This paper revisits Myerson and Satterthwaite's (1983) classic analysis of mechanism design for bilateral trading, replacing equilibrium with a level-k model of strategic thinking and focusing on ...direct mechanisms. The revelation principle fails for level-k models, so restricting attention to direct mechanisms and imposing incentive-compatibility are not without loss of generality. If, however, only direct, level-k-incentive-compatible mechanisms are feasible and traders' levels are observable, Myerson and Satterthwaite's characterization of mechanisms that maximize traders' total surplus subject to incentive constraints generalizes qualitatively to level-k models. If only direct, level-k-incentive-compatible mechanisms are feasible but traders' levels are not observable, generically a particular posted-price mechanism maximizes traders' total expected surplus subject to incentive constraints. If direct, non-level-k-incentive-compatible mechanisms are feasible and traders best respond to them, total expected surplus-maximizing mechanisms may take completely different forms.
The aim of the study was to identify the survival of patients with locally advanced pancreatic cancer (LAPC) and assess the effect of surgical resection after neoadjuvant therapy on patient outcomes.
...An increasing number of LAPC patients who respond favorably to neoadjuvant therapy undergo surgical resection. The impact of surgery on patient survival is largely unknown.
All LAPC patients who presented to the institutional pancreatic multidisciplinary clinic (PMDC) from January 2013 to September 2017 were included in the study. Demographics and clinical data on neoadjuvant treatment and surgical resection were documented. Primary tumor resection rates after neoadjuvant therapy and overall survival (OS) were the primary study endpoints.
A total of 415 LAPC patients were included in the study. Stratification of neoadjuvant therapy in FOLFIRINOX-based, gemcitabine-based, and combination of the two, and subsequent outcome comparison did not demonstrate significant differences in OS of 331 non-resected LAPC patients (P = 0.134). Eighty-four patients underwent resection of the primary tumor (20%), after a median duration of 5 months of neoadjuvant therapy. FOLFIRINOX-based therapy and stereotactic body radiation therapy correlated with increased probability of resection (P = 0.006). Resected patients had better performance status, smaller median tumor size (P = 0.029), and lower median CA19-9 values (P < 0.001) at PMDC. Patients who underwent surgical resection had significant higher median OS compared with those who did not (35.3 vs 16.3 mo, P < 0.001). The difference remained significant when non-resected patients were matched for time of neoadjuvant therapy (19.9 mo, P < 0.001).
Surgical resection of LAPC after neoadjuvant therapy is feasible in a highly selected cohort of patients (20%) and is associated with significantly longer median overall survival.
Open radical cystectomy (ORC) has proven to be an important component in the treatment of high-risk bladder cancer (BCa). ORC surgical morbidity remains high; therefore, minimally invasive surgical ...techniques have been introduced in an attempt to improve patient outcomes.
To compare cancer outcomes in BCa patients managed with ORC or robotic-assisted radical cystectomy (RARC).
A prospective, randomized trial was completed between 2010 and 2013. Patients were randomized to ORC/pelvic lymphadenectomy (PLND) or RARC/PLND, with all undergoing open/extracorporeal urinary diversion. Median follow-up was 4.9 (IQR: 3.9–5.9) yr after surgery among surviving patients.
Secondary outcomes to the trial included recurrence-free, cancer-specific, and overall survival.
The trial randomized 118 patients who underwent RC/PLND and urinary diversion. Sixty were randomized to RARC and 58 to ORC. Four RARC-assigned patients refused randomization and received ORC; however, an intention to treat analysis was performed. No differences were observed in recurrence (hazard ratio HR: 1.27; 95% confidence interval CI: 0.69–2.36; p=0.4) or cancer-specific survival (p=0.4). No difference in overall survival was observed (p=0.8). However, the pattern of first recurrence demonstrated a nonstatistically significant increase in metastatic sites for those undergoing ORC (sub-HR sHR: 2.21; 95% CI: 0.96–5.12; p=0.064) and a greater number of local/abdominal sites in the RARC-treated patients (sHR: 0.34; 95% CI: 0.12–0.93; p=0.035). The major limitation to this study is that the trial was not powered to determine differences in cancer recurrences, survival outcomes, or patterns of recurrence.
The secondary outcomes from our randomized trial did not definitively demonstrate differences in cancer outcomes in patients treated with ORC or RARC. However, differences in observed patterns of first recurrence highlight the need for future studies.
Of 118 patients randomly assigned to undergo radical cystectomy/pelvic lymphadenectomy and urinary diversion, half were assigned to open surgery and half to robot-assisted techniques. We found no difference in risk of recurring or dying of bladder cancer between the two groups.
In this secondary analysis of cancer outcomes from our randomized controlled trial, we did not find a difference in overall recurrence rates and cancer-specific survival between open radical cystectomy and robot-assisted radical cystectomy for high-risk bladder cancer. Variations in patterns of recurrence require further study.
This paper proposes a structural nonequilibrium model of initial responses to incomplete-information games based on "level-k" thinking, which describes behavior in many experiments with ...complete-information games. We derive the model's implications in first- and second-price auctions with general information structures, compare them to equilibrium and Eyster and Rabin's (2005) "cursed equilibrium," and evaluate the model's potential to explain nonequilibrium bidding in auction experiments. The level-k model generalizes many insights from equilibrium auction theory. It allows a unified explanation of the winner's curse in common-value auctions and overbidding in those independent-private-value auctions without the uniform value distributions used in most experiments.
Humans have long used cognitive enhancement methods to expand the proficiency and range of the various mental activities that they engage in, including writing to store and retrieve information, and ...computers that allow them to perform myriad activities that are now commonplace in the internet age. Neuroenhancement describes the use of neuroscience-based techniques for enhancing cognitive function by acting directly on the human brain and nervous system, altering its properties to increase performance. Cognitive neuroscience has now reached the point where it may begin to put theory derived from years of experimentation into practice. This special issue includes 16 articles that employ or examine a variety of neuroenhancement methods currently being developed to increase cognition in healthy people and in patients with neurological or psychiatric illness. This includes transcranial electromagnetic stimulation methods, such as transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS), along with deep brain stimulation, neurofeedback, behavioral training techniques, and these and other techniques in conjunction with neuroimaging. These methods can be used to improve attention, perception, memory and other forms of cognition in healthy individuals, leading to better performance in many aspects of everyday life. They may also reduce the cost, duration and overall impact of brain and mental illness in patients with neurological and psychiatric illness. Potential disadvantages of these techniques are also discussed. Given that the benefits of neuroenhancement outweigh the potential costs, these methods could potentially reduce suffering and improve quality of life for everyone, while further increasing our knowledge about the mechanisms of human cognition.
Thoroughbred horses are finely-tuned athletes with a high aerobic capacity relative to skeletal muscle mass, attributable to centuries of genetic selection for speed and stamina. Polymorphisms in the ...myostatin gene (MSTN), a pronounced inhibitor of skeletal muscle growth, have been shown to almost singularly account for gene-based race distance aptitude in racehorses. In Thoroughbreds, two MSTN polymorphisms, a single nucleotide variation in the first intron (SNP g.66493737C>T) and a non-coding transposable element within the promoter region (a 227 bp SINE insertion) are of particular interest. Until now, it has not been clear which of these variants affect skeletal muscle phenotypes or whether both can impact racing performance. In a large cohort of Thoroughbreds, we observed a complete concordance between the SNP and the SINE insertion. By means of in vitro assays in C2C12 myoblasts, we isolated the SNP variant from the SINE polymorphism and showed the latter is exclusively responsible for adversely affecting transcription initiation and gene expression thereby limiting myostatin protein production. Mapping the MSTN transcription start site in horse skeletal muscle likewise revealed anomalous transcription initiation in the presence of the SINE insertion. Our data provides mechanistic evidence that the SINE insertion uniquely accounts for the MSTN "speed gene" effect on race distance aptitude in the Thoroughbred horse.
This review provides an extensive summary of the effects of carbohydrate fluorination with regard to changes in physical, chemical and biological properties with respect to regular saccharides. The ...specific structural, conformational, stability, reactivity and interaction features of fluorinated sugars are described, as well as their applications as probes and in chemical biology.
Deep machine learning is used to analyze a proton radiograph from a tin pulsed power experiment and determine density values for each pixel in the image. Two promising convolutional neural network ...architectures that have proven to be effective for image analysis in other applications are applied to analyze a proton radiograph and find density values. The process of creating a suitable training dataset is described, involving the Lagrangian hydrodynamic model used for simulations of the experiment, the proton radiography forward model to make synthetic images for training, and the manner in which data augmentation is used to expand the resulting image dataset. It is shown that machine learning not only produces a reasonable density field but is also able to predict features in the density field that are suggested by the proton radiograph but not captured by simulations.
Since the outbreak of COVID-19 in December 2019, no global consensus treatment has been developed and generally accepted for the disease. However, eradicating the disease will require a safe and ...efficacious vaccine. In order to prepare for the eventual development of a safe and efficacious COVID-19 vaccine and to enhance its uptake, it is imperative to assess vaccine hesitancy in Cameroonians. After obtaining ethical clearance from the Institutional Review Board of the University of Buea, a questionnaire was administered (May-August 2020) to consenting adults either online or in person. A qualitative thematic analysis was done to analyze the participants' answers to the open questions. A deductive approach was used, that is, the codes and patterns according to the World Health Organization (WHO) Strategic Advisory Group of Experts (SAGE) Working Group Matrix of Determinants of vaccine hesitancy. The number of consenting adult Cameroonians who completed the questionnaire were 2512 (Two thousand five hundred and twelve). Vaccine hesitancy to a COVID-19 vaccine was 84.6% in Cameroonians. Using the WHO recommended Matrix of Determinant of Vaccine hesitancy, the most prominent determinants observed in this study were: Communication and Media Environment, Perception of pharmaceutical industry, Reliability and/or source of vaccine and cost. Most Cameroonians agree that even though there are benefits of a clinical trial, they will prefer it should be done out of the continent and involving African scientists for eventual acceptance and uptake. The concerns of safety, efficacy and confidence has to be addressed using a Public Engagement approach if a COVID-19 vaccine has to be administered successfully in Africa or Cameroon specifically. Since this study was carried out following WHO standards, its result can be compared to those of other studies carried out in different cultural settings using similar standards.
Ageing is associated with various ailments including Alzheimer ’s disease (AD), which is a progressive form of dementia. AD symptoms develop over a period of years and, unfortunately, there is no ...cure. Existing AD treatments can only slow down the progression of symptoms and thus it is critical to diagnose the disease at an early stage. To help improve the early diagnosis of AD, a deep learning-based classification model with an embedded feature selection approach was used to classify AD patients. An AD DNA methylation data set (64 records with 34 cases and 34 controls) from the GEO omnibus database was used for the analysis. Before selecting the relevant features, the data were preprocessed by performing quality control, normalization and downstream analysis. As the number of associated CpG sites was huge, four embedded-based feature selection models were compared and the best method was used for the proposed classification model. An Enhanced Deep Recurrent Neural Network (EDRNN) was implemented and compared to other existing classification models, including a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), and a Deep Recurrent Neural Network (DRNN). The results showed a significant improvement in the classification accuracy of the proposed model as compared to the other methods.
•DRNN based model for detecting Alzheimer’s disease by selecting the gene subset from DNA Methylation dataset.•The dataset is preprocessed through quality control, normalization and downstream analysis.•Implemented two tree based and regression based embedded feature selection to find the best feature set.•We present the DRNN with an improvement for fast convergence with Bayesian Optimization to tune hyperparameters.
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