gebhart/patrepeval huggingface dataset:
Droplet-based microtumor model to assess cell-ECM interactions and drug resistance of gastric cancer cells (2017)

Gastric cancer (GC) is a common aggressive malignant tumor with high incidence and mortality worldwide. GC is classified into intestinal and diffuse types according to the histo-morphological features. Because of distinctly different clinico-pathological features, new cancer therapy strategies and in vitro preclinical models for the two pathological variants of GC is necessary. Since extracellular matrix (ECM) influence the biological behavior of tumor cells, we hypothesized that GC might be more similarly modeled in 3D with matrix rather than in 2D. Herein, we developed a microfluidic-based a three-dimensional (3D) in vitro gastric cancer model, with subsequent drug resistance assay. AGS (intestinal type) and Hs746T (diffuse type) gastric cancer cell lines were encapsulated in collagen beads with high cellular viability. AGS exhibited an aggregation pattern with expansive growth, whereas Hs746T showed single-cell-level infiltration. Importantly, in microtumor models, epithelial-mesenchymal transition (EMT) and metastatic genes were upregulated, whereas E-cadherin was downregulated. Expression of ß-catenin was decreased in drug-resistant cells, and chemosensitivity toward the anticancer drug (5-FU) was observed in microtumors. These results suggest that in vitro microtumor models may represent a biologically relevant platform for studying gastric cancer cell biology and tumorigenesis, and for accelerating the development of novel therapeutic targets.
Neurosteroid biosynthesis down‐regulation and changes in GABA<sub>A</sub>receptor subunit composition: a<i>biomarker axis</i>in stress‐induced cognitive and emotional impairment (2017)

By rapidly modulating neuronal excitability, neurosteroids regulate physiological processes, such as responses to stress and development. Excessive stress affects their biosynthesis and causes an imbalance in cognition and emotions. The progesterone derivative, allopregnanolone (Allo) enhances extrasynaptic and postsynaptic inhibition by directly binding at GABA<sub>A</sub> receptors, and thus, positively and allosterically modulates the function of GABA. Allo levels are decreased in stress-induced psychiatric disorders, including depression and post-traumatic stress disorder (PTSD), and elevating Allo levels may be a valid therapeutic approach to counteract behavioural dysfunction. While benzodiazepines are inefficient, selective serotonin reuptake inhibitors (SSRIs) represent the first choice treatment for depression and PTSD. Their mechanisms to improve behaviour in preclinical studies include neurosteroidogenic effects at low non-serotonergic doses. Unfortunately, half of PTSD and depressed patients are resistant to current prescribed 'high' dosage of these drugs that engage serotonergic mechanisms. Unveiling novel biomarkers to develop more efficient treatment strategies is in high demand. Stress-induced down-regulation of neurosteroid biosynthesis and changes in GABA<sub>A</sub> receptor subunit expression offer a putative biomarker axis to develop new PTSD treatments. The advantage of stimulating Allo biosynthesis relies on the variety of neurosteroidogenic receptors to be targeted, including TSPO and endocannabinoid receptors. Furthermore, stress favours a GABA<sub>A</sub> receptor subunit composition with higher sensitivity for Allo. The use of synthetic analogues of Allo is a valuable alternative. Pregnenolone or drugs that stimulate its levels increase Allo but also sulphated steroids, including pregnanolone sulphate which, by inhibiting NMDA tonic neurotransmission, provides neuroprotection and cognitive benefits. In this review, we describe current knowledge on the effects of stress on neurosteroid biosynthesis and GABA<sub>A</sub> receptor neurotransmission and summarize available pharmacological strategies that by enhancing neurosteroidogenesis are relevant for the treatment of SSRI-resistant patients. Linked Articles This article is part of a themed section on Pharmacology of Cognition: a Panacea for Neuropsychiatric Disease? To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.19/issuetoc.
Genetic studies of the molecular basis of whooping cough. (1985)

The etiologic agent of whooping cough, Bordetella pertussis, synthesizes several biochemically and biologically active factors believed to contribute to the disease process. They include filamentous hemagglutinin (FHA), pertussis toxin (PTX), adenylate cyclase toxin (AC), and hemolysin (HLY). We have used a genetic approach to evaluate the contribution of these factors to the virulence of B. pertussis. A series of B. pertussis mutants prepared by transposon Tn5 insertion mutagenesis were characterized and determined to be specifically deficient in the elaboration of these factors. The wild type strain and the mutants were tested for their ability to produce a lethal infection in the classical infant mouse intranasal infection model. The dose of organisms required to cause lethal infection in 50% of the animals (LD50) for the wild type strain was 2 X 10(3) bacteria. A mutant deficient in production of the proposed adherence factor, FHA, was equivalent in virulence to the wild type strain (BP353, LD50 = 8 X 10(3)). In contrast, mutants deficient in production of PTX alone (BP357, LD50 = 5 X 10(6)), AC and HLY (BP348, LD50 greater than 3 X 10(7)), or all known toxins and virulence factors (BP347, LD50 greater than 3 X 10(7)) were severely impaired in their ability to cause a lethal infection. Our data indicate the hemolysin and adenylate cyclase as well as pertussis toxin are important virulence factors and provide the basis for a working model of the molecular basis of the disease caused by Bordetella pertussis.
Protein Kinase Cδ Is Required for Survival of Cells Expressing Activated p21 (2007)

Inhibition of protein kinase C (PKC) activity in transformed cells and tumor cells containing activated p21(RAS) results in apoptosis. To investigate the pro-apoptotic pathway induced by the p21(RAS) oncoprotein, we first identified the specific PKC isozyme necessary to prevent apoptosis in the presence of activated p21(RAS). Dominant-negative mutants of PKC, short interfering RNA vectors, and PKC isozyme-specific chemical inhibitors directed against the PKCdelta isozyme demonstrated that PKCdelta plays a critical role in p21(RAS)-mediated apoptosis. An activating p21(RAS) mutation, or activation of the phosphatidylinositol 3-kinase (PI3K) Ras effector pathway, increased the levels of PKCdelta protein and activity in cells, whereas inhibition of p21(RAS) activity decreased the expression of the PKCdelta protein. Activation of the Akt survival pathway by oncogenic Ras required PKCdelta activity. Akt activity was dramatically decreased after PKCdelta suppression in cells containing activated p21(RAS). Conversely, constitutively activated Akt rescued cells from apoptosis induced by PKCdelta inhibition. Collectively, these findings demonstrate that p21(RAS), through its downstream effector PI3K, induces PKCdelta expression and that this increase in PKCdelta activity, acting through Akt, is required for cell survival. The p21(RAS) effector molecule responsible for the initiation of the apoptotic signal after suppression of PKCdelta activity was also determined to be PI3K. PI3K (p110(C)(AAX), where AA is aliphatic amino acid) was sufficient for induction of apoptosis after PKCdelta inhibition. Thus, the same p21(RAS) effector, PI3K, is responsible for delivering both a pro-apoptotic signal and a survival signal, the latter being mediated by PKCdelta and Akt. Selective suppression of PKCdelta activity and consequent induction of apoptosis is a potential strategy for targeting of tumor cells containing an activated p21(RAS).
Mind-Body Interaction and the Conservation of Energy (1986)

Critique de l'argument selon lequel l'interaction esprit-corps violerait le principe de conservation de l'energie (en raison du saut implique entre etats du cerveau avant et apres l'evenement mental)
A new approach to bot detection: striking the balance between precision and recall (2016)

The presence of bots has been felt in many aspects of social media. Twitter, one example of social media, has especially felt the impact, with bots accounting for a large portion of its users. These bots have been used for malicious tasks such as spreading false information about political candidates and inflating the perceived popularity of celebrities. Furthermore, these bots can change the results of common analyses performed on social media. It is important that researchers and practitioners have tools in their arsenal to remove them. Approaches exist to remove bots, however they focus on precision to evaluate their model at the cost of recall. This means that while these approaches are almost always correct in the bots they delete, they ultimately delete very few, thus many bots remain. We propose a model which increases the recall in detecting bots, allowing a researcher to delete more bots. We evaluate our model on two real-world social media datasets and show that our detection algorithm removes more bots from a dataset than current approaches.
Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles (2009)

Saliva is a readily accessible and informative biofluid, making it ideal for the early detection of a wide range of diseases including cardiovascular, renal, and autoimmune diseases, viral and bacterial infections and, importantly, cancers. Saliva-based diagnostics, particularly those based on metabolomics technology, are emerging and offer a promising clinical strategy, characterizing the association between salivary analytes and a particular disease. Here, we conducted a comprehensive metabolite analysis of saliva samples obtained from 215 individuals (69 oral, 18 pancreatic and 30 breast cancer patients, 11 periodontal disease patients and 87 healthy controls) using capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS). We identified 57 principal metabolites that can be used to accurately predict the probability of being affected by each individual disease. Although small but significant correlations were found between the known patient characteristics and the quantified metabolites, the profiles manifested relatively higher concentrations of most of the metabolites detected in all three cancers in comparison with those in people with periodontal disease and control subjects. This suggests that cancer-specific signatures are embedded in saliva metabolites. Multiple logistic regression models yielded high area under the receiver-operating characteristic curves (AUCs) to discriminate healthy controls from each disease. The AUCs were 0.865 for oral cancer, 0.973 for breast cancer, 0.993 for pancreatic cancer, and 0.969 for periodontal diseases. The accuracy of the models was also high, with cross-validation AUCs of 0.810, 0.881, 0.994, and 0.954, respectively. Quantitative information for these 57 metabolites and their combinations enable us to predict disease susceptibility. These metabolites are promising biomarkers for medical screening. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0178-y) contains supplementary material, which is available to authorized users.
Sleep Staging Monitoring Based on Sonar Smartphone Technology (2019)

This paper presents the validation results of a new non-contact ultrasonic technology, which employs inaudible Sonar to monitor the movements and respiration of a subject in bed. Sleep monitoring can be achieved by placing a smartphone onto the bedside table and starting a custom app. The app employs sophisticated and novel proprietary algorithms to identify sleep stages: Wake (W), Light Sleep (N1, N2 sleep), Deep Sleep (N3 sleep), Rapid Eye Movement (REM) Sleep or Absence.The sleep staging performance of the app were assessed by testing it against expert manually scored polysomnography (PSG) of 38 subjects gathered in a sleep laboratory. As a secondary assessment, on the same dataset, the performance of the app is compared to that of a reference non-contact device, the S+ by ResMed.Performance across different sleep stage detections was balanced, exceeding the agreement typically reported for actigraphy based devices [1], [2] thanks to a significantly higher sensitivity for all sleep stages. Furthermore, the performance of the app was found to be comparable to the S+ by ResMed product [3], [4].The combination of unobtrusive non-contact sensing and accurate sleep quality assessment, coupled with removal of the requirement to purchase a custom device to enable monitoring of sleep, enables consumers to measure their sleep in the home environment in a zero-cost and accessible manner, while providing sleep staging information not otherwise available with actigraphy based devices.
Experimental Time and Frequency Domain MIMO Channel Matrix Characterization versus Distance for 6×28Gbaud QPSK Transmission over 40×25km Few Mode Fiber (2014)

We present the first experimental time- and frequency-domain MIMO channel matrix characterization versus distance for 6×28Gbaud recirculating loop transmission over 4025km FMF. Beyond 10dB time- and frequency-domain singular-value spreads are observed, motivating nonlinear MIMO equalization.
Synthesis and Supramolecular Characterization of a Novel Class of Glycopyranosyl-Containing Amphiphiles (2000)

A novel class of glycopeptidolipids is described, which potentially can be used as a novel antigen-delivery system. The compounds have been prepared by a combination of solid-supported and solution-based methods. The use of the orthogonally protected FmocLysDde derivative provided an opportunity to incorporate two different types lipids. It was found that the model compound 1 forms aggregates in aqueous media which can be described as rod or tubelike structures. The aggregates can be stabilized by topotactic photopolymerization. Studies on the structural analogues 2-5 revealed the effect of the carbohydrate, peptide, and lipid moiety on the aggregation properties. It is concluded that none of the structure elements can lay claim to be exclusively important for the formation of highly organized aggregates such as tubes, fibers, or helical ribbons from 1, but the presence of all of these structural elements afforded the most uniformly shaped extended structures.
Zinc-Salt-Mediated Synthesis, Growth Kinetic, and Shaped Evolution of Silver Nanoparticles (2012)

We report the synthesis of various shaped silver nanoparticles mediated by ZnCl 2 salt. It has been demonstrated that the salt, PVP/AgNO 3 mole ratio, and the type of polyol (EG, DEG,and GL) significantly determined twinning probability, which is an index of silver seed growth origin. High twining probability arising from low PVP/Ag + ratio and 50 mole% salt favours 1D grown nanowires and nanorods, whereas low twining amidst high mole% salt (150) in DEG offered 2D grown nanoflakes and nanosheets. Other shaped silver nanoparticles have been found. Accidentally, we arrived at a core-shell heterostructure of Ag-ZnO nanocomposite with Ag core enrichment when mole% of the salt was made up to 300. Growth kinetic of nanosphere obtained was monitored, and effect of salt mediation was found crucial. Structural evolution of shaped Ag nanoparticles and nanocomposites have been monitored using XRD, SEM, EDX, TEM, and UV/vis.
Latus Incentive Scheme: Enabling Decentralization in Blockchains based on Recursive SNARKs. (2021)

In the Zendoo whitepaper we introduced a novel SNARK-based construction that allows Bitcoin-like blockchains to create and communicate with sidechains of different types without knowing their internal structure. We also introduced a specific construction, called Latus, allowing creation of fully verifiable sidechains. In the paper we omitted a detailed description of an incentive scheme for Latus that is an essential element of a real decentralized system. This paper fills the gap by introducing details of the incentive scheme for the Latus sidechain. The represented ideas can also be adopted by other SNARK-based blockchains to incentivize decentralized proofs creation.
The Relationship Between Serum Levels of Total IgE, IL‐18, IL‐12, IFN‐<i>γ</i> and Disease Severity in Children With Atopic Dermatitis (2006)

Studies about the role of cytokines on the immunopathogenesis of atopic dermatitis (AD) are generally based on in vitro observations and this role has not been completely clarified yet. Serum levels of total IgE, IL-18, IL-12, IFN-gamma and the relationship between these parameters and disease severity, determined using the SCORAD index, in a group of atopic patients were investigated in this study. Serum levels of total IgE were measured by the nephelometric method and serum levels of IL-18, IL-12/p40 and IFN-gamma were measured by ELISA method. Serum levels of total IgE and IL-18 were found significantly higher in study group than in controls (P<.001). There was no statistically significant difference between patients and controls in respect of serum levels of IL-12/p40 (P = .227). A statistically significant relationship between SCORAD values and serum levels of total IgE (P < .001), IL-18 (P < .001), and IL-12/p40 (P < .001) was determined. These results show that serum levels of IL-18 can be a sensitive parameter that importantly correlates with clinical severity of AD, can play a role in the immunopathogenesis of AD, and furthermore may be used in the diagnosis and follow-up of the disease in addition to other parameters.
Numerical Integration Schemes for Unequal Data Spacing (2017)

In this Paper, we have derived the numerical integration formula for unequal data spacing. For doing so we use Newton’s Divided difference formula to evaluate general quadrature formula and then generate Trapezoidal and Simpson’s rules for the unequal data spacing with error terms. Finally we use numerical examples to compare the numerical results with analytical results and the well-known Monte- Carlo integration results and find excellent agreement.
Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation (2014)

In the traditional object recognition pipeline, descriptors are densely sampled over an image, pooled into a high dimensional non-linear representation and then passed to a classifier. In recent years, Fisher Vectors have proven empirically to be the leading representation for a large variety of applications. The Fisher Vector is typically taken as the gradients of the log-likelihood of descriptors, with respect to the parameters of a Gaussian Mixture Model (GMM). Motivated by the assumption that different distributions should be applied for different datasets, we present two other Mixture Models and derive their Expectation-Maximization and Fisher Vector expressions. The first is a Laplacian Mixture Model (LMM), which is based on the Laplacian distribution. The second Mixture Model presented is a Hybrid Gaussian-Laplacian Mixture Model (HGLMM) which is based on a weighted geometric mean of the Gaussian and Laplacian distribution. An interesting property of the Expectation-Maximization algorithm for the latter is that in the maximization step, each dimension in each component is chosen to be either a Gaussian or a Laplacian. Finally, by using the new Fisher Vectors derived from HGLMMs, we achieve state-of-the-art results for both the image annotation and the image search by a sentence tasks.
NIPALSTREE:  A New Hierarchical Clustering Approach for Large Compound Libraries and Its Application to Virtual Screening (2006)

A hierarchical clustering algorithm--NIPALSTREE--was developed that is able to analyze large data sets in high-dimensional space. The result can be displayed as a dendrogram. At each tree level the algorithm projects a data set via principle component analysis onto one dimension. The data set is sorted according to this one dimension and split at the median position. To avoid distortion of clusters at the median position, the algorithm identifies a potentially more suited split point left or right of the median. The procedure is recursively applied on the resulting subsets until the maximal distance between cluster members exceeds a user-defined threshold. The approach was validated in a retrospective screening study for angiotensin converting enzyme (ACE) inhibitors. The resulting clusters were assessed for their purity and enrichment in actives belonging to this ligand class. Enrichment was observed in individual branches of the dendrogram. In further retrospective virtual screening studies employing the MDL Drug Data Report (MDDR), COBRA, and the SPECS catalog, NIPALSTREE was compared with the hierarchical k-means clustering approach. Results show that both algorithms can be used in the context of virtual screening. Intersecting the result lists obtained with both algorithms improved enrichment factors while losing only few chemotypes.
Transient neuronal inhibition reveals opposing roles of indirect and direct pathways in sensitization (2020)

The dorsal striatum plays an important role in the development of drug addiction; however, a precise understanding of the roles of striatopallidal (indirect) and striatonigral (direct) pathway neurons in regulating behaviors remains elusive. Using a novel approach that relies on the viral-mediated expression of an engineered GPCR (hM4D), we demonstrated that activation of hM4D receptors with clozapine-N-oxide (CNO) potently reduced striatal neuron excitability. When hM4D receptors were selectively expressed in either direct or indirect pathway neurons in rats, CNO did not change acute locomotor responses to amphetamine but altered behavioral plasticity associated with repeated drug treatment. Specifically, transiently disrupting striatopallidal neuronal activity facilitated behavioral sensitization whereas decreasing excitability of striatonigral neurons impaired its persistence. These findings suggest that acute drug effects can be parsed from the behavioral adaptations associated with repeated drug exposure and highlight the utility of this approach for deconstructing neuronal pathway contributions to behaviors such as sensitization.
Theoretical aspects of detection of bacteraemia as a function of the volume of blood cultured (1993)

18% of Staphylococcus aureus bacteraemias and 29% of Escherichia coli bacteraemias have circulating bacteria with an estimated spread of less than 0.04 cfu/ml. With a 95% probability of detection of a bacteraemia, a concentration in blood corresponding to 3 cfu/sampling unit is necessary. In our empirical material, where 30 ml was cultured, the probability of detection of E. coli bacteraemias would have decreased by 11% if 20 ml had been cultured, and 27% if only 10 ml had been cultured. The corresponding figures for S. aureus were 6% and 15%, respectively. For low grade E. coli bacteraemias (< 0.04 cfu/ml) the decrease would have been 33% and 67%, respectively.
De Novo Design of Nanostructured Iron–Cobalt Fischer–Tropsch Catalysts (2013)

Audio cassettes hold the key to enhancing Fischer–Tropsch catalysis. Catalysts based on ultra-thin cobalt shells surrounding cheap iron oxide cores (see picture) are developed, an approach previously optimized for preparing magnetic tape for audio cassettes. These particles are easily made on a large scale, and are excellent Fischer–Tropsch catalysts, giving good diesel fractions. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.