6,518 research outputs found
Comparative Effects of DL-Thyronine, L-Triiodothyronine and L-Thyroxine on the Isolated Perfused Frog Heart
No full text Page 63: Morris Kleinfeld, Albert Rosenthal and Edward Stein, "Comparative effects of dl-thyronine, l-triiodothyronine and l-thyroxine on the isolated perfused frog heart." The name of the second author should read Alvin S. Rosenthal. </jats:p
Reconciling Well-Founded Semantics of DL-Programs and Aggregate Programs
No full textLogic programs with aggregates and description logic programs (dl-programs) are two recent extensions to logic programming. In this paper, we study the relationships between these two classes of logic programs, under the well-founded semantics. The main result is that, under a satisfaction-preserving mapping from dl-atoms to aggregates, the well-founded semantics of dl-programs by Eiter et al., coincides with the well-founded semantics of aggregate programs, defined by Pelov et al. as the least fixpoint of a 3-valued immediate consequence operator under the ultimate approximating aggregate. This result enables an alternative definition of the same well-founded semantics for aggregate programs, in terms of the first principle of unfounded sets. Furthermore, the result can be applied, in a uniform manner, to define the well-founded semantics for dl-programs with aggregates, which agrees with the existing semantics when either dl-atoms or aggregates are absent
Utilising Deep Learning Models for the Surface Registration Problem in HoloNav
No full textSurface Registration is a registration problem that handles the registration of two similar surfaces. In most research that utilises Deep Learning (DL) models to handle surface registration two theories are investigated; the first being whether surfaces sampled from the same origin can be registered together, and the second theory being whether the models can register Point Clouds with low overlapping data for utilisation in Simultaneous Localisation and Mapping (SLAM) applications. However, the surface registration to be utilised in the HoloNav Augmented Reality (AR) navigation system will utilise Point Clouds sampled from different origins with a high overlap ratio. This research, therefore, aims to determine the viability of DL methods for surface registration in HoloNav data. To determine the viability, rotation and translation errors in the match were used, with the aforementioned metrics later being evaluated manually with the utilisation of a visualiser. The results indicate that the models can generalise on the navigator data for an initial Euler angle difference of 45 degrees, but due to the difference in sampling density on the utilised point clouds can not provide accurate matches. Therefore, the utilisation of DL models can be considered to be viable if the navigator data has a sampling density similar to the pre-operative model.https://github.com/alpcicimen/holonav-dl-registration The link to the github repository containing the utilised dataset, scripts, as well as the modified DL models RPMNet and PREDATOR.CSE3000 Research ProjectComputer Science and Engineerin
The Scent of a Smell: An Extensive Comparison between Textual and Structural Smells
No full textCode smells are symptoms of poor design or implementation choices that have a negative effect on several aspects of software maintenance and evolution, such as program comprehension or change- and fault-proneness. This is why researchers have spent a lot of effort on devising methods that help developers to automatically detect them in source code. Almost all the techniques presented in literature are based on the analysis of structural properties extracted from source code, although alternative sources of information (e.g., textual analysis) for code smell detection have also been recently investigated. Nevertheless, some studies have indicated that code smells detected by existing tools based on the analysis of structural properties are generally ignored (and thus not refactored) by the developers. In this paper, we aim at understanding whether code smells detected using textual analysis are perceived and refactored by developers in the same or different way than code smells detected through structural analysis. To this aim, we set up two different experiments. We have first carried out a software repository mining study to analyze how developers act on textually or structurally detected code smells. Subsequently, we have conducted a user study with industrial developers and quality experts in order to qualitatively analyze how they perceive code smells identified using the two different sources of information. Results indicate that textually detected code smells are easier to identify and for this reason they are considered easier to refactor with respect to code smells detected using structural properties. On the other hand, the latter are often perceived as more severe, but more difficult to exactly identify and remove.Accepted Author ManuscriptSoftware Engineerin
Weak if any effect of estrogen on spatial memory in rats
No full textIn a number of species, males appear to have spatial abilities that are superior to those of females. The favored explanation for this cognitive difference is hormonal: higher testosterone levels in males than in females. An alternative explanation focuses on the role of varying levels of estrogens in females during the estrus cycle; females perform as well as males on days of low estrogen, but more poorly on days of high estrogen. Other investigators have reported that estrogens improve both types of memory processes, which depend on the striatal (nonspatial navigation) and hippocampal (spatial) memory systems. Additionally, estrogens have been found to protect the working memory. These contradictory results initiated the present study, in which ovariectomized female rats were trained to escape in a Morris water maze. The daily trials were preceded by estradiol application in low doses
(Experiment I) or in higher doses (Experiment II). In Experiment I, no differences at all were found between the latencies of the treated and control groups to reach a submerged platform in a Morris water maze. In Experiment II, however, the animals treated with the higher dose of estradiol showed a small deficit in the acquisition of the Morris water maze task. This study indicates that estradiol at around the physiological level has no effect on spatial learning and memory functions
1918: Golda Meir with husband Morris Meyerson
No full textGolda Meir with husband Morris Meyerson.Grayscal
Protocol for the saMS trial (supportive adjustment for multiple sclerosis): a randomized controlled trial comparing cognitive behavioral therapy to supportive listening for adjustment to multiple sclerosis
No full textBackgroundMultiple Sclerosis (MS) is an incurable, chronic, potentially progressive and unpredictable disease of the central nervous system. The disease produces a range of unpleasant and debilitating symptoms, which can have a profound impact including disrupting activities of daily living, employment, income, relationships, social and leisure activities, and life goals. Adjusting to the illness is therefore particularly challenging. This trial tests the effectiveness of a cognitive behavioural intervention compared to supportive listening to assist adjustment in the early stages of MS.MethodsThis is a two arm randomized multi-centre parallel group controlled trial. 122 consenting participants who meet eligibility criteria will be randomly allocated to receive either Cognitive Behavioral Therapy or Supportive Listening. Eight one hour sessions of therapy (delivered over a period of 10 weeks) will be delivered by general nurses trained in both treatments. Self-report questionnaire data will be collected at baseline (0 weeks), mid-therapy (week 5 of therapy), post-therapy (15 weeks) and at six months (26 weeks) and twelve months (52 weeks) follow-up. Primary outcomes are distress and MS-related social and role impairment at twelve month follow-up. Analysis will also consider predictors and mechanisms of change during therapy. In-depth interviews to examine participants’ experiences of the interventions will be conducted with a purposively sampled sub-set of the trial participants. An economic analysis will also take place. DiscussionThis trial is distinctive in its aims in that it aids adjustment to MS in a broad sense. It is not a treatment specifically for depression. Use of nurses as therapists makes the interventions potentially viable in terms of being rolled out in the NHS. The trial benefits from incorporating patient input in the development and evaluation stages. The trial will provide important information about the efficacy, cost-effectiveness and acceptability of the interventions as well as mechanisms of psychosocial adjustment.Trial registrationCurrent Controlled Trials ISRCTN91377356<br/
A commentary on concurrent MCL1 and JUN amplification in pseudomyxoma peritonei: a comprehensive genetic profiling and survival analysis
No full textMucinous appendiceal tumors comprise of 1% of all colorectal cancer accounting for about 1500 cases per year in the United States.1 These tumors originate in the appendix but often as a result of its growth within a narrow appendiceal lumen, the tumor perforates or may result in full thickness growth and invasion through the appendix lumen to involve the serosa. Transcoelomic seeding of tumor on the peritoneal surfaces result in the clinical syndrome of pseudomyxoma peritonei. Though this cancer is uncommon, there has been an enormous development in our understanding of the disease biology on the basis of its natural history in the last three decades. Dysplasia occurring in the mucus-secreting epithelium was initially classified histologically by Ronnett et al.2 into three diagnostic categories comprising of disseminated peritoneal adenomucinosis, peritoneal mucinous carcinomatosis and an intermediate grade. This was based on the amount of cellularity, proliferative activity and presence of cytologic features of carcinoma. Today, the Bradley criteria is more commonly used and it dichotomizes the classification into a low- and high-grade group.3 Surgical cytoreduction in combination with hyperthermic intraperitoneal chemotherapy has been demonstrated to be the standard of care achieving long-term survival gains over limited surgical debulking.1 In a recent worldwide collaborative registry study, the 10-year survival of patients wherein a complete macroscopic surgical cytoreduction was not attempted or not possibly achieved operatively was 70% in patients who had a complete macroscopic cytoreduction.4 Inability to achieve a complete cytoreduction may often be considered a surrogate reflection of an aggressive tumor that is more cellular and less mucinous resulting in more extensive invasion of the peritoneal surfaces. Patients with higher volume disease involving a larger extent of the peritoneal surfaces are also at higher risk of surgical morbidity. Further, recent data suggests a role for modern systemic chemotherapy in high-grade appendiceal tumors with radiographic responses demonstrated in 44% of patients.5 If we were able to delineate molecular signatures and identify which tumors bear unfavorable tumor biology in addition to the current prognostic role of the histological classification, this may assist in treatment risk stratification. It will allow identification of suitable patients for surgical cytoreduction and/or for systemic chemotherapy. This would reduce surgical morbidity in patients who would otherwise not benefit from surgery.No Full Tex
A deep learning approach to calculate elementary effects of morris sensitivity analysis
No full textThe shape optimization of the multicomponent hydraulic turbomachinery is complex and computationally expensive due to the high number of computational fluid dynamics simulations. It is essential to identify the most influential parameters for which sensitivity analysis is needed to reduce the number of simulations. Morris sensitivity analysis provides a cost‐effective alternative for global sensitivity analysis that screens the essential parameters, requiring only a few computations to identify the most influential parameters from many parameters. This method is based on the elementary effects (EEs), which calculates the derivatives using the finite difference method. A deep learning (DL) approach is proposed to estimate the Morris method's EE. Two DL methods are proposed: the first utilizes the backpropagation of deep neural networks to calculate the partial derivatives of outputs to inputs; the second method relies on an artificial neural network‐based surrogate model which is trained using the optimization run dataset of hydraulic machinery with 30 parameters. The experimental results showed that the surrogate model trained with at least 7000 samples computes similar EEs as the classical Morris method with 310 samples. However, the backpropagation approach on Morris samples was observed to be less effective compared to a surrogate modeling approach.Deutsche Forschungsgemeinschaf
March dl: Adding Adaptive Heuristics and a New Branching Strategy
No full textWe introduce the march dl satisability (SAT) solver, a successor of march eq. The latter was awarded state-of-the-art in two categories during the Sat 2004 competition. The focus lies on presenting those features that are new in march dl. Besides a description, each of these features is illustrated with some experimental results. By extending the pre-processor, using adaptive heuristics, and by using a new branching strategy, march dl is able to solve nearly all benchmarks faster than its predecessor. Moreover, various instances which were beyond the reach of march eq, can now be solved - relatively easily - due to these new features.Software TechnologyElectrical Engineering, Mathematics and Computer Scienc
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