gebhart/patrepeval huggingface dataset:
Release of DNA template restrictions in rabbit spermatozoa and rat liver nuclei (1976)

The effects of disulfide reduction on DNA template activity of rabbit sperm and rat liver nuclei are compared. After treatment of rabbit spermatozoa with 5 mM dithiothreitol and 1% Triton X-100 for 90 minutes at room temperature, sperm nuclei decondense and nuclear template restrictions for DNA synthesis are released. By contrast, similar thiol treatment of isolated rat liver nuclei fails to release DNA template restrictions. These results suggest a significant difference in the control of template activity in mammalian sperm and somatic cell nuclei.
Genetically encoded fluorescent sensors for redox processes (2015)

Redox processes play a key role in cells of all.organisms. These processes imply directed flows of electrons via so-called redox pairs: substances that exist in both reduced and oxidized states simultaneously within the cell. Examples of redox pairs are NAD+/NADH, NADP+/NADPH, GSSG/2GSH. Until recently, studies of redox processes in the living cells were challenged by the lack of suitable methods. Genetically encoded fluorescent biosensors provide a new way to study biological processes including redox ones. Biosensors allow real-time detection of messengers, metabolites and enzymatic activities in living systems of different complexity from cultured cells to transgenic animals. In this review, we describe the main types of known redox biosensors with examples of their use.
A general approach for partitioning N-dimensional parallel nested loops with conditionals (2006)

Parallel loops account for the greatest amount of parallelism in scientific and numerical codes. For example, most of the DO loops in SPEC CFP2000 and SPEC OMPM2001 are of DOALL type and account for a large percentage of the total execution time. One of the ways to exploit parallelism is to partition the iteration space of a DOALL loop amongst different processors in a parallel processor system. Naturally, a good partitioning is of key importance to achieve high performance and for efficient use of multiprocessor systems. Although a significant amount of work has been done in partitioning and scheduling of loops with both rectangular and non-rectangular iteration spaces, the problem of partitioning loops with conditionals has not been addressed so far to the best of our knowledge. In this paper, we present a mathematical model for partitioning parallel nested loops, both perfect and non-perfect, with conditionals, where the expressions in a conditional are affine functions of the outer loop indices. We present a loop transformation based on elimination of redundant constraints bounding the iteration space of a nested loop. The transformation plays a critical role during the (static) partitioning process as it helps to capture the "exact" lower and upper bounds (can be either a constant or symbolic) of the loop indices. We generate a canonical form of the loop nest using the transformation and employ the geometric approach we proposed earlier (in [1, 2]) for partitioning the iteration space along an axis corresponding to the outermost loop. For cases in which such a transformation does not exist, we propose a general approach for loop canonicalization. We present several examples from the literature and numerical packages to illustrate the effectiveness of our approach.
Polymorphisms in the RNASE3 Gene Are Associated with Susceptibility to Cerebral Malaria in Ghanaian Children (2011)

The 371G allele of RNASE3 is associated with susceptibility to CM and forms part of a risk associated haplotype GGA defined by the markers: rs2073342 (G-allele), rs2233860 (G-allele) and rs8019343 (A-allele) respectively. Collectively, these results suggest a hitherto unrecognized role for eosinophils in CM pathogenesis.
Control of the of the final stage of immune-mediated diabetes by ISO-1, an antagonist of macrophage migration inhibitory factor (2008)

We recently showed that attenuation of inflammatory cytokine MIF with pharmacological inhibitor ISO-1 down-regulates the immune-mediated diabetes in mice. Here we explore the effects of MIF neutralization by ISO-1 on the local inflammatory pathway of the disease. In vivo treatment of mice with ISO-1 inhibited the expression of pro-inflammatory cytokines and iNOS in the pancreatic islets. Moreover, ISO-1 affected in vitro cytokine-induced NO pro?duction by fibroblasts, endothelial cells, insulinoma cells, and pancreatic islets, and rescued ? cells from NO-dependent damage. These results suggest regulatory potential of ISO-1 at the level of the pancreas which can preserve the target tissue from autoimmune attack.
Treatment of Experimentally Induced Aneurysms with Stents (2005)

This study demonstrates the possible shortcomings and problems of emerging stent techniques to treat intracerebral aneurysms, shows where technical advances have to be made, and describes in which cases of aneurysm morphology caution has to be exercised when considering an endovascular approach using stents.
Electroluminescence from Organometallic Lead Halide Perovskite‐Conjugated Polymer Diodes (2015)

Organometallic lead perovskite-based solar cells can be converted to light-emitting diodes by engineering the current density. Diodes are fabricated with adjacent perovskite and conjugated polymer layers using orthogonal solvents. Under forward bias, these devices show simultaneous emission from both the luminescent conjugated polymer and the perovskite, providing direct information on electron and hole recombination as a function of device architecture and bias voltage.
L’analyse avancée de l’information par fusion de données textuelles et numériques (2007)

National audience
A Combination of Kruskal and K-means Algorithms for Network Lifetime Extension in Wireless Sensor Networks (2021)

Recently, the deployment of Wireless Sensor Networks (WSNs) increases in various applications. These networks are dispersed in an inaccessible area and have to operate on small batteries. Energy is one of the most critical resources since sensor nodes sense, collect, process and send data to its base station. Many researches have demonstrated the importance of clustering to extend the lifetime of wireless sensor networks. Clustering algorithms consider several parameters like the density, the residual energy, and the distance separating the base station and the nodes to select the appropriate cluster heads. In this paper, K-means algorithm is first used to select a set of Cluster Heads (CHs) from all the nodes. Then we propose the Kruskal algorithm to define the optimal path between CHs and BS. Simulation results show that the proposed combination allows improved lifetime extension compared with existing algorithms.
Intracellular distribution and transcriptional regulation of Atlantic salmon (Salmo salar) Rab5c, 7a and 27a homologs by immune stimuli (2020)

Rab GTPases control trafficking of intracellular vesicles and are key regulators of endocytic and secretory pathways. Due to their specific distribution, they may serve as markers for different endolysosomal compartments. Since Rab GTPases are involved in uptake and trafficking of endocytosed ligands and cell receptors, as well as secretion of immune mediators, they have been implicated in diverse immunological processes and their functions are often exploited by intracellular pathogens such as viruses. While Rab proteins have been studied extensively in mammals, their functions in vesicle trafficking in teleosts are not well known. In the present work, Atlantic salmon Rab5c, Rab7a and Rab27a homologs were studied in terms of intracellular distribution and gene expression. Structured illumination microscopy demonstrated that transgenic, GFP-tagged salmon Rab5c and Rab7a are, predominantly, located within early endosomes and late endosomes/lysosomes, respectively. In contrast, Rab27a showed a broader distribution, which indicates that it associates with diverse intracellular vesicles and organelles. Infection of salmon with Salmonid alphavirus subtype 3 (SAV3) enhanced the mRNA levels of all of the studied Rab isoforms in heart and head kidney and most of them were upregulated in spleen. This may reflect the capacity of the virus to exploit the functions of these rab proteins. It is also possible that the transcriptional regulation of Rab proteins in SAV3-infected organs may play a role in the antiviral immune response. The latter was further supported by in vitro experiments with adherent head kidney leukocytes. The expression of Rab5c and Rab27a was upregulated in these cells following stimulation with TLR ligands including CpG oligonucleotides and polyI:C. The expression of most of the analyzed Rab isoforms in the primary leukocytes was also enhanced by stimulation with type I IFN. Interestingly, IFN-gamma had a negative effect on Rab7a expression which may be linked to the priming activity of this cytokine on monocytes and macrophages. Overall, these data demonstrate that the intracellular distribution of Rab5c, Rab7a and Rab27a is phylogenetically conserved within vertebrates and that these molecules might be implicated in viral infections and the regulation of the antiviral immune response in Atlantic salmon.
Secretion of insulin-like growth factor-I (IGF-I) and IGF-binding proteins from bovine mammary tissue in vitro (1991)

In vitro, insulin-like growth factor-I (IGF-I) promotes both growth and development of bovine mammary tissue. In vivo, the effects of IGF-I may encompass endocrine, paracrine or autocrine mediation. We addressed the possibility of paracrine/autocrine effects of IGF-I in the mammary gland by examining the in-vitro secretion of IGF-I and IGF-binding proteins (IGFBPs) from bovine mammary tissue. Bovine mammary explants from pregnant non-lactating and lactating non-pregnant animals were found to synthesize and secrete IGF-I and IGFBPs. Mammary acini cultures, representative of mammary secretory epithelia, secreted both IGF-I and IGFBP, but synthesized only IGFBP. Concentrations of IGF-I in conditioned media from explants were 1.54 and 0.72 fmol/micrograms DNA for pregnant and lactating animals respectively. Concentrations of IGFBPs in conditioned media from explants were similar for both physiological states at 2529 pmol 125I-labelled IGF-I bound/micrograms DNA. Ligand/Western blotting procedures identified four IGFBPs of 29, 33, 37 and 44 kDa for acini cultures and five IGFBPs of 28, 31, 36, 44 and 46 kDa for explant cultures. Similar affinities for IGF-I and IGF-II were shown by IGFBP, using 125I-labelled recombinant human IGF-I as the competing ligand (median effective dose (ED50) of 0.085 pmol). When 125I-labelled bovine IGF-II was used as the ligand, only bovine IGF-II (ED50 of 0.25 pmol) inhibited binding. The addition of prolactin, insulin and cortisol, with or without GH, did not affect secretion of either IGF-I or IGFBP. This report describes the ability of normal mammary tissue to synthesize and secrete IGF-I and IGFBPs.
Randomization methods for assessing data analysis results on real‐valued matrices (2009)

Abstract Randomization is an important technique for assessing the significance of data analysis results. Given an input dataset, a randomization method samples at random from some class of datasets that share certain characteristics with the original data. The measure of interest on the original data is then compared to the measure on the samples to assess its significance. For certain types of data, e.g., gene expression matrices, it is useful to be able to sample datasets that have the same row and column distributions of values as the original dataset. Testing whether the results of a data mining algorithm on such randomized datasets differ from the results on the true dataset tells us whether the results on the true data were an artifact of the row and column statistics, or due to some more interesting phenomena in the data. We study the problem of generating such randomized datasets. We describe methods based on local transformations and Metropolis sampling, and show that the methods are efficient and usable in practice. We evaluate the performance of the methods both on real and generated data. We also show how our methods can be applied to a real data analysis scenario on DNA microarray data. The results indicate that the methods work efficiently and are usable in significance testing of data mining results on real‐valued matrices. Copyright © 2009 Wiley Periodicals, Inc., A Wiley Company
A NOVEL FRAMEWORK FOR SURFACE RECONSTRUCTION FROM 3-DIMENSIONAL CONTOURS (1986)

In many applications, such as computer vision, medical imaging, scene forming, and motion animation, surface data is available in the form of two-dimensional (2D) or three-dimensional (3D) contours. Reconstruction of the surface integrates this information into a three-dimensional format. A good surface reconstruction framework must be able to include the surface characteristics inherited in the local structures of the contours. In order to achieve this goal, we have developed a novel surface reconstruction framework which consists of the formulation and solution of three separate subproblems:\nFirst, a new scheme for segmenting 3D contours into a set of 3D curve segments is developed. This scheme uses the norm of the Darboux vector, also called generalized curvature, as the segmentation criterion. The Darboux vector includes, as its components, the conventional curvature and the second curvature or torsion of the contour. Hence, it gives a complete representation of the structural characteristics of the contour. We use distributional derivatives in our formulation in order to combat the noise in contour data. The final result of the segmentation process is the representation of a 3D contour in the form of a string of 3D curve segments separated by a number of breakpoints.\nSecond, having represented 3D contours by strings as above, one-to-one correspondences between breakpoints of adjacent strings are found. This correspondence problem is converted into a matching procedure by searching for the best possible match between the set of all homomorphic images of one contour and the adjacent one. An attribute vector, called a 3D-C-descriptor, is assigned to each curve segment. 3D-C-descriptors are then used as a cost function to locate this best match via the branch-and-bound search technique. This search is conducted with dynamic-programming which reduces the otherwise exponential-time complexity problem to one with polynomial-time complexity.\nFinally, once the curve segments are matched on all contours, parametric surface patches are created between the matched curve segments. Surface patch formation is carried out by blending the curve segments using novel spline and sinc blending functions. These blending functions interpolate simultaneously several contours and are chosen such that the boundary conditions are satisfied, and the shape of a curve segment is propagated along the surface beyond neighboring contours.
Functional characterisation of a putative rhamnogalacturonan II specific xylosyltransferase (2008)

An Arabidopsis thaliana gene, At1g56550, was expressed in Pichia pastoris and the recombinant protein was shown to catalyse transfer of D-xylose from UDP-alpha-D-xylose onto methyl alpha-L-fucoside. The product formed was shown by 1D and 2D 1H NMR spectroscopy to be Me alpha-D-Xyl-(1,3)-alpha-L-Fuc, which is identical to the proposed target structure in the A-chain of rhamnogalacturonan II. Chemically synthesized methyl L-fucosides derivatized by methyl groups on either the 2-, 3- or 4 position were tested as acceptor substrates but only methyl 4-O-methyl-alpha-L-fucopyranoside acted as an acceptor, although to a lesser extent than methyl alpha-L-fucoside. At1g56550 is suggested to encode a rhamnogalacturonan II specific xylosyltransferase.
MEAL: Multi-Model Ensemble via Adversarial Learning (2018)

Often the best performing deep neural models are ensembles of multiple base-level networks. Unfortunately, the space required to store these many networks, and the time required to execute them at test-time, prohibits their use in applications where test sets are large (e.g., ImageNet). In this paper, we present a method for compressing large, complex trained ensembles into a single network, where knowledge from a variety of trained deep neural networks (DNNs) is distilled and transferred to a single DNN. In order to distill diverse knowledge from different trained (teacher) models, we propose to use adversarial-based learning strategy where we define a block-wise training loss to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously. The proposed ensemble method (MEAL) of transferring distilled knowledge with adversarial learning exhibits three important advantages: (1) the student network that learns the distilled knowledge with discriminators is optimized better than the original model; (2) fast inference is realized by a single forward pass, while the performance is even better than traditional ensembles from multi-original models; (3) the student network can learn the distilled knowledge from a teacher model that has arbitrary structures. Extensive experiments on CIFAR-10/100, SVHN and ImageNet datasets demonstrate the effectiveness of our MEAL method. On ImageNet, our ResNet-50 based MEAL achieves top-1/5 21.79%/5.99% val error, which outperforms the original model by 2.06%/1.14%. Code and models are available at: https://github.com/AaronHeee/MEAL
Abstract 2326: Loss of prostate-specific antigen slows growth and alters the extracellular proteome of LNCaP human prostate cancer cells (2010)

Abstract Prostate-specific antigen (PSA) is the most extensively used biomarker in cancer. There continues to be intense debate regarding the use of PSA in screening for prostate cancer (PCa) and how it may be used for optimal benefit. The role PSA plays in reproductive biology and PCa has been an area of ongoing interest. As a serine protease with novel substrate preferences, PSA continues to be secreted at high levels (mg/mL) even by the majority of advanced prostatic carcinomas. This protease has been shown to affect the growth or viability of a variety of cell types, including epithelial cells, osteoclasts, T-cells, and human PCa cell lines. In this study, we stably reduced endogenous PSA gene expression using lentiviral short hairpin loop RNA (shRNA) technology in the LNCaP human PCa cell line. We then investigated any resulting changes to LNCaP growth rates in vitro and ex vivo. Acting on the hypothesis that any PSA-induced changes to LNCaP behavior are the result of the enzyme's extracellular activity, we used a high-throughput shot-gun mass spectrometry-based (MS) approach to analyze the in vitro extracellular LNCaP proteome in control and PSA-targeted shRNA populations. Having demonstrated that our shRNA strategy targeted to PSA caused as much as a 90% reduction in PSA protein production compared to control cells, we next showed that in vitro doubling times are slowed from 2 days to 11 days. Subcutaneous tumors displayed an almost 50% reduction in take rate, with the final weights of those remaining only reaching 10% of controls after 8 weeks of growth. Serum-free media conditioned for 24 hours by control or PSA-targeted LNCaP populations were then subjected to global semi-quantitative proteomics analysis using the high resolution LTQ-Orbitrap mass spectrometer (Thermo Electron, San Jose, CA). In one 200 minute LTQ-Orbitrap analysis, in conjunction with the Mascot Search algorithm (Matrix Science, Boston, MA), we were able to identify 1784 proteins in the LNCaP conditioned media. Of these, 686 were specific to the control populations, 527 were specific to the reduced PSA populations, and 571 were common to both. These results demonstrate the utility of high-throughput proteomic strategies as an unbiased approach for the identification of candidate mechanisms behind novel observations in cancer biology. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2326.
A portable, low-cost, highly integrated, 3D medical ultrasound system (2004)

A design is presented, with prototype component experimental results, of a portable, low-cost 3D ultrasound system that utilizes innovative integrated circuit (IC) topologies and beamforming algorithms to condense the beamforming process into a small custom IC coupled with a generic, commercially available DSP chip. Furthermore, the system includes a low-cost, fully sampled 2D transducer array placed directly adjacent to the receive circuitry allowing precise impedance matching and improved SNR.
Multiple ocular diseases detection based on joint sparse multi-task learning (2015)

In this paper, we present a multiple ocular diseases detection scheme based on joint sparse multi-task learning. Glaucoma, Pathological Myopia (PM), and Age-related Macular Degeneration (AMD) are three major causes of vision impairment and blindness worldwide. The proposed joint sparse multitask learning framework aims to reconstruct a test fundus image with multiple features from as few training subjects as possible. The linear version of this problem could be casted into a multi-task joint covariate selection model, which can be very efficiently optimized via kernelizable accelerated proximal gradient method. Extensive experiments are conducted in order to validate the proposed framework on the SiMES dataset. From the Area Under Curve (AUC) results in multiple ocular diseases classification, our method is shown to outperform the state-of-the-art algorithms.
Application of Decoy Oligodeoxynucleotides-Based Approach to Renal Diseases (2004)

Recent progress in cellular and molecular research has provided a new technique to inhibit target gene expression based on DNA technology such as antisense oligodeoxynucleotides (ODN), small interfering RNA (siRNA), ribozyme and decoy ODN. Especially, recently, a successful ODN-based approach termed decoy ODN has used synthetic ODN containing an enhancer element that can penetrate cells, to bind to sequence-specific DNA-binding proteins and interfere with transcription in vitro and in vivo. Transfer of cis-element double-stranded decoy ODN has been reported as a new powerful tool in a new class of anti-gene strategies to treat various diseases as gene therapy or as a research tool to examine the molecular mechanisms of expression of a specific gene. Transfer of double-stranded ODN corresponding to the cis-sequence will result in attenuation of the authentic cis-trans interaction, leading to removal of trans-factors from the endogenous cis-elements with subsequent modulation of gene expression. To date, we have chosen several target transcription factors such as nuclear factor-kappaB (NF-kappaB) and E2F to prevent the progression of several diseases including renal diseases. As other targets, we focused on negative regulatory element (NRE) for the renin gene and angiotensinogen gene-activating element (AGE) for the angiotensinogen gene to examine the molecular mechanisms of gene expression, AP-1 and ets-1. In this paper, we introduce the decoy strategy and demonstrate examples of application of decoy ODN approach targeting E2F and NF-kappaB in renal diseases.