Diseño de una prótesis de cadera utilizando materiales compuestos para permitir la movilidad en miembros inferiores

Los problemas de salud a la cadera en pacientes adulto mayor son mayormente ocasionados por fracturas de cadera intradomiciliarias y extradomiciliarias. Esto origina la pérdida de movilidad y en un porcentaje minoritario hasta la muerte. Por ello, es necesario el reemplazo a partir de una prótesis de cadera que tenga buena durabilidad y no tengan que ser cambiadas, produciendo dolor, malestar y en muchos casos la invalidez del paciente. Por ello el objetivo fue diseñar una prótesis de cadera utilizando materiales compuestos que tengan una buena resistencia, buena durabilidad y permitan la movilidad en miembros inferiores. Para ello, se evaluó el porcentaje de personas a partir de 50 años con problemas de salud a la cadera y se identificó que a partir de los 60 años las mujeres sufren fracturas tipo extracapsulares (zona 2 y 3). Así mismo, usando la metodología de diseño conceptual de Pahl y Beitz; Karl T. Ulrich y Steven D. Eppinger se logró un diseño que cumple con las necesidades y métricas que la paciente necesita. Por otro lado, utilizando el software CES EduPack se logró identificar opciones de materiales para el desarrollo de la prótesis, siendo el Ti-6Al-4V y la biocerámica de zirconia los materiales seleccionados. A partir de entrevistas y encuestas a especialistas se diseñó y simuló la prótesis en el programa SolidWorks, tomando en cuenta la norma ISO 7206-4. Los resultados estuvieron por debajo del límite elástico máximo de 9,10e+08 Pa y una vida total de 1,400e+08 ciclos, siendo valores favorables

Controllable Image Generation via Collage Representations

Recent advances in conditional generative image models have enabled impressive results. On the one hand, text-based conditional models have achieved remarkable generation quality, by leveraging large-scale datasets of image-text pairs. To enable fine-grained controllability, however, text-based models require long prompts, whose details may be ignored by the model. On the other hand, layout-based conditional models have also witnessed significant advances. These models rely on bounding boxes or segmentation maps for precise spatial conditioning in combination with coarse semantic labels. The semantic labels, however, cannot be used to express detailed appearance characteristics. In this paper, we approach fine-grained scene controllability through image collages which allow a rich visual description of the desired scene as well as the appearance and location of the objects therein, without the need of class nor attribute labels. We introduce "mixing and matching scenes" (M&Ms), an approach that consists of an adversarially trained generative image model which is conditioned on appearance features and spatial positions of the different elements in a collage, and integrates these into a coherent image. We train our model on the OpenImages (OI) dataset and evaluate it on collages derived from OI and MS-COCO datasets. Our experiments on the OI dataset show that M&Ms outperforms baselines in terms of fine-grained scene controllability while being very competitive in terms of image quality and sample diversity. On the MS-COCO dataset, we highlight the generalization ability of our model by outperforming DALL-E in terms of the zero-shot FID metric, despite using two magnitudes fewer parameters and data. Collage based generative models have the potential to advance content creation in an efficient and effective way as they are intuitive to use and yield high quality generations

Fluorescent carbon dot–molecular salt hydrogels

The incorporation of functionalised carbon nanodots within a novel low molecular weight salt hydrogel derived from 5-aminosalicylic acid is reported. The carbon dots result in markedly enhanced gelation properties, while inclusion within the hydrophobic gel results in a dramatic fluorescence enhancement for the carbon nanomaterials. The resulting hybrid CD gels exhibit a useful sensor response for heavy metal ions, particularly Pb2+

Treatment recommendations for psoriatic arthritis

Objective: To develop comprehensive recommendations for the treatment of the various clinical manifestations of psoriatic arthritis (PsA) based on evidence obtained from a systematic review of the literature and from consensus opinion. Methods: Formal literature reviews of treatment for the most significant discrete clinical manifestations of PsA (skin and nails, peripheral arthritis, axial disease, dactylitis and enthesitis) were performed and published by members of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA). Treatment recommendations were drafted for each of the clinical manifestations by rheumatologists, dermatologists and PsA patients based on the literature reviews and consensus opinion. The level of agreement for the individual treatment recommendations among GRAPPA members was assessed with an online questionnaire. Results: Treatment recommendations were developed for peripheral arthritis, axial disease, psoriasis, nail disease, dactylitis and enthesitis in the setting of PsA. In rotal, 19 recommendations were drafted, and over 80% agreement was obtained on 16 of them. In addition, a grid that factors disease severity into each of the different disease manifestations was developed to help the clinician with treatment decisions for the individual patient from an evidenced-based perspective. Conclusions: Treatment recommendations for the cardinal physical manifestations of PsA were developed based on a literature review and consensus between rheumatologists and dermatologists. In addition, a grid was established to assist in therapeutic reasoning and decision making for individual patients. It is anticipated that periodic updates will take place using this framework as new data become available

Development of a Virtual CFR Engine Model for Knocking Combustion Analysis

[EN] Knock is a major bottleneck to achieving higher thermal efficiency in spark-ignited (SI) engines. The overall tendency to knock is highly dependent on fuel anti-knock quality as well as engine operating conditions. It is, therefore, critical to gain a better understanding of fuel-engine interactions in order to develop robust knock mitigation strategies. In the present work, a numerical model based on three-dimensional (3-D) computational fluid dynamics (CFD) was developed to capture knock in a Cooperative Fuel Research (CFR) engine. For combustion modeling, a hybrid approach incorporating the G-equation model to track turbulent flame propagation, and a homogeneous reactor multi-zone model to predict end-gas auto-ignition ahead of the flame front and post-flame oxidation in the burned zone, was employed. In addition, a novel methodology was implemented wherein a laminar flame speed lookup table generated a priori from a chemical kinetic mechanism could be used to provide flame speed as an input to the G-equation model, instead of using conventional empirical correlations. Multi-cycle Reynolds-Averaged Navier Stokes (RANS) simulations were performed for two different spark timings (STs) corresponding to non-knocking and knocking conditions, with other operating conditions kept the same as those of a standard Research Octane Number (RON) test. Iso-octane was considered as the fuel for the numerical study. Two different reduced kinetic mechanisms were employed to describe end-gas auto-ignition chemistry and to generate the flame speed lookup table. Experimental data, including intake/exhaust boundary conditions, was provided by a spark timing sweep study conducted in an in-house CFR engine. Moreover, cylinder wall/valve/port surface temperatures and residual gas fraction (RGF) were estimated using a well-calibrated one-dimensional (1-D) model. On the other hand, a novel methodology was also developed to analyze experimental data for the knocking case and identify the most representative cycle. For the non-knocking case, a good agreement was found between experiment and CFD simulation, with respect to cycle-averaged values of 10% burn point (CA10), 50% burn point (CA50) and peak pressure magnitude/location. The virtual CFR engine model was also demonstrated to be capable of predicting average knock characteristics for the knocking case, such as knock point, knock intensity and energy of resonance, with good accuracy.The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (Argonne). Argonne, a U.S. Department of Energy (DOE) Office of Science laboratory, is operated under Contract No. DEAC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in the said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. This research was partially funded by DOE's Office of Vehicle Technologies and Office of Energy Efficiency and Renewable Energy (EERE) under Contract No. DE-AC02-06CH11357. The authors wish to thank Gurpreet Singh, Kevin Stork, and Leo Breton, program managers at DOE, for their support. This research was conducted as part of the Co-Optimization of Fuels and Engines (Co-Optima) project sponsored by the U.S. DOE Office of EERE, Bioenergy Technologies and Vehicle Technologies OfficesPal, P.; Kolodziej, C.; Choi, S.; Som, S.; Broatch, A.; Gómez-Soriano, J.; Wu, Y.... (2018). Development of a Virtual CFR Engine Model for Knocking Combustion Analysis. SAE International Journal of Engines. 11(6):1069-1082. https://doi.org/10.4271/2018-01-0187S1069108211

Neutrophil extracellular trap formation requires OPA1-dependent glycolytic ATP production

Optic atrophy 1 (OPA1) is a mitochondrial inner membrane protein that has an important role in mitochondrial fusion and structural integrity. Dysfunctional OPA1 mutations cause atrophy of the optic nerve leading to blindness. Here, we show that OPA1 has an important role in the innate immune system. Using conditional knockout mice lacking Opa1 in neutrophils (Opa1(N Delta)), we report that lack of OPA1 reduces the activity of mitochondrial electron transport complex I in neutrophils. This then causes a decline in adenosine-triphosphate (ATP) production through glycolysis due to lowered NAD(+) availability. Additionally, we show that OPA1-dependent ATP production in these cells is required for microtubule network assembly and for the formation of neutrophil extracellular traps. Finally, we show that Opa1(N Delta) mice exhibit a reduced antibacterial defense capability against Pseudomonas aeruginosa.Peer reviewe

Integrating Stand and Soil Properties to Understand Foliar Nutrient Dynamics during Forest Succession Following Slash-and-Burn Agriculture in the Bolivian Amazon

Secondary forests cover large areas of the tropics and play an important role in the global carbon cycle. During secondary forest succession, simultaneous changes occur among stand structural attributes, soil properties, and species composition. Most studies classify tree species into categories based on their regeneration requirements. We use a high-resolution secondary forest chronosequence to assign trees to a continuous gradient in species successional status assigned according to their distribution across the chronosequence. Species successional status, not stand age or differences in stand structure or soil properties, was found to be the best predictor of leaf trait variation. Foliar δ13C had a significant positive relationship with species successional status, indicating changes in foliar physiology related to growth and competitive strategy, but was not correlated with stand age, whereas soil δ13C dynamics were largely constrained by plant species composition. Foliar δ15N had a significant negative correlation with both stand age and species successional status, – most likely resulting from a large initial biomass-burning enrichment in soil 15N and 13C and not closure of the nitrogen cycle. Foliar %C was neither correlated with stand age nor species successional status but was found to display significant phylogenetic signal. Results from this study are relevant to understanding the dynamics of tree species growth and competition during forest succession and highlight possibilities of, and potentially confounding signals affecting, the utility of leaf traits to understand community and species dynamics during secondary forest succession

High Glucose Enhances Cytotoxic T Lymphocyte-Mediated Cytotoxicity

Cytotoxic T lymphocytes (CTLs) are key players to eliminate tumorigenic or pathogen-infected cells using lytic granules (LG) and Fas ligand (FasL) pathways. Depletion of glucose leads to severely impaired cytotoxic function of CTLs. However, the impact of excessive glucose on CTL functions still remains largely unknown. Here we used primary human CD8+ T cells, which were stimulated by CD3/CD28 beads and cultured in medium either containing high glucose (HG, 25 mM) or normal glucose (NG, 5.6 mM). We found that in HG-CTLs, glucose uptake and glycolysis were enhanced, whereas proliferation remained unaltered. Furthermore, CTLs cultured in HG exhibited an enhanced CTL killing efficiency compared to their counterparts in NG. Unexpectedly, expression of cytotoxic proteins (perforin, granzyme A, granzyme B and FasL), LG release, cytokine/cytotoxic protein release and CTL migration remained unchanged in HG-cultured CTLs. Interestingly, additional extracellular Ca2+ diminished HG-enhanced CTL killing function. Our findings suggest that in an environment with excessive glucose, CTLs could eliminate target cells more efficiently, at least for a certain period of time, in a Ca2+-dependent manner

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors