Influence of planting depth and mulch on the growth of nine species of ornamental plants in landscape and container settings

Six different planting depths and mulch treatments were applied to nine different species of landscape plants in a field study beginning July 5, 2001. Plants were maintained for a two-year study period on the effects of improper planting depths and mulch on the following species; Southern Magnolia ‘Little Gem’ (Magnolia grandiflora), Bald Cypress (Taxodium distichum), Crape Myrtle (Lagerstromia indica x fauriei ‘Natchez’), Azalea (Rhododendron indicum ‘George L. Tabor’), Indian Hawthorn (Raphiolepis indica ‘Clara’), Loropetalum (Loropetalum chinense ‘Burgundy’), Dwarf Gardenia (Gardenia jasminoides ‘Radicans’), Dwarf Nandina (Nandina domestica ‘Fire power’), and Liriope (Liriope muscari ‘Big Blue’). Four growth indexes, and quality ratings were collected over this study. A second experiment was conducted on the same plant species in a container study that involved three different planting depths. Growth index measurements were taken four times over an eight-month period, and shoot and root dry weights were also collected. This research will help to determine the tolerances of these nine species to improper planting techniques and mulch. For this study the following planting depths were used in the field were 7.6 cm above grade, 3.8 cm above grade, at grade, 3.8 cm below grade and 7.6 cm below grade. One half of the plants in the field were mulched at a rate of three-inches with 5/8th screen pine bark mulch. In the container study the following planting depths were used: at grade, 3.8 cm below grade, 7.6 cm below grade. For all species, growth did not differ among the planting depth treatments in the field. However, there was a significant affect by the application of mulch on seven of the eight species in the field study. In the container study, significant affect of planting depth was observed in all species except bald cypress, Indian hawthorn, and dwarf gardenia

I Did it For Me: Negotiating identity and agency

Scholars have debated the feminist critique of female beauty practices for years with the fundamental disagreement revolving around the notion of “agency.” This study used textual analysis to explore how the concept of “agency” has been employed in cosmetic surgery ads placed in large city magazines.   Three themes emerged: realize your potential, pleasing yourself, and control your destiny.  This research expands our understanding of how physicians are repositioning cosmetic surgery to women through discourses that empower, appeal to their sense of self, and play upon feminist sensibilities that privilege individual choice. This research also contributes to the literature surrounding the ongoing debate of agency by examining how it plays out in another form of text previously unexamined (physician advertising) and how it touches upon a new player in the health beauty system (physicians) rather than prior studies, which focus on idealized images in the media

Egalisation vectorielle pour signaux OFDM sans intervalle de garde

La modulation OFDM utilise habituellement un intervalle de garde inséré avant chaque symbole, qui permet de lutter efficacement contre la dispersion du canal. En revanche, cette approche génère une perte de débit utile pouvant atteindre 20%. On propose dans cette contribution une solution sans intervalle de garde qui met en oeuvre une égalisation vectorielle avec décision dans la boucle. La structure du récepteur repose sur 2 matrices triangulaires, dont les coefficients doivent être estimés et adaptés en fonction des variations du canal, à partir d'un ensemble de pilotes répartis à l'intérieur de chaque symbole. On décrit différents algorithmes d'estimation et on montre en particulier qu'il est nécessaire de tenir compte des dépendances existantes à l'intérieur et entre ces matrices pour atteindre des performances acceptables en terme de convergence

Tomographie de réseau appliquée à la simulation et à l'analyse de trafic dans des séquences d'images de microscopie à fluorescence

La technique de marquage avec la protéine GFP "Green Fluorescent Protein" et la vidéomicroscopie à fluorescence sont des outils d'investigation permettant d'observer des dynamiques et des interactions moléculaires dans des cellules vivantes, tant à l'échelle microscopique qu'à l'échelle nanoscopique. Par conséquent, il est impératif de développer de nouvelles techniques d'analyse d'images capables de quantifier les dynamiques des processus biologiques observés dans ces séquences. Ceci motive notre effort de recherche qui consiste à développer de nouvelles méthodes d'extraction d'informations à partir de données nD. Dans l'analyse de trafic, le suivi d'objets s'appuyant sur des techniques conventionnelles peut s'avérer très complexe, voire impossible, surtout quand un grand nombre de petits objets coalescents sont en interaction. Néanmoins, l'estimation des trajectoires complètes de tous les objets n'est pas toujours nécessaire à la compréhension et la mesure de l'activité cellulaire. En effet, estimer les régions "origine" et "destination" de ces objets peut s'avérer plus pertinente. Dans cet article, nous proposons une approche originale pour inférer les zones "origine" et "destination" à partir d'informations partielles relatives au trafic. Ainsi, le trafic membranaire est assimilé à un trafic routier, ce qui permet alors d'exploiter les récentes avancées en Tomographie de Réseau (TR) bien connues dans la communauté réseaux de communication pour étudier le trafic vésiculaire. Cette approche est validée sur des séquences d'images artificielles relatives à la protéine Rab6, une GTPase impliquée dans la régulation du trafic membranaire intracellulaire

Discriminant random field and patch-based redundancy analysis for image change detection

International audienceTo develop better image change detection algorithms, new models able to capture all the spatio-temporal regularities and geometries seen in an image pair are needed. In con- trast to the usual pixel-wise methods, we propose a patch- based formulation for modeling semi-local interactions and detecting occlusions and other local or regional changes in an image pair. To this end, the image redundancy property is exploited to detect unusual spatio-temporal patterns in the scene. We first define adaptive detectors of changes between two given image patches and combine locally in space and scale such detectors. The resulting score at a given loca- tion is exploited within a discriminant Markov random field (DRF) whose global optimization flags out changes with no optical flow computation. Experimental results on several applications demonstrate that the method performs well at detecting occlusions and meaningful regional changes and is especially robust in the case of low signal-to-noise ratios

Defined Folate-PEG-siRNA Conjugates for Receptor-specific Gene Silencing

Gene silencing mediated by small interfering RNA (siRNA) is a novel approach in the development of new cancer therapeutics. Polycations used for nucleic acid delivery still remain heterogeneous compounds, despite continuous progress in polymer synthetic technologies. Here we report the development of a structural defined folic acid polyethylene glycol (PEG) siRNA conjugate accessible via click chemistry yielding a monodisperse ligand-PEG-siRNA conjugate. The folic acid targeting ligand was synthesized by solid phase supported peptide chemistry. The conjugate was shown to be specifically internalized into folic acid receptor expressing cells. When combined with a structurally defined polycation, again synthesized with the precision of solid phase chemistry, efficient receptor specific gene silencing is achieved

An integrated model for predicting KRAS dependency

The clinical approvals of KRAS G12C inhibitors have been a revolutionary advance in precision oncology, but response rates are often modest. To improve patient selection, we developed an integrated model to predict KRAS dependency. By integrating molecular profiles of a large panel of cell lines from the DEMETER2 dataset, we built a binary classifier to predict a tumor's KRAS dependency. Monte Carlo cross validation via ElasticNet within the training set was used to compare model performance and to tune parameters α and λ. The final model was then applied to the validation set. We validated the model with genetic depletion assays and an external dataset of lung cancer cells treated with a G12C inhibitor. We then applied the model to several Cancer Genome Atlas (TCGA) datasets. The final "K20" model contains 20 features, including expression of 19 genes and KRAS mutation status. In the validation cohort, K20 had an AUC of 0.94 and accurately predicted KRAS dependency in both mutant and KRAS wild-type cell lines following genetic depletion. It was also highly predictive across an external dataset of lung cancer lines treated with KRAS G12C inhibition. When applied to TCGA datasets, specific subpopulations such as the invasive subtype in colorectal cancer and copy number high pancreatic adenocarcinoma were predicted to have higher KRAS dependency. The K20 model has simple yet robust predictive capabilities that may provide a useful tool to select patients with KRAS mutant tumors that are most likely to respond to direct KRAS inhibitors

Multiscale neighborhood-wise decision fusion for redundancy detection in image pairs

SIAM Journal Multiscale Modeling & SimulationTo develop better image change detection algorithms, new models able to capture spatio-temporal regularities and geometries present in an image pair are needed. In this paper, we propose a multiscale formulation for modeling semi-local inter-image interactions and detecting local or regional changes in an image pair. By introducing dissimilarity measures to compare patches and binary local decisions, we design collaborative decision rules that use the total number of detections obtained from the neighboring pixels, for different patch sizes. We study the statistical properties of the non-parametric detection approach that guarantees small probabilities of false alarms. Experimental results on several applications demonstrate that the detection algorithm (with no optical flow computation) performs well at detecting occlusions and meaningful changes for a variety of illumination conditions and signal-to-noise ratios. The number of control parameters of the algorithm is small and the adjustment is intuitive in most cases