treatment does not have a negative impact on bone disease in MM1S-Luc-Neo bearing mice.&lt;/p&gt;
Evaluation of Energy in Heated Water Vapor for the Application of Lung Volume Reduction in Patients with Severe Emphysema (2013)

Considering both total energy and vapor energy is important during the development of clinical vapor applications. For InterVapor, a closer study of both energy types justified a reduced target vapor-dosing range for lung volume reduction. The clinical implication is a potential improvement for benefiting the risk profile.
Microscopic gain analysis of modulation-doped GeSn/SiGeSn quantum wells: epitaxial design toward high-temperature lasing (2019)

Threshold carrier densities of GeSn quantum well (QW) lasers and the physical reason of low-temperature lasing of current GeSn laser are investigated through the comparison of threshold carrier densities of conventional III-V QW lasers. Electrons distributed over L-band is the main cause of decreased gain for GeSn QWs. To increase the gain (and improve the laser characteristics), a modulation-doped GeSn QW is proposed and the material gain is analyzed based on many-body theory for both qualitative and quantitative simulation. Significant gain increase can be expected for n-type modulation doping QWs. The doping condition for elevated temperature lasing is discussed and it was found that material gain curve similar to III-V QW is obtained for GeSn QW with n-type modulation doping of 6 × 10<sup>18</sup> cm<sup>-3</sup>. It was also found that unlike III-V QW lasers, n-type modulation doping is more effective for high-speed operation in terms of differential gain than p-type modulation doping.
Second Cancers After Squamous Cell Carcinoma and Adenocarcinoma of the Cervix (2008)

The second cancer profiles among cervical SCC and AC survivors mirror the similarities and differences in cofactors for these two histologies. Because smoking is not a cofactor for cervical AC, the increased lung cancer risk suggests a role for additional factors.
Cotreatment with a novel phosphoinositide analogue inhibitor and carmustine enhances chemotherapeutic efficacy by attenuating AKT activity in gliomas (2006)

In the current study, the authors characterized the efficacy of a new class of adjuvant chemotherapeutics that show promise in enhancing the efficacy of standard chemotherapy regimens in gliomas.
Neural Stem Cells for Transplantation (2000)

Neural stem cell (NSC) biology has generated a great deal of interest in recent years. It can inform on both intrinsic and extrinsic processes that underlie cell diversity within the central nervous system (CNS), and is a powerful method for improving understanding of cell ancestry and potential. An extension of this knowledge is apparent in applied neuroscience, in which defined stem-cell-derived populations offer vast therapeutic potential in the management of human CNS disease. Cells can now be isolated from the developing or adult rodent CNS, and expanded in culture, while retaining the capacity for differentiation into a wide variety of cellular phenotypes. In many cases, these may represent the founders of the CNS: multipotent NSCs (McKay, 1997). Similar cells may also be isolated from human primary fetal tissues. Ethical and practical constraints on the procurement of viable human tissue emphasizes the worth of NSCs in providing an plentiful alternative source of human neural cells.
Polymer architecture as key to unprecedented high-resolution 3D-printing performance: The case of biodegradable hexa-functional telechelic urethane-based poly-ε-caprolactone (2020)

Two-photon polymerization (2PP) is a high-resolution 3D-printing technology with a very rapidly expanding field of applications, including tissue engineering (TE). In this field, 2PP offers unprecedented possibilities for systematic studies of both cell–cell and cell–material interactions in 3D. For TE applications, the reliable production of biodegradable micro-scaffolds in porous, complex architectures is essential. However, the number of biodegradable materials that support the required level of spatial resolution is very limited, being a major bottleneck for the use of 2PP in the TE field. Herein, we introduce a hexa-functional urethane-based biodegradable precursor that overcomes the limitations associated with the high-resolution printing of current biodegradable precursors. The precursor is a telechelic urethane-based poly-ε-caprolactone (PCL) possessing three acrylate functionalities at each polymer end group which enables the reliable production of complex architectures owing to its superior physical properties as compared to the traditional di-acrylate terminated analogs. The newly developed hexa-functional telechelic urethane-based PCL reveals enhanced crosslinking kinetics and one order of magnitude higher Young’s modulus compared to the di-functional precursor (57.8 versus 6.3 MPa), providing an efficient and solvent-free 2PP processing at fast scanning speeds of up to 100 mm s−1 with unprecedented feature resolutions (143 ± 18 nm at 100 mm s−1 scanning speed). The crosslinked hexa-functional polymer combines strength and flexibility owing to the segregation between its hard polyacrylate and soft PCL segments, which makes it suitable for biological systems in contrast to the highly crosslinked and rigid structures typically manufactured by 2PP. Furthermore, it revealed lower degradation rate compared to its di-functional analog, which can be considered as an advantage in terms of biocompatibility due to the slower formation of acidic degradation products. Extracts of the developed polymers did not show a cytotoxic effect on the L929 fibroblasts as confirmed via ISO 10993-5 standard protocol. The presented precursor design constitutes a simple and effective approach that can be easily translated towards other biodegradable polymers for the manufacturing of biodegradable constructs with nano-scale precision, offering for the first time to use the true capabilities of 2PP for TE applications with the use of synthetic biodegradable polymers.
Photoreduction at illuminated p-type semiconducting silicon photoelectrodes. Evidence for Fermi level pinning (1980)

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTPhotoreduction at illuminated p-type semiconducting silicon photoelectrodes. Evidence for Fermi level pinningAndrew B. Bocarsly, Dana C. Bookbinder, Raymond N. Dominey, Nathan S. Lewis, and Mark S. WrightonCite this: J. Am. Chem. Soc. 1980, 102, 11, 3683–3688Publication Date (Print):May 1, 1980Publication History Published online1 May 2002Published inissue 1 May 1980https://doi.org/10.1021/ja00531a003RIGHTS & PERMISSIONSArticle Views1460Altmetric-Citations113LEARN 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 InReddit PDF (736 KB) Get e-Alerts Get e-Alerts
Exploiting sets of independent moves in VRP (2017)

Most heuristic methods for VRP and its variants are based on the partial exploration of large neighborhoods, typically by means of single, simple moves applied to the current solution. In this paper we define an extended concept of independent moves and show how even a very standard heuristic method can significantly improve when considering the simultaneous application of carefully chosen sets of moves. We show in particular that, when choosing a set such that the total cost variation is equal to the sum of the variations induced by each single move, the quality of solutions obtained is in general very high. When compared with numerical results obtained by some of the best available heuristics on challenging, large scale, problems, our simple algorithm equipped with the application of optimally chosen independent moves displayed very good quality.
Training Deep Generative Models in Highly Incomplete Data Scenarios with Prior Regularization (2021)

Deep generative frameworks including GANs and normalizing flow models have proven successful at filling in missing values in partially observed data samples by effectively learning –either explicitly or implicitly– complex, high-dimensional statistical distributions. In tasks where the data available for learning is only partially observed, however, their performance decays monotonically as a function of the data missingness rate. In high missing data rate regimes (e.g., 60% and above), it has been observed that state-of-the-art models tend to break down and produce unrealistic and/or semantically inaccurate data. We propose a novel framework to facilitate the learning of data distributions in high paucity scenarios that is inspired by traditional formulations of solutions to ill-posed problems. The proposed framework naturally stems from posing the process of learning from incomplete data as a joint optimization task of the parameters of the model being learned and the missing data values. The method involves enforcing a prior regularization term that seamlessly integrates with objectives used to train explicit and tractable deep generative frameworks such as deep normalizing flow models. We demonstrate via extensive experimental validation that the proposed framework outperforms competing techniques, particularly as the rate of data paucity approaches unity.
Small Molecule ONC201/TIC10 Induces Caspase-Dependent Apoptosis in Acute Lymphoblastic Leukemia Cells Via Modulation of Bcl-2 and IAP Family Proteins (2014)

Abstract ONC201/TIC10 is a potent small molecule anti-tumor agent in several types of solid tumors and lymphomas. ONC201/TIC10 is on track to enter clinical trials for patients with advanced cancer in 2014, with IND issued by the FDA in March, 2014. Early trials will evaluate the safety and efficacy of ONC201/TIC10 as a monoagent in hematological malignancies. In the current study, we evaluated the anti-cancer effects of the small molecule in Acute Lymphoblastic Leukemia (ALL). Analysis of cell viability by the CellTiter-Glo method revealed that ONC201/TIC10 treatment reduces the viability of three ALL cell lines (Reh, Jurkat, MOLT-4) in a dose- (2.5/5/10 μM) and time-dependent manner (24/48/72 h). We have previously reported that ONC201/TIC10-mediated reduction in cell viability and apoptosis in various types of solid tumors occurs at 60/72 h. Interestingly, ONC201/TIC10 reduces the viability of ALL cell lines within 24/48 h at the indicated doses. An inactive TIC10 isomer compound synthesized by Medkoo Biosciences with a structure related to the active ONC201/TIC10 compound does not reduce the viability of ALL cells. Sub-G1 analysis indicated that ONC201/TIC10 induces apoptosis in ALL cells and a pan-caspase inhibitor reduces ONC201/TIC10-mediated apoptosis. Western blot analysis was used to further investigate the mechanism of ONC201/TIC10-mediated apoptosis. ONC201/TIC10-mediated apoptosis involves PARP cleavage and caspase-9 activation. Anti-apoptotic Bcl-2 family members Bcl-2 and Bcl-xl are downregulated while the pro-apoptotic Bcl-2 family member Bim is upregulated in response to ONC201/TIC10 treatment. ONC201/TIC10 also downregulates the inhibitor of apoptosis (IAP) family proteins cIAP1 and cIAP2. We have previously shown that the anti-tumor effect of ONC201/TIC10 involves inhibition Akt and ERK phosphorylation resulting in Foxo3a activation and TRAIL-gene transcription. We observed inhibition of Akt phosphorylation upon ONC201/TIC10 treatment of ALL cells. Thus, ONC201/TIC10 holds promise as a novel agent for the treatment of ALL based on its robust activity in preclinical models of the disease. Our ongoing studies are evaluating the impact of this novel therapy on ALL cells with different translocations, and are introducing combination therapy with ONC201/TIC10 for ALL. Figure 1 Figure 1. Disclosures Allen: Oncoceutics: Employment, Equity Ownership, Patents &amp; Royalties. El-Deiry:Oncoceutics, Inc.: Equity Ownership, Patents &amp; Royalties.
Pharmacologically Augmented S -Nitrosylated Hemoglobin Improves Recovery From Murine Subarachnoid Hemorrhage (2010)

Targeted S-nitrosylation of hemoglobin improved outcome parameters, including vessel diameter, tissue blood flow, cortical tissue Po(2), and neurologic function in a murine SAH model. Augmenting endogenous Po(2)-dependent delivery of NO bioactivity to selectively dilate the compromised cerebral vasculature has significant clinical potential in the treatment of SAH.
Holistic digital gamma-ray spectroscopy methods and instrumentation for high-throughput high-resolution applications (2023)

Method of real-time adaptive digital pulse signal processing for high count rate gamma-ray spectroscopy applications includes receiving a preamplifier signal at a pulse deconvolver, the preamplifier signal including resolution deterioration resulting from pulse pile-up. The method further includes generating a deconvoluted signal, by the pulse deconvolver, from the preamplifier signal, the deconvoluted signal having less resolution deterioration as compared to the received preamplifier signal. The method furthermore includes shaping of the deconvoluted signal by a trapezoid filter, the shaping comprising adjusting a shaping parameter of the trapezoid filter for an incoming signal based on a time separation from a subsequent incoming signal.
Tuple-level vs Element-level Classification. (1992)

A multilevel relational database represents information in a multilevel state of the world, which is the knowledge of the truth value of a statement with respect to a security level. The security semantics of a data classification scheme specifies the way that the classification of data in a multilevel relational database corresponds to the classification of statements in a multilevel state of the world. We formalize the security semantics of tuple-level and element-level data classification schemes, and show that they have the same expressive power. We derive entity, referential, and polyinstantiation integrity properties from the security semantics, and show that existing approaches to polyinstantiation integrity are inappropriate. Our results provide the foundation for the multilevel relational model, its integrity properties, its operational semantics, and its implementation.
Genome-wide identification of grain filling genes regulated by the OsSMF1 transcription factor in rice (2017)

The results of this study suggest that OsSMF1 is one of the key transcription factors that functions in a wide range of seed developmental processes with different specific binding affinities for the three DNA-binding motifs.
Special Series on Harsh Chips [Guest editors' introduction] (2014)

This introduction to the special series discusses the emerging field of harsh-environment-capable chips, or harsh chips. It also introduces the first two Harsh Chips articles in the issue.
Proportional fairness in wireless LANs and ad hoc networks (2005)

The paper considers scenarios in which fairness and efficiency are two conflicting objectives in wireless networks, and investigates the use of a proportional fairness objective to strike a balance between the two objectives. We explain the physical meaning of proportional fairness in a wireless network and give an analysis showing that proportional fairness is equivalent or close to max-min fairness in terms of air-time usage (as opposed to bandwidth usage). For infrastructure WLANs, two approaches to achieving proportional fairness are discussed. For ad hoc networks, achieving proportional fairness is more complex and requires global information on contention among different traffic flows. We propose and evaluate the use of a distributed max-min air-time allocation algorithm to approximate the proportional fairness objective.
Aluminum layer formation on aluminum nitride ceramic by irradiation with a TEA CO/sub 2/ laser (2002)

Recently aluminum nitride (AlN) has become an important material for substrates of high power opto-electronic devices such as laser diodes due to its attractive thermo-physical properties such as high thermal conductivity, high electrical resistivity, and low thermal expansion close to that of silicon. AlN is one of the covalent bonding ceramic and sublimates at high temperature. The irradiation with the TEA CO/sub 2/ laser induces the decomposition and leaves the metallic Al layer. In the present work, the variation of morphology and electrical resistivity of the AlN surface irradiated with the TEA CO/sub 2/ laser are investigated.
1617: A Novel Microfluidics Device for Male Subfertility Screening (2006)

You have accessJournal of UrologyModerated Poster, Wednesday, May 24, 2006, 1:00 - 3:00 pm1 Apr 20061617: A Novel Microfluidics Device for Male Subfertility Screening Matthew C. McCormack, Stewart W. McCallum, and Barry Behr Matthew C. McCormackMatthew C. McCormack More articles by this author , Stewart W. McCallumStewart W. McCallum More articles by this author , and Barry BehrBarry Behr More articles by this author View All Author Informationhttps://doi.org/10.1016/S0022-5347(18)33809-6AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "1617: A Novel Microfluidics Device for Male Subfertility Screening." The Journal of Urology, 175(4S), p. 521 © 2016 by American Urological AssociationFiguresReferencesRelatedDetails Volume 175Issue 4SApril 2006Page: 521 Advertisement Copyright & Permissions© 2016 by American Urological AssociationMetricsAuthor Information Matthew C. McCormack More articles by this author Stewart W. McCallum More articles by this author Barry Behr More articles by this author Expand All Advertisement PDF downloadLoading ...
Primary prevention of sudden death in idiopathic dilated cardiomyopathy (2018)

Idiopathic dilated cardiomyopathy is characterized by early ventricular enlargement and systolic contractile dysfunction with congestive heart failure not secondary to recognizable causes. Symptoms of congestive heart failure develop at a later stage, usually between 18 and 50 years of age, although they may occasionally occur earlier as a first manifestation of the underlying disease. Mechanisms of life-threatening arrhythmias are facilitated by subendocardial scarring, electrolyte unbalance, stretch-induced electrophysiological changes, autonomic impairment, conduction delay, or proarrhythmic effects of drug therapy. Sudden death may occur as a consequence of ventricular fibrillation, but electromechanical dissociation or bradycardia may also be a possible underlying cause. Most of the clinical characterization of idiopathic dilated cardiomyopathy is drawn from studies also enrolling patients with cardiomyopathies secondary to variable underlying conditions. Secular trends have improved the ability of early diagnosis, and the therapeutic strategies used to prevent sudden death. Among them are angiotensin-converting enzyme inhibitors, beta blockers, and mineralocorticoids/aldosterone receptor antagonists. The role of implantable cardioverter defibrillator (ICD) therapy for the primary prevention of all-cause mortality is controversial with some studies showing and others questioning the benefit of ICD in this population. Survivors of near-fatal arrhythmias have a high risk of recurrence, which may often be fatal. Idiopathic dilated cardiomyopathy contributes to less than 15% of all such patients. Previous randomized studies conducted in large heterogeneous populations showed that ICD therapy is beneficial and improves survival by about 30%. This therapy is currently recommended for all survivors of a near-fatal arrhythmia regardless of the underlying substrate.
Learning to learn with quantum neural networks via classical neural\n networks (2019)

Quantum Neural Networks (QNNs) are a promising variational learning paradigm\nwith applications to near-term quantum processors, however they still face some\nsignificant challenges. One such challenge is finding good parameter\ninitialization heuristics that ensure rapid and consistent convergence to local\nminima of the parameterized quantum circuit landscape. In this work, we train\nclassical neural networks to assist in the quantum learning process, also know\nas meta-learning, to rapidly find approximate optima in the parameter landscape\nfor several classes of quantum variational algorithms. Specifically, we train\nclassical recurrent neural networks to find approximately optimal parameters\nwithin a small number of queries of the cost function for the Quantum\nApproximate Optimization Algorithm (QAOA) for MaxCut, QAOA for\nSherrington-Kirkpatrick Ising model, and for a Variational Quantum Eigensolver\nfor the Hubbard model. By initializing other optimizers at parameter values\nsuggested by the classical neural network, we demonstrate a significant\nimprovement in the total number of optimization iterations required to reach a\ngiven accuracy. We further demonstrate that the optimization strategies learned\nby the neural network generalize well across a range of problem instance sizes.\nThis opens up the possibility of training on small, classically simulatable\nproblem instances, in order to initialize larger, classically intractably\nsimulatable problem instances on quantum devices, thereby significantly\nreducing the number of required quantum-classical optimization iterations.\n