Double noding technique for mixed mode crack propagation studies

A simple dynamic finite element algorithm for analyzing a propagating mixed mode crack tip is presented. A double noding technique, which can be easily incorporated into existing dynamic finite element codes, is used together with a corrected J integral to extract modes I and II dynamic stress intensity factors of a propagating crack. The utility of the procedure is demonstrated by analyzing test problems involving a mode I central crack propagating in a plate subjected to uniaxial tension, a mixed mode I and II stationary, slanted central crack in a plate subjected to uniaxial impact loading, and a mixed mode I and II extending, slanted single edge crack in a plate subjected to uniaxial tension

Probabilistic sizing of laminates with uncertainties

A reliability based design methodology for laminate sizing and configuration for a special case of composite structures is described. The methodology combines probabilistic composite mechanics with probabilistic structural analysis. The uncertainties of constituent materials (fiber and matrix) to predict macroscopic behavior are simulated using probabilistic theory. Uncertainties in the degradation of composite material properties are included in this design methodology. A multi-factor interaction equation is used to evaluate load and environment dependent degradation of the composite material properties at the micromechanics level. The methodology is integrated into a computer code IPACS (Integrated Probabilistic Assessment of Composite Structures). Versatility of this design approach is demonstrated by performing a multi-level probabilistic analysis to size the laminates for design structural reliability of random type structures. The results show that laminate configurations can be selected to improve the structural reliability from three failures in 1000, to no failures in one million. Results also show that the laminates with the highest reliability are the least sensitive to the loading conditions

Effect of seasonal variation on clinical outcome in patients with chronic conditions: Analysis of the commonwealth scientific and industrial research organization (csiro) national telehealth trial

©Ahmadreza Argha, Andrey Savkin, Siaw-Teng Liaw, Branko George Celler. Background: Seasonal variation has an impact on the hospitalization rate of patients with a range of cardiovascular diseases, including myocardial infarction and angina. This paper presents findings on the influence of seasonal variation on the results of a recently completed national trial of home telemonitoring of patients with chronic conditions, carried out at five locations along the east coast of Australia. Objective: The aim is to evaluate the effect of the seasonal timing of hospital admission and length of stay on clinical outcome of a home telemonitoring trial involving patients (age: mean 72.2, SD 9.4 years) with chronic conditions (chronic obstructive pulmonary disease coronary artery disease, hypertensive diseases, congestive heart failure, diabetes, or asthma) and to explore methods of minimizing the influence of seasonal variations in the analysis of the effect of at-home telemonitoring on the number of hospital admissions and length of stay (LOS). Methods: Patients were selected from a hospital list of eligible patients living with a range of chronic conditions. Each test patient was case matched with at least one control patient. A total of 114 test patients and 173 control patients were available in this trial. However, of the 287 patients, we only considered patients who had one or more admissions in the years from 2010 to 2012. Three different groups were analyzed separately because of substantially different climates: (1) Queensland, (2) Australian Capital Territory and Victoria, and (3) Tasmania. Time series data were analyzed using linear regression for a period of 3 years before the intervention to obtain an average seasonal variation pattern. A novel method that can reduce the impact of seasonal variation on the rate of hospitalization and LOS was used in the analysis of the outcome variables of the at-home telemonitoring trial. Results: Test patients were monitored for a mean 481 (SD 77) days with 87% (53/61) of patients monitored for more than 12 months. Trends in seasonal variations were obtained from 3 years’ of hospitalization data before intervention for the Queensland, Tasmania, and Australian Capital Territory and Victoria subgroups, respectively. The maximum deviation from baseline trends for LOS was 101.7% (SD 42.2%), 60.6% (SD 36.4%), and 158.3% (SD 68.1%). However, by synchronizing outcomes to the start date of intervention, the impact of seasonal variations was minimized to a maximum of 9.5% (SD 7.7%), thus improving the accuracy of the clinical outcomes reported. Conclusions: Seasonal variations have a significant effect on the rate of hospital admission and LOS in patients with chronic conditions. However, the impact of seasonal variation on clinical outcomes (rate of admissions, number of hospital admissions, and LOS) of at-home telemonitoring can be attenuated by synchronizing the analysis of outcomes to the commencement dates for the telemonitoring of vital signs. Trial Registration: Australian New Zealand Clinical Trial Registry ACTRN12613000635763; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=364030&isReview=true (Archived by WebCite at http://www.webcitation.org/ 6xLPv9QDb

Effects of Selective Deletion of Tyrosine Hydroxylase from Kisspeptin Cells on Puberty and Reproduction in Male and Female Mice.

The neuropeptide kisspeptin, encoded by Kiss1, regulates reproduction by stimulating GnRH secretion. Kiss1-syntheizing neurons reside primarily in the hypothalamic anteroventral periventricular (AVPV/PeN) and arcuate (ARC) nuclei. AVPV/PeN Kiss1 neurons are sexually dimorphic, with females expressing more Kiss1 than males, and participate in estradiol (E2)-induced positive feedback control of GnRH secretion. In mice, most AVPV/PeN Kiss1 cells coexpress tyrosine hydroxylase (TH), the rate-limiting enzyme in catecholamine synthesis (in this case, dopamine). Dopamine treatment can inhibit GnRH neurons, but the function of dopamine signaling arising specifically from AVPV/PeN Kiss1 cells is unknown. We generated a novel TH flox mouse and used Cre-Lox technology to selectively ablate TH specifically from Kiss1 cells. We then examined the effects of selective TH knock-out on puberty and reproduction in both sexes. In control mice, 90% of AVPV/PeN Kiss1 neurons coexpressed TH, whereas in mice lacking TH exclusively in Kiss1 cells (termed Kiss THKOs), TH was successfully absent from virtually all Kiss1 cells. Despite this absence of TH, both female and male Kiss THKOs displayed normal body weights, puberty onset, and basal gonadotropin levels in adulthood, although testosterone (T) was significantly elevated in adult male Kiss THKOs. The E2-induced LH surge was unaffected in Kiss THKO females, and neuronal activation status of kisspeptin and GnRH cells was also normal. Supporting this, fertility and fecundity were normal in Kiss THKOs of both sexes. Thus, despite high colocalization of TH and Kiss1 in the AVPV/PeN, dopamine produced in these cells is not required for puberty or reproduction, and its function remains unknown

A Jackknife and Voting Classifier Approach to Feature Selection and Classification

With technological advances now allowing measurement of thousands of genes, proteins and metabolites, researchers are using this information to develop diagnostic and prognostic tests and discern the biological pathways underlying diseases. Often, an investigator’s objective is to develop a classification rule to predict group membership of unknown samples based on a small set of features and that could ultimately be used in a clinical setting. While common classification methods such as random forest and support vector machines are effective at separating groups, they do not directly translate into a clinically-applicable classification rule based on a small number of features.We present a simple feature selection and classification method for biomarker detection that is intuitively understandable and can be directly extended for application to a clinical setting. We first use a jackknife procedure to identify important features and then, for classification, we use voting classifiers which are simple and easy to implement. We compared our method to random forest and support vector machines using three benchmark cancer ‘omics datasets with different characteristics. We found our jackknife procedure and voting classifier to perform comparably to these two methods in terms of accuracy. Further, the jackknife procedure yielded stable feature sets. Voting classifiers in combination with a robust feature selection method such as our jackknife procedure offer an effective, simple and intuitive approach to feature selection and classification with a clear extension to clinical applications

Notch signaling regulates perivascular adipose tissue (PVAT) function during diet-induced obesity

Objectives: -To test the hypothesis that canonical Notch signaling influence vascular function by regulating PVAT-derived vascular relaxation factors. -To determine how overexpression of Notch signaling or loss of the key Notch signaling component influence PVAT function.https://knowledgeconnection.mainehealth.org/lambrew-retreat-2021/1035/thumbnail.jp