Parallel Markov Chain Monte Carlo Methods for Large Scale Statistical Inverse Problems

The Bayesian method has proven to be a powerful way of modeling inverse problems. The solution to Bayesian inverse problems is the posterior distribution of estimated parameters which can provide not only estimates for the inferred parameters but also the uncertainty of these estimations. Markov chain Monte Carlo (MCMC) is a useful technique to sample the posterior distribution and information can be extracted from the sampled ensemble. However, MCMC is very expensive to compute, especially in inverse problems where the underlying forward problems involve solving differential equations. Even worse, MCMC is difficult to parallelize due to its sequential nature|that is, under the current framework, we can barely accelerate MCMC with parallel computing. We develop a new framework of parallel MCMC algorithms-the Markov chain preconditioned Monte Carlo (MCPMC) method-for sampling Bayesian inverse problems. With the help of a fast auxiliary MCMC chain running on computationally cheaper approximate models, which serves as a stochastic preconditioner to the target distribution, the sampler randomly selects candidates from the preconditioning chain for further processing on the accurate model. As this accurate model processing can be executed in parallel, the algorithm is suitable for parallel systems. We implement it using a modified master-slave architecture, analyze its potential to accelerate sampling and apply it to three examples. A two dimensional Gaussian mixture example shows that the new sampler can bring statistical efficiency in addition to increasing sampling speed. Through a 2D inverse problem with an elliptic equation as the forward model, we demonstrate the use of an enhanced error model to build the preconditioner. With a 3D optical tomography problem we use adaptive finite element methods to build the approximate model. In both examples, the MCPMC successfully samples the posterior distributions with multiple processors, demonstrating efficient speedups comparing to traditional MCMC algorithms. In addition, the 3D optical tomography example shows the feasibility of applying MCPMC towards real world, large scale, statistical inverse problems

Subcellular localization of APMCF1 and its biological significance of expression pattern in normal and malignant human tissues

<p>Abstract</p> <p>Background</p> <p>APMCF1 is a novel human gene first cloned from apoptotic MCF-7 cells. Our previous study found ectogenic APMCF1 could induce G1 arrest in hepatocarcinoma cell line HHCC. In order to search its broad expression profile for further understanding of its mechanism in tumor, we investigated a subcellular location of APMCF1 and performed an immunohistochemistry study including various tumor and normal tissues. Discovery from the expression characterization of AMPCF1 may have applicability in the analysis of its biological function in tumor.</p> <p>Methods</p> <p>We investigated subcellular localization of APMCF1 by transient transfection in green monkey kidney epithelial cells (COS-7) with a fusion protein vector pEGFP-APMCF1 and detected expression profile in a broad range of normal and malignant human tissues via tissue microarray (TMA) by immunohistochemistry with polyclonal antibody first produced in our laboratory.</p> <p>Results</p> <p>EGFP-APMCF1 was generally localized in the cytoplasm of COS-7 cell. Positive staining of APMCF1 was found in liver, lung, breast, colon, stomach, esophagus and testis, exhibited a ubiquitous expression pattern while its expression was up-regulated in tumor tissues compared with corresponding normal tissues. Normal brain neuron cells also showed expression of APMCF1, but negative in gliocyte cells and glioma. Both the normal and tumor tissues of ovary were absent of APMCF1 expression. Positive immunostaining for APMCF1 with large samples in liver, colon, esophagus, lung and breast carcinomas were 96% (51/53), 80% (44/55), 57% (30/53), 58% (33/57) and 34% (16/47) respectively.</p> <p>Conclusion</p> <p>These results revealed a cytoplastic expression pattern of APMCF1 and up-regulated in tumour tissues suggesting APMCF1 may have potential relationship with oncogenesis. The data presented should serve as a useful reference for further studies of APMCF1 functions in tumorigenesis and might provide a potential anti-tumor target.</p

A Reward-Based Framework of Perceived Control

Perceived control can be broadly defined as the belief in one’s ability to exert control over situations or events. It has long been known that perceived control is a major contributor toward mental and physical health as well as a strong predictor of achievements in life. However, one issue that limits a mechanistic understanding of perceived control is the heterogeneity of how the term is defined in models in psychology and neuroscience, and used in experimental settings across a wide spectrum of studies. Here, we propose a framework for studying perceived control by integrating the ideas from traditionally separate work on perceived control. Specifically, we discuss key properties of perceived control from a reward-based framework, including choice opportunity, instrumental contingency, and success/reward rate. We argue that these separate reward-related processes are integral to fostering an enhanced perception of control and influencing an individual’s behavior and well-being. We draw on select studies to elucidate how these reward-related elements are implicated separately and collectively in the investigation of perceived control. We highlight the role of dopamine within corticostriatal pathways shared by reward-related processes and perceived control. Finally, through the lens of this reward-based framework of perceived control, we consider the implications of perceived control in clinical deficits and how these insights could help us better understand psychopathology and treatment options

The effectiveness and safety of prophylactic central neck dissection in clinically node-negative papillary thyroid carcinoma patients: A meta-analysis

ObjectiveThis meta-analysis was performed to evaluate the effectiveness and safety of prophylactic central neck dissection (PCND) in patients with clinically node-negative (cN0) papillary thyroid carcinoma.Materials and methodsA meta-analysis of the literature was performed using the key words “papillary thyroid carcinomas” and “lymph node ecisions” for searches of electronic databases. Complications such as transient hypocalcemia, permanent hypocalcemia, transient and permanent hypoparathyroidism, transient and permanent vocal cord paralysis, transient recurrent and permanent recurrent laryngeal nerve injury, and local recurrence were pooled by meta-analysis. Stata17.0 was used to carry out the meta-analysis.ResultsData were extracted from 15 studies. In the present review, the group of patients who had total thyroidectomy (TT) with PCND had a lower local recurrence than the group with TT alone (OR 0.22, 95% CI 0.10-0.45, P = 0.000), whereas the incidence of permanent hypocalcemia (OR 4.24, 95% CI 1.05-17.22, P = 0.043) and transient hypoparathyroidism (OR 2.14, 95% CI 1.34-3.42, P =0.001) were higher. No significant differences were recorded in the incidence of other complications: transient hypocalcemia (OR 2.24, 95% CI 0.77-6.51, P = 0.138), permanent hypoparathyroidism (OR 1.70, 95% CI 0.89-3.27, P = 0.111), transient vocal cord paralysis (OR 1.48, 95% CI 0.78-2.83, P = 0.231), permanent vocal cord paralysis (OR 1.44, 95% CI 0.53-3.94, P = 0.477), transient recurrent laryngeal nerve injury (OR 1.47, 95% CI 0.93-2.32, P = 0.102) and permanent recurrent laryngeal nerve injury (OR 1.24, 95% CI 0.56-2.74, P = 0.587) between the two groups.ConclusionCompared with TT alone, TT with PCND was more effective in reducing local recurrence without increasing the risk of recurrent laryngeal nerve, thyroid and vocal cord, except for hypocalcemia and transient hypoparathyroidism. Therefore, we believe that TT with PCND should be recommended for patients with cN0 PTC.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD4202 2355078